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 |
|---|---|---|---|---|
"""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
__lowerCAmelCase : Optional[Any] = ... | 644 |
'''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 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] =logging.get_logger(__name__)
A_ : Tuple ={
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.j... | 274 |
'''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 | 0 |
'''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_... | 212 |
'''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 | 0 |
'''simple docstring'''
from collections import defaultdict
def snake_case__ ( _A: str , _A: Dict ) -> bool:
'''simple docstring'''
lowerCAmelCase = first_str.lower().strip()
lowerCAmelCase = second_str.lower().strip()
# Remove whitespace
lowerCAmel... | 370 |
'''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 | 0 |
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase_ : Optional[Any] = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> List[str]:
"""simple docstring"""
a_ : int ... | 570 |
'''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 | 0 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMix... | 205 |
'''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 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
'''simple docs... | 113 |
'''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 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_A : Optional[Any] = logging.get_logger(__name__)
def _a ( UpperCAmelCase ) -> List[int]:
"""simple docstring"""
if isinstance(UpperCAmelCase ... | 315 |
'''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 | 0 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, l... | 20 |
'''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 | 0 |
'''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_squeezebert import SqueezeBertTokenizer
a__ : str =logging.get_logger(__name__... | 399 |
'''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 | 0 |
'''simple docstring'''
from copy import deepcopy
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase = None ,__UpperCAmelCase = None ) -> None:
if arr is None and size is not None:
lowerCAmelCase__ : Optional[int] ... | 565 |
'''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 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = len(lowerCamelCase__ )
lowerCAmelCase__ = len(matrix[0] )
lowerCAmelCase__ = min(lowerCamelCase__ , lowerCamelCase__ )
for row in range(lowerCamelCase__ ):
# ... | 644 |
'''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 | 0 |
'''simple docstring'''
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __UpperCAmelCase ( a_ ... | 274 |
'''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 | 0 |
'''simple docstring'''
import numpy as np
from PIL import Image
def __snake_case ( _UpperCAmelCase : Optional[int], _UpperCAmelCase : Any, _UpperCAmelCase : int):
UpperCamelCase = np.array(_UpperCAmelCase)
if arr.shape[0] != arr.shape[1]:
r... | 212 |
'''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 | 0 |
'''simple docstring'''
from __future__ import annotations
__lowercase = list[tuple[int, int]]
__lowercase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
... | 370 |
'''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 | 0 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str ) -> Optional[Any]:
a_ : int = text, pattern
a_ :... | 570 |
'''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 | 0 |
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 .tokenization_albert impor... | 205 |
'''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 | 0 |
from __future__ import annotations
_lowerCAmelCase : Union[str, Any] =[True] * 1_00_00_01
_lowerCAmelCase : Optional[Any] =2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
_lowerCAmelCase : List[Any] =False
i += 1
def ... | 113 |
'''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 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a_ )
class __SCREAMING_SNAKE_CASE ( a_ ):
_UpperCAmelCase : str = field(default="summarization" ,metadata={... | 315 |
'''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 | 0 |
def _lowercase( ):
a__ =[]
a__ =1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
a__ =''''''.join(__a )
return (
int(constant[0] )
* int(constant[9] )
* int(const... | 20 |
'''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 | 0 |
'''simple docstring'''
from manim import *
class snake_case ( a_ ):
"""simple docstring"""
def _lowerCamelCase ( self : int ):
__UpperCamelCase = Rectangle(height=0.5 , width=0.5 )
__UpperCamelCase = Rectangle(height=0.46 , width=0... | 399 |
'''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 | 0 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , **UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = AutoConfig.... | 565 |
'''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 | 0 |
"""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 .toke... | 644 |
'''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 | 0 |
'''simple docstring'''
from manim import *
class __UpperCAmelCase ( a_ ):
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
l... | 274 |
'''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 | 0 |
'''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,
MusicgenForConditi... | 212 |
'''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 | 0 |
'''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,
)
__lowercase = {
"""configuration_xlm... | 370 |
'''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 | 0 |
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_available():
... | 570 |
'''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 | 0 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ : List[Any] = Lock()
def lowercase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMI... | 205 |
'''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 | 0 |
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
UpperCAmelCase__: Optional[int] = set_counts
UpperCAmelCase__: Optional[int] = max(_snake_case )
UpperCAmelCase__: Union[str, Any] = len(_snake... | 113 |
'''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 | 0 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Tuple:
"""simple docstring"""
lowerCamelCase__ : Optional[Any] = ''''''
for i in table:
res += inp[i - 1]
return res
def _a ( UpperCAmelCase ) -> List[Any]:
"""simple doc... | 315 |
'''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 | 0 |
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_tokenizers
class ... | 20 |
'''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 | 0 |
'''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 transform... | 399 |
'''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 | 0 |
'''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 ..pipe... | 565 |
'''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 | 0 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 644 |
'''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 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A_ : Tuple ={
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfi... | 274 |
'''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 | 0 |
'''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 ... | 212 |
'''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 | 0 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a__( a_ ):
'''simple docstring'''
... | 370 |
'''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 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( __A : Union[str, Any] = "" ) -> dict[str, float]:
"""simple docstring"""
a_ : List[Any] = url or '''https:/... | 570 |
'''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 | 0 |
def lowercase ( SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_ = True
for i in range(0 , len(SCREAMING... | 205 |
'''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 | 0 |
import pytest
_lowerCAmelCase : List[Any] ="""__dummy_dataset1__"""
_lowerCAmelCase : Optional[int] ="""
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", ... | 113 |
'''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 | 0 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
'''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 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowerCAmelCase: Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
_lowerCAmelCase: Tuple = None
def _lowercase( ):
a__ =argparse.Argume... | 20 |
'''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 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[Any] =logging.get_logger(__name__)
a__ : List[str] ={
"""microsoft/focal... | 399 |
'''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 | 0 |
'''simple docstring'''
import copy
import re
class lowerCAmelCase_:
'''simple docstring'''
__lowercase : List[Any] = 'hp'
__lowercase : List[Any] = {}
__lowercase : Any = None
@classmethod
def UpperCAmelCase_ ( cls ,__UpperCA... | 565 |
'''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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : str = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()... | 644 |
'''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 | 0 |
'''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 ... | 274 |
'''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 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
snake_case_ : 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 bin... | 212 |
'''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 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils... | 370 |
'''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 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def SCREAMING_SNAKE_CASE_ ( __A : str = True , *__A : Union[str, Any] , **__A : Tuple ) -> ... | 570 |
'''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 | 0 |
from math import pi, sqrt, tan
def lowercase ( SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowercase ( SCREAMING_SNAKE_CASE , SC... | 205 |
'''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 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 113 |
'''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 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 315 |
'''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 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_lowerCAmelCase: Any = TypeVar('T')
class lowercase_ (Generic[T] ):
def __init__( self , lowercase_) -> List[Any]:
a__ =data
a_... | 20 |
'''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 | 0 |
'''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 transfo... | 399 |
'''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 | 0 |
'''simple docstring'''
_lowerCAmelCase = range(2, 20 + 1)
_lowerCAmelCase = [10**k for k in range(ks[-1] + 1)]
_lowerCAmelCase = {}
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstr... | 565 |
'''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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase : int = {
"""configuration_speech_to_te... | 644 |
'''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 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Any =logging.get_logger(__name__)
A_ : int ={
"""vocab_file""": """vocab.json"... | 274 |
'''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 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowercase__ :
'''simple docstring'''
_snake_case = 42
_snake_case = None
_snake_case = None
... | 212 |
'''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 | 0 |
'''simple docstring'''
from math import sqrt
def snake_case__ ( _A: Optional[Any] ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
for i in range(1 , int(sqrt(_A ) + 1 ) ):
if n % i == 0 and i != sqrt(_A ):
total += i + n // i
el... | 370 |
'''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 | 0 |
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 transformers.uti... | 570 |
'''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 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __SCREAMING_SNAKE_CASE ( lowercase):
def __init__( self : Any ... | 684 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
... | 684 | 1 |
__lowerCAmelCase = [0, 2, 4, 6, 8]
__lowerCAmelCase = [1, 3, 5, 7, 9]
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return ... | 684 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER... | 684 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"distilbert-base-uncased"... | 684 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
return getitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
return setitem, ... | 684 | 1 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
if len(_lowerCAmelCase ) <= 1:
return [tuple(_lowerCAmelCase )]
_UpperCAmelCase = []
def generate(_lowerCAmelCase , _lowerCAmelCase ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generat... | 684 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while l... | 684 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_M... | 684 |
__lowerCAmelCase = 2_5_6
# Modulus to hash a string
__lowerCAmelCase = 1_0_0_0_0_0_3
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
... | 684 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSe... | 684 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.uti... | 684 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILIm... | 684 |
# 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
#
# Unless re... | 684 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __SCREAMING_SNAKE_CASE ( lowercase):
def __lt__( self : str , __UpperCamelCase : Union[str, Any] ):
ret... | 684 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in ... | 684 | 1 |
import pytest
__lowerCAmelCase = "__dummy_dataset1__"
__lowerCAmelCase = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"val... | 684 |
import json
from typing import TYPE_CHECKING, 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_blenderbot... | 684 | 1 |
import numpy as np
def __lowerCamelCase ( _lowerCAmelCase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.g... | 684 | 1 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __SCREAMING_SNAKE_CASE ( lowercase):
def __init__( self : Optional[int] , __UpperCamelCase : Dict ... | 684 |
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
_UpperCAmelCase = []
_UpperCAmelCase = set({"(", "[", "{"} )
_UpperCAmelCase = set({")", "]", "}"} )
_UpperCAmelCase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_lowerCAme... | 684 | 1 |
import argparse
import json
import os
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_seed
from acceler... | 684 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equatio... | 684 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __SCREAMING_SNAKE_CASE (... | 684 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
# In... | 684 | 1 |
__lowerCAmelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__lowerCAmelCase ... | 684 |
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,
slow,
)
... | 684 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __SCREAMING_SNAKE_CASE ( lowercase , unittest.Tes... | 684 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvaila... | 684 | 1 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __SCREAMING_SNAKE_CASE ( lowercase):
def __init__( self : Optional[int] , __UpperCamelCase : List[str]="" , __UpperCamelCase : Optional[Any]="train" ):
... | 684 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCRE... | 684 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_... | 684 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __UpperCamelCase : List[Any]=0 ): # a graph with Node 0,1,...,N-1
_UpperCAmelCase = n
_UpperCAmelCase = [
[math.inf for j in range... | 684 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__lowerCAmelCase ... | 684 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING... | 684 | 1 |
from __future__ import annotations
from typing import Any
def __lowerCamelCase ( _lowerCAmelCase ) -> None:
create_state_space_tree(_lowerCAmelCase , [] , 0 )
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> None:
if index == le... | 684 |
import requests
__lowerCAmelCase = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def __lowerCamelCase ( _lowerCAmelCase ) -> None:
# fetching a list of articles in json format
_UpperCAmelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()... | 684 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __lowerCamelCase ( ) -> Optional[int]:
_UpperCAmelCase = ArgumentParser(
description=(
"PyTorch TPU di... | 684 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Any ):
_UpperCAmelCase ... | 684 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__lowerCAmelCase = "\nimport os\n"
__lowerCAmelCase = "\ndef foo():\n import os\n return False\n"
__lowerCAmelCase = "\ndef foo():\n def bar():\n if True:\n ... | 684 |
from __future__ import annotations
from collections import namedtuple
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> tuple:
_UpperCAmelCase = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
... | 684 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__lowerCAmelCase = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
... | 684 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCamelCase ( _lowerCAmelCase ) -> Any:
_UpperCAmelCase = {}
_UpperCAmelCase = job["started_at"]
_UpperCAmelCase = job["completed_at"]... | 684 | 1 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
assert x is not None
assert y is not None
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
# declaring the array for storing the dp values
_UpperCAme... | 684 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
... | 684 | 1 |
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = [1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0, 0, 0
_UpperCAmelCase = ugly_nums[ia] * 2
_UpperCAmelCase = ugly_nums[ia] * 3
_UpperCAmel... | 684 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER... | 684 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from trans... | 684 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
return getitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
return setitem, ... | 684 | 1 |
def __lowerCamelCase ( _lowerCAmelCase = 600_851_475_143 ) -> int:
try:
_UpperCAmelCase = int(_lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater... | 684 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while l... | 684 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_... | 684 |
__lowerCAmelCase = 2_5_6
# Modulus to hash a string
__lowerCAmelCase = 1_0_0_0_0_0_3
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
... | 684 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Co... | 684 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.uti... | 684 | 1 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCAmelCase = "src/transformers"
... | 684 |
# 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
#
# Unless re... | 684 | 1 |
from math import sqrt
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = 0
for i in range(1 , int(sqrt(_lowerCAmelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(_lowerCAmelCase ):
total += i + n // i
elif i == sqrt(_lowerCAmelCase ):
total ... | 684 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in ... | 684 | 1 |
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
assert column_title.isupper()
_UpperCAmelCase = 0
_UpperCAmelCase = len(_lowerCAmelCase ) - 1
_UpperCAmelCase = 0
while index >= 0:
_UpperCAmelCase = (ord(column_title[index]... | 684 |
import json
from typing import TYPE_CHECKING, 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_blenderbot... | 684 | 1 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
... | 684 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.g... | 684 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolv... | 684 |
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
_UpperCAmelCase = []
_UpperCAmelCase = set({"(", "[", "{"} )
_UpperCAmelCase = set({")", "]", "}"} )
_UpperCAmelCase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_lowerCAme... | 684 | 1 |
from collections.abc import Callable
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , __UpperCamelCase : Callable | None = None ):
# Stores actual heap items.
_UpperCAmelCase = []
# Stores indexes of each item for sup... | 684 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equatio... | 684 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elast... | 684 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
# In... | 684 | 1 |
__lowerCAmelCase = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared... | 684 |
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,
slow,
)
... | 684 | 1 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equatio... | 684 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvaila... | 684 | 1 |
import json
from typing import TYPE_CHECKING, 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_blenderbot... | 684 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCRE... | 684 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 684 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __UpperCamelCase : List[Any]=0 ): # a graph with Node 0,1,...,N-1
_UpperCAmelCase = n
_UpperCAmelCase = [
[math.inf for j in range... | 684 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__lowerCAmelCase = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.pro... | 684 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING... | 684 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.