code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_available():
rai... | 203 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCAmelCase ( unittest.TestCase ):
__UpperCAmelCase : Union[str, Any] = JukeboxTokenizer
__UpperCAmelCase : Union[str, Any] ... | 203 | 1 |
"""simple docstring"""
from math import pi, sqrt
def snake_case__ ( __lowerCamelCase : float ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
if num > 1_71.5:
raise OverflowError('''math range error''' )
elif num - int... | 272 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =[0] * len(__lowerCamelCase )
lowerCamelCase__ : List[Any] =[]
lowerCamelCase__ : List[Any] =[1] * len(__lowerCamelCase )
... | 272 | 1 |
import argparse
from collections import defaultdict
import yaml
__UpperCamelCase : Optional[Any] = 'docs/source/en/_toctree.yml'
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = defaultdict(_lowercase )
SCREAMING_SNAKE_CASE ... | 182 | import itertools
import string
from collections.abc import Generator, Iterable
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(_lowercase )
while True:
SCREAMING_SNAKE_CASE : Optional[Any] = tup... | 182 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
... | 157 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git wo... | 157 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name_... | 96 |
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 46 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_co... | 59 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 59 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.compile(r"\b(a|an|the)\b", re.UNICODE)
SCREAMING_SNAKE_CASE__ : int = None
def __magic_name__ ( ) -> str:
_... | 270 |
import pprint
import requests
SCREAMING_SNAKE_CASE__ : str = "https://zenquotes.io/api"
def __magic_name__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def __magic_name__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random'... | 270 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_c... | 310 |
"""simple docstring"""
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 OptionalDependency... | 310 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_lowercase : Any = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language... | 93 | from collections import defaultdict
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 1
SCREAMING_SNAKE_CASE_ : Tuple = True
for v in tree[start]:
if v not in visited:
ret += dfs(lowerCAmelCase )
if r... | 18 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ = 0 ):
__SCREAMING_SNAKE_CASE = length or len(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
__SCREAMING_SNAKE_CA... | 361 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not i... | 255 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
lowercase__ : List[str] ... | 187 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""facebook/xmod-base""": """https://huggingface.co/faceboo... | 176 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase_ : Optional[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadava... | 359 |
from __future__ import annotations
class UpperCamelCase :
def __init__( self , UpperCAmelCase__=None ):
A__ = data
A__ = None
def __repr__( self ):
A__ = []
A__ = self
while temp:
st... | 198 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 119 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 203 | 0 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.... | 317 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 317 | 1 |
from collections.abc import Generator
from math import sin
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
UpperCamelCase :int =... | 38 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.Wav... | 38 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_... | 120 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestC... | 120 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from... | 31 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n... | 31 | 1 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(lowercase , lowercase ) or not number >= 1:
raise ValueE... | 319 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ... | 319 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/conf... | 113 |
"""simple docstring"""
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
__Upper... | 113 | 1 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __magic_name__ ( _a ):
def __init__( self , __snake_case="" , __snake_case="train" ) -> Optional[int]:
'''simple do... | 371 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 | 0 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__Upp... | 146 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 0 |
import os
_UpperCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase_( snake_case__: str ) -> int:
UpperCAmelCase__ = 0
UpperCAmelCase__ = 0
while index < len(snake_case__ ... | 335 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
Compu... | 335 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
fr... | 175 |
"""simple docstring"""
# Copyright 2021 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-... | 126 | 0 |
from __future__ import annotations
from fractions import Fraction
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A_ ( _UpperCAmelCase ):
SCREAMING_SN... | 360 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 127 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
f... | 194 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=5 ) -> Union[str, Any]:
"""simple docstring"""
assert masked_in... | 194 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1_024 ):
_UpperCAmelCase , _UpperCAmelC... | 322 |
'''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 (
TFBaseModelOutputWi... | 322 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["image_processor", "tokenizer"]
... | 33 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeq... | 182 | 0 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processor... | 369 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_fea... | 254 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if n_term == "":
return []
SCREAMING_SNAKE_CASE_: list =[]
for temp in range(int(lowercase ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __nam... | 173 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __magic_name__ ( lowercase ):
return np.maximum(0 , lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 173 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configu... | 371 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
_A = 42
_A = 42
class lowerCamelCase__ ... | 151 | 0 |
from collections.abc import Iterable
from typing import Any
class __A :
"""simple docstring"""
def __init__( self , lowerCamelCase__ = None ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] ... | 71 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from toke... | 190 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus imp... | 83 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 83 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_availa... | 185 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
A__ : List[str] = get_logger(__name__)
A__ : str = R"""
Args:
input_ids (`jnp.ndarray` ... | 185 | 1 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transformers.co... | 192 | import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
_UpperC... | 192 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
SCREAMING_SNAKE_CASE__... | 321 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_chec... | 155 | 0 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__ : List[str] = 'src/diffusers'
# Matches is_xxx_available()
A__ : Dict = re.compile(r'is\_([a-z... | 358 |
"""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 VaeImag... | 209 | 0 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def UpperCamelCase_ ( A__ : List[Any] ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 120 |
lowerCAmelCase : Optional[Any] = '\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 ... | 253 | 0 |
'''simple docstring'''
from __future__ import annotations
__A : Dict = list[tuple[int, int]]
__A : List[str] = [
[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],
... | 364 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _... | 89 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Dict = logging.get_logger(__name__)
lowerCAmelCase : Any = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/... | 253 |
from collections import deque
class _a :
"""simple docstring"""
def __init__( self: Union[str, Any] , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: int ):
'''simple docstring'''
UpperC... | 149 | 0 |
'''simple docstring'''
import sys
def __lowerCamelCase ( A__ ) -> Tuple:
"""simple docstring"""
UpperCamelCase = len(A__ )
UpperCamelCase = [[0 for x in range(A__ )] for x in range(A__ )]
UpperCamelCase = [[0 for... | 249 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = [0] * len(A__ )
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = 0
for values in graph.value... | 249 | 1 |
"""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,
... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase = 3, __lowerCamelCase = 7, __lowerCamelCase = 100_0000 ):
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 1
for current_denominator in range(1, limit + 1 ):
UpperCAmelCase_ : Dict ... | 61 | 1 |
'''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 _A ( __SCRE... | 359 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_... | 16 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 165 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class lowerCamelCase :
lowerCamelCase__ : Optional[str] = field(
default='codeparrot/codeparrot' ,metadata={'help': 'Model name or path of model to... | 165 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> bool:
lowerCAmelCase__ : int = int(number**0.5 )
return number == sq * sq
def lowerCAmelCase__ ( SCREAMING_SNAKE_C... | 307 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PR... | 307 | 1 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 94 |
'''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
#... | 104 | 0 |
import math
def __A ( __lowerCamelCase ) -> str:
a = 0
a = 0
while num > 0:
a = num % 8
a = octal + (remainder * math.floor(math.pow(10 , __lowerCamelCase ) ))
counter += 1
a = ma... | 351 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if versi... | 95 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
... | 348 | 0 |
"""simple docstring"""
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_dow... | 365 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 95 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe imp... | 343 |
def lowerCamelCase_ ( _a : List[str] ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = [0] * len(_a )
UpperCAmelCase_ : Dict = []
UpperCAmelCase_ : Optional[int] = []
UpperCAmelCase_ : Dict = 0
for values in graph.values... | 345 | 0 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLa... | 28 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __UpperCamelCase ( lowercase__ : int ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError("""Undefined for non-integers""" )
eli... | 28 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 85 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__A = {
"E": 1_2.7_0,
"T": 9.0_6,
"A": 8.1_7,
"O": 7.5_1,
"I": 6.9_7,
"N": 6.7_5,
"S": 6.3_3,
"H": 6.0_9,
"R": 5.9_9,
"D": 4.2_5,
"L": 4.0_3,
"C": 2.7_8... | 90 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : int = {'''configuration_plbart''': ['''PLBART_PRETRAIN... | 314 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : Optional[Any] = '''
import os
'''
snake_case__ : Tuple = '''
def foo():
import os
return False
'''
snake_case__ : Any ... | 314 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Tuple = logging.get_logger(__name__)
snake_case : Optional[Any] = {
'''facebook/d... | 94 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_ = logging.getLogger(__name__)
if __name__ == "__m... | 58 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : list[int] ) ->list[list[int]]:
A__ : Union[str, Any] = []
if len(UpperCAmelCase__ ) == 1:
return [nums.copy()]
for _ in range(len(UpperCAmelCase__ ) ):
A__ ... | 296 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
A_ = logging.get_logge... | 296 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQu... | 55 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
a_ : List[str] = TypeVar("""T""")
class snake_case ( Generic[T] ):
"""simple docstring"""
def __init__( self , UpperCamelCase ):
... | 55 | 1 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shape... | 285 |
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 import ... | 285 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase__ = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowerCAmelCase__ ... | 108 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ = 100
lowerCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
... | 108 | 1 |
"""simple docstring"""
from timeit import timeit
_lowerCAmelCase : List[Any] = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a c... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extractio... | 340 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
de... | 272 | '''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import ... | 272 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase: Optional[Any] = {"""processi... | 336 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if k in (0.04, 0.06):
_lowercase : Optional[Any] = k
_lowercase : Option... | 336 | 1 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
UpperCAmelCase__ : str = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
UpperCAmelC... | 25 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Any ... | 371 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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.numpy as jnp
from transf... | 279 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...... | 94 |
"""simple docstring"""
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : int ,A_ : int ) -> Union[str, Any]:
A = n
A = [None] * self.n
A = 0 # index of the first element
A = ... | 74 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCamelCase ( _A, _A, _A = "x", _A = 10**-10, _A = 1, ):
"""simple docstring"""
__magic_name__ : int = symbols(_A )
__magic_name__ : T... | 368 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_comm... | 138 | 0 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowercase__ : Optional[int] = datasets.utils.logging.get_logger(__name__)
@datacl... | 324 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A__ : Any = True
except (ImportError, ModuleNotFoundError):
A__ : str = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', q... | 144 | 0 |
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gp... | 355 |
"""simple docstring"""
import argparse
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_warmu... | 324 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def _snake_case( SCREAMING_SNAKE_CASE__="ro" , SCREAMING_SNAKE_CASE__="en" , SCREAMING_SNAKE_CASE__="wmt16" , SCREAMING_SNAKE_CASE__=None ) -> None:
try:
import datasets
except (ModuleNotFoundError, ... | 20 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : str = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfi... | 20 | 1 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATU... | 368 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
Uppe... | 61 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__n... | 136 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : int = {
"facebook/encode... | 136 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def lowercase ( A_ , A_ )-> str:
'''simple docstring'''
a : Tuple = BeautifulSoup(requests.get(A_ , params=A_ ).content , "html.parser" )
a ... | 226 |
"""simple docstring"""
import sys
import turtle
def lowercase ( A_ , A_ )-> tuple[float, float]:
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase ( A_ , A_ , A_ , A_ , ... | 226 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 319 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing impor... | 319 | 1 |
from __future__ import annotations
def snake_case (A_ :list[int] , A_ :int ):
'''simple docstring'''
a : list[list[int]] = []
a : list[int] = []
a : Optional[Any] = 0
a : List[str] = sum(A_ )... | 364 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : List[str] = logging.get... | 186 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowercase : Union[str, Any] = logging.getLogger(__name... | 42 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter)... | 42 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffus... | 226 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWit... | 226 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case :int = logging.get_logger(__name__)
__snake_case :Tuple = {
'''facebook/data2vec-text-ba... | 49 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ... | 49 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers... | 358 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class ... | 296 | 0 |
from __future__ import annotations
import math
def _UpperCamelCase ( lowercase__ , lowercase__ ):
if len(lowercase__ ) != 2 or len(a[0] ) != 2 or len(lowercase__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
... | 9 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) =... | 97 | 0 |
import heapq
def lowerCamelCase_ ( UpperCamelCase__ : dict ) -> set[int]:
"""simple docstring"""
__lowerCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue wil... | 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __low... | 348 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | """simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase__ : List[Any] = '\\n\n'
lowerCAmelCase__ : Tuple = '\nPerplexity (PPL) i... | 98 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_modeling_common i... | 63 | from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_p... | 63 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 169 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_lowerCAmelCase : int = get_logger(__name__)
_lowerCAmelCase : Any = r"\n Args:\n input_ids (`jnp.ndarray` of shape... | 169 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 363 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( UpperCamelCas... | 39 | 0 |
"""simple docstring"""
import numpy as np
def lowercase (snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase (snake_case__ : np.ndarray ) -> np.ndar... | 155 |
"""simple docstring"""
def lowercase (snake_case__ : list ) -> list:
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowerCAmelCase = []
def generate(snake_case__ : int ... | 155 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_lowercase : Any = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_lowercase : Optional[Any] ... | 21 | '''simple docstring'''
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
_lowercase : Union[str, Any] ... | 21 | 1 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_a... | 81 | import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__snake_case = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_tru... | 348 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
),
... | 63 | # Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( Tenso... | 63 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 36 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 36 | 1 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ = "▁"
SCREAM... | 350 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__snake_case = logging.getLogger(__name__)
class UpperCAmelCase_ :
"""simple docstring"""
def __init... | 259 |
def _A ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
# Return True if there is node that has not iterated.
UpperCamelCase :Tuple = [False] * len(SCRE... | 259 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> Tuple:
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(A_ ) * abs(A_ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=T... | 358 |
'''simple docstring'''
import random
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A = False ) -> dict:
_snake_case = {i: [] for i in range(__A )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
ret... | 160 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.ja... | 34 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL ... | 34 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
"microsoft/git-base": "https://hug... | 104 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base impor... | 104 | 1 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""... | 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1... | 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase : Dict = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig'''... | 358 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 36 | 0 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassi... | 178 |
import unittest
import numpy as np
from transformers import RobertaConfig, 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 transformers.mo... | 178 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 127 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 127 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a ( UpperC... | 110 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE... | 110 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Any ):
"""simple docstring"""
... | 249 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 249 | 1 |
"""simple docstring"""
import unittest
from transformers import 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,
)
from ... | 105 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : list ) ->int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(g... | 105 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 352 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def S... | 167 | 0 |
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