code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
... | 32 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generatio... | 682 | 0 |
import re
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
i... | 183 |
"""simple docstring"""
import os
def UpperCAmelCase__ ():
'''simple docstring'''
with open(os.path.dirname(lowerCAmelCase_ ) + "/p022_names.txt" ) as file:
__SCREAMING_SNAKE_CASE = str(file.readlines()[0] )
__SCREAMING_SNAKE_CASE = names.replace... | 682 | 0 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoToke... | 78 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
... | 682 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def Upp... | 455 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.mo... | 682 | 0 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowerCAmelCase: Union[str, Any] = logging.get_lo... | 20 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_avai... | 682 | 0 |
from ... import PretrainedConfig
lowercase_ = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A = NEZHA_PRETRAINED_CONFIG_ARCH... | 562 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_... | 682 | 0 |
from ...configuration_utils import PretrainedConfig
UpperCamelCase = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/googl... | 45 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
a__ : int = '''us-east-1''' # defaults region
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
snake_case__ : str
snake_case__ : Optional[Any] = "arn:a... | 682 | 0 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fast... | 624 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
a__ : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
def __init__( self : Any , UpperCAmelCa... | 682 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 636 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__SCREAMING_SNAKE_CASE = len(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = max(lowerC... | 682 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLM... | 28 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ : Tuple = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_... | 682 | 0 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvi... | 46 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : List[str] = logging.get_logger(__name__)
a__ : str = {
'''xlm-... | 682 | 0 |
from __future__ import annotations
import math
def A__ ( SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negative... | 32 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 682 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils i... | 183 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 682 | 0 |
'''simple docstring'''
import requests
SCREAMING_SNAKE_CASE_: List[str] ='''''' # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE_: str ='''https://api.openweathermap.org/data/2.5/'''
def lowerCAmelCase_ ( snake_case_ : Optional[int] = "Chicago" , snake_case_ ... | 78 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 682 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCamelCase__ = collections.namedtuple("""_Datasets... | 455 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if edge <= 0 or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
... | 682 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require... | 20 |
"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = r'''
... | 682 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self : int , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Opt... | 562 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.te... | 682 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def A ( lowercase__ : int , lowercase__ : Tuple , lowercase__ : Optional[int] , lowercase__ : Any , lowercase__ : Union[str, Any] , lowercase__ : int ) -> str:
if (ksize % 2)... | 45 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivi... | 682 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
fro... | 624 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = np.max(lowerCAmelCase_ , axis=-1 , keepdims=lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = ... | 682 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTest... | 636 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_C... | 682 | 0 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available... | 683 |
'''simple docstring'''
_UpperCAmelCase : str = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : List[str] = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5:... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
if height >= 1:
move_tower(height - 1 ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
move_disk(UpperCamelCase ,UpperCa... | 683 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
s... | 683 | 1 |
'''simple docstring'''
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case ) -> None:
_UpperCamelCase : Optional[int] = set_counts
_UpperCamelCase : Optional[int] = max(_snake_case )
_UpperCamelCase : ... | 683 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 683 | 1 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.para... | 683 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 683 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
r... | 683 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
_UpperCAmelCase : int = 100
_UpperCAmelCase : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_UpperCAmelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime no... | 683 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase ( a_ ):
"""simple docstring"""
def __init__( self ... | 683 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformer... | 683 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
"""kssteven/iber... | 683 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case = None ) -> Optional[int]:
_UpperCamelCase : int = value
_UpperCamelCase : Node | ... | 683 | 1 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : Tuple = 'EncodecFeatureExtractor'
A__ : str = ('T5Tokeni... | 683 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils i... | 683 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""... | 683 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase : Tuple = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transf... | 683 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResam... | 683 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 683 | 1 |
'''simple docstring'''
import numpy as np
from PIL import Image
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> np.ndarray:
_UpperCamelCase : Any = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The ... | 683 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.para... | 683 | 1 |
'''simple docstring'''
from math import sqrt
def snake_case__ ( UpperCamelCase ) -> int:
_UpperCamelCase : List[str] = 0
for i in range(1 ,int(sqrt(UpperCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(UpperCamelCase ):
total += i + n // ... | 683 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
fr... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ) -> bool:
_UpperCamelCase : Optional[Any] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case__ ( UpperCamelCase = 50_00 ) -> int:
_UpperCamelCase : Dict = [(i *... | 683 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""... | 683 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tok... | 683 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_UpperCAmelCase : Optional[int] = logging.get_... | 683 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils impo... | 683 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ) -> list:
_UpperCamelCase : Any = False
while is_sorted is False: # Until all the indices are traversed keep looping
_UpperCamelCase : List[str] = True
for i in range(0 ,len(UpperCamelCase ... | 683 | 1 |
'''simple docstring'''
from collections import defaultdict
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> bool:
_UpperCamelCase : str = first_str.lower().strip()
_UpperCamelCase : Dict = second_str.lower().strip()
# Remove whitespace
_UpperC... | 683 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_att... | 683 | 1 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case = None ) -> Optional[int]:
_UpperCamelCase : int = value
_UpperCamelCase : Node | ... | 683 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : str = ['image_processor', 'tokenizer']
A__ : Dict = 'CLIPImageProcessor... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> Tuple:
_UpperCamelCase : Optional[Any] = ''''''
for i in table:
res += inp[i - 1]
return res
def snake_case__ ( UpperCamelCase ) -> List[Any]:
return data[1:] + d... | 683 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase : Union[str, Any] = (720, 1280) # Height, Width
_UpperCAmelCase : str = (0.4, 0.6) # if height or width lower than this scale, drop it.
... | 683 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 683 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCA... | 683 | 1 |
'''simple docstring'''
from collections import defaultdict
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case , _snake_case ) -> Optional[Any]:
_UpperCamelCase : Optional[Any] = total # total no of tasks (N)
# DP tab... | 683 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 683 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 683 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transforme... | 683 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_atte... | 683 |
'''simple docstring'''
_UpperCAmelCase : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def snake_case__ ( UpperCamelCase ) -> int:
_UpperCamelCase : Any = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.... | 683 | 1 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCA... | 683 |
'''simple docstring'''
_UpperCAmelCase : str = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : List[str] = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5:... | 683 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 683 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
s... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex i... | 683 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 683 | 1 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def snake_case__ ( ) -> Any:
_UpperCamelCase : Optional[int] = 9
_UpperCamelCase : str = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2... | 683 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 683 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' ... | 683 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
_UpperCAmelCase : int = 100
_UpperCAmelCase : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_UpperCAmelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime no... | 683 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 683 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformer... | 683 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_pa... | 683 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case = None ) -> Optional[int]:
_UpperCamelCase : int = value
_UpperCamelCase : Node | ... | 683 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenizati... | 683 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils i... | 683 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_UpperCAmelCase : List[str] = logging.get_logg... | 683 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase : Tuple = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transf... | 683 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils i... | 683 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 683 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokeniza... | 683 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.para... | 683 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : str = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_available():
r... | 683 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
fr... | 683 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
... | 683 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""... | 683 | 1 |
'''simple docstring'''
from copy import deepcopy
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case = None , _snake_case = None ) -> None:
if arr is None and size is not None:
_UpperCamelCase : Optional[int] = size
... | 683 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_UpperCAmelCase : Optional[int] = logging.get_... | 683 | 1 |
'''simple docstring'''
from math import pow, sqrt
def snake_case__ ( *UpperCamelCase ) -> bool:
_UpperCamelCase : Optional[Any] = len(UpperCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case__ ( UpperCamelCase ... | 683 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ) -> list:
_UpperCamelCase : Any = False
while is_sorted is False: # Until all the indices are traversed keep looping
_UpperCamelCase : List[str] = True
for i in range(0 ,len(UpperCamelCase ... | 683 | 1 |
'''simple docstring'''
import numpy as np
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 1e-12 ,UpperCamelCase = 1_00 ,) -> tuple[float, np.ndarray]:
assert np.shape(UpperCamelCase )[0] == np.shape(UpperCamelCase )[1]
# Ensure proper dimensi... | 683 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_att... | 683 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( UpperCamelCase ,UpperCamelCase = False ) -> float:
if not arr:
return 0
_UpperCamelCase : Optional[int] = 0 if allow_empty_subarrays else float('''-inf''' )
_UpperCamelCase ... | 683 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : str = ['image_processor', 'tokenizer']
A__ : Dict = 'CLIPImageProcessor... | 683 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( a_ ):
... | 683 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase : Union[str, Any] = (720, 1280) # Height, Width
_UpperCAmelCase : str = (0.4, 0.6) # if height or width lower than this scale, drop it.
... | 683 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : ... | 683 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCA... | 683 | 1 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
_UpperCAmelCase : int = 100
_UpperCAmelCase : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_UpperCAmelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime no... | 683 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 683 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> tuple:
_UpperCamelCase : Tuple = namedtuple('''result''' ,'''name value''' )
if (voltage... | 683 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transforme... | 683 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcess... | 683 |
'''simple docstring'''
_UpperCAmelCase : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def snake_case__ ( UpperCamelCase ) -> int:
_UpperCamelCase : Any = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.... | 683 | 1 |
'''simple docstring'''
from manim import *
class UpperCAmelCase ( a_ ):
"""simple docstring"""
def _lowercase ( self ) -> str:
_UpperCamelCase : str = Rectangle(height=0.5 , width=0.5 )
_UpperCamelCase : int = Rectangle(height=0.... | 683 |
'''simple docstring'''
_UpperCAmelCase : str = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : List[str] = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5:... | 683 | 1 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
_UpperCAmelCase : Dict = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two ... | 683 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
s... | 683 | 1 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_UpperCAmelCase : Tuple = 2048
_UpperCAmelCase : int = 4096
_UpperCAmelCase : Dict = 42
_UpperCAmelCase : str = os.environ.pop("""PROCESS_TRAIN""", """false""")
_UpperCAmelCase : List[str] ... | 683 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 683 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""Yitu... | 683 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 683 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> int:
if len(UpperCamelCase ) < k or k < 0:
raise ValueError('''Invalid Input''' )
_UpperCamelCase : Tuple = sum(array[:k] )
for i in r... | 683 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
_UpperCAmelCase : int = 100
_UpperCAmelCase : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_UpperCAmelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime no... | 683 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_UpperCAmelCase : Optional[int] = logging.get_... | 683 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformer... | 683 | 1 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class UpperCAmelCase ( a_ , a_ )... | 683 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case = None ) -> Optional[int]:
_UpperCamelCase : int = value
_UpperCamelCase : Node | ... | 683 | 1 |
'''simple docstring'''
import math
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> float:
return math.pow(UpperCamelCase ,2 ) - a
def snake_case__ ( UpperCamelCase ) -> float:
return 2 * x
def snake_case__ ( UpperCamelCa... | 683 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils i... | 683 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase ( a_ ):
"""simple docstring"""
def _lowercase ( self , _snake_case ) -> float:
ret... | 683 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase : Tuple = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transf... | 683 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a_ )
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : str = field(default='summa... | 683 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 683 | 1 |
'''simple docstring'''
_UpperCAmelCase : Dict = {
0: """0""",
1: """1""",
2: """2""",
3: """3""",
4: """4""",
5: """5""",
6: """6""",
7: """7""",
8: """8""",
9: """9""",
10: """a""",
11: """b""",
12: """c""",
13: """d""",
14: """e""",
15: """f"""... | 683 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.para... | 683 | 1 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstri... | 683 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
fr... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ) -> int:
_UpperCamelCase : int = len(UpperCamelCase )
_UpperCamelCase : Optional[Any] = len(matrix[0] )
_UpperCamelCase : str = min(UpperCamelCase ,UpperCamelCase )
for ... | 683 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""... | 683 | 1 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_UpperCAmelCase : Optional[Any] = """."""
# ... | 683 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_UpperCAmelCase : Optional[int] = logging.get_... | 683 | 1 |
'''simple docstring'''
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models... | 683 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ) -> list:
_UpperCamelCase : Any = False
while is_sorted is False: # Until all the indices are traversed keep looping
_UpperCamelCase : List[str] = True
for i in range(0 ,len(UpperCamelCase ... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> List[Any]:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_UpperCamelCase : Optional[Any] = (boundary[1] - boundary[0]) / steps
_UpperCamelCase : Optional[int] ... | 683 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_att... | 683 | 1 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ) -> int:
_UpperCamelCase : Tuple = AutoConfig.from_pretrained(UpperCamelCase ,**U... | 683 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : str = ['image_processor', 'tokenizer']
A__ : Dict = 'CLIPImageProcessor... | 683 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeniza... | 683 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase : Union[str, Any] = (720, 1280) # Height, Width
_UpperCAmelCase : str = (0.4, 0.6) # if height or width lower than this scale, drop it.
... | 683 | 1 |
'''simple docstring'''
import functools
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> int:
_UpperCamelCase : List[str] = len(UpperCamelCase )
_UpperCamelCase : str = len(UpperCamelCase )
@functools.cache
def min_distance(UpperCame... | 683 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCA... | 683 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatu... | 683 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 683 | 1 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> Generator[tuple[str, ...], None, None]:
_UpperCamelCase : int = iter(UpperCamelCase )
while True:
_Upp... | 683 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transforme... | 683 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...... | 683 |
'''simple docstring'''
_UpperCAmelCase : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def snake_case__ ( UpperCamelCase ) -> int:
_UpperCamelCase : Any = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.... | 683 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 683 |
'''simple docstring'''
_UpperCAmelCase : str = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : List[str] = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5:... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
s... | 683 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : Dict = (IPNDMScheduler,)
A__ : List[str] = (('num_inference_steps', 50... | 683 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 683 | 1 |
'''simple docstring'''
def snake_case__ ( UpperCamelCase ) -> int:
_UpperCamelCase : int = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def snake_case__ ( UpperCamelCase = 1_00 ) -> int:
_UpperCamelCase : Any ... | 683 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 683 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeniza... | 683 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
_UpperCAmelCase : int = 100
_UpperCAmelCase : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_UpperCAmelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime no... | 683 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils ... | 683 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformer... | 683 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : str = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDe... | 683 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case = None ) -> Optional[int]:
_UpperCamelCase : int = value
_UpperCamelCase : Node | ... | 683 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 683 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils i... | 683 | 1 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_av... | 683 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase : Tuple = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transf... | 683 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_UpperCAmelCase ... | 683 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 683 | 1 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_UpperCAmelCase : Union[str, Any] = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
... | 683 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.para... | 683 | 1 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase : Union[str, Any] = {"""UserAgent""": UserAgent().random}
def snake_case__ ( UpperCamelCase ) -> dict:
_Upper... | 683 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
fr... | 683 | 1 |
'''simple docstring'''
_UpperCAmelCase : str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_UpperCAm... | 683 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""... | 683 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import Ba... | 683 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_UpperCAmelCase : Optional[int] = logging.get_... | 683 | 1 |
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