code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase : Tuple = logging.getLogger(__name__)
class __snake_case ( lowerCAmelCase ):
def __init__( self ,snake_case=... | 20 | """simple docstring"""
from math import factorial
def UpperCAmelCase ( UpperCAmelCase = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fa... | 69 | 0 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE : Optional[int] = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n a... | 21 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''Muhammad Umer Farooq'''
__SCREAMING_SNAKE_CASE :Optional[int] = '''MIT'''
__SCREAMING_SNAKE_CASE :Any = '''1.0.0'''
__SCREAMING_SNAKE_CASE :Union[str, Any] =... | 22 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCamelCase__: Union[str, Any] = logging.get_logger(__name__)
cl... | 23 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avai... | 24 | """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 = logging.get_logger(__name__)
__Up... | 69 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch im... | 25 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
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 lowercase ( ... | 26 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
__a : List[str] = list(_SCREAMING_SNAKE_CASE )... | 27 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
'''simple docstring'''
def __lowerCamelCase ( ) -> Dict:
"""simple docstring"""
UpperCamelCase = 0
for i in range(1 , 1_001 ):
total += i**i
return str(A__ )[-10:]
if __name__ == "__main__":
print(solution())
| 28 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resi... | 29 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :str = CustomTokenizer
pass
| 30 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-... | 31 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoMode... | 32 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Ten... | 33 | """simple docstring"""
import inspect
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_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
'''simple docstring'''
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 _a ( unittest.TestCase ):
def ... | 34 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[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],
... | 69 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"configuration_maskformer_... | 35 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = SwinConfig(image_size=192 ... | 36 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( __UpperCAmelCase ) -> Optional[Any]:
... | 37 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 38 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from data... | 39 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.conf... | 40 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
'''simple docstring'''
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, ... | 41 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 50 ) -> int:
_snake_case = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_lengt... | 42 | """simple docstring"""
from math import factorial
def UpperCAmelCase ( UpperCAmelCase = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fa... | 69 | 0 |
import math
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 43 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require... | 44 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int ) -> str:
if number > 0:
raise ValueError('''input must be a negative integer''' )
__a = len(bin(lowerCAmelCase__ )[3:] )
__a = bin(abs(lowerCAmelCase__ ) - (1 << binary_nu... | 45 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version... | 46 | """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 = logging.get_logger(__name__)
__Up... | 69 | 0 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class A__ :
def __init__( self : Tuple , _a : Any , _a : int , _a : int ) -> List[str]:
'''simple docstring'''
if ... | 47 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
from collections import UserDict
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():
... | 48 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
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
)
__snake_case :str = logging.getLogger(__name__)
if __name__ == "__main__":... | 49 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
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_commo... | 50 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
f... | 51 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 52 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self : Optional[int] , __A : list[list[int]] ):
__UpperCamelCase = TypeError(
'Matrices must be formed from a list of ... | 53 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
... | 54 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
a_ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokeni... | 55 | """simple docstring"""
import inspect
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_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from ... | 56 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[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],
... | 69 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 57 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowercase_ = """Usage of script: script_name <size_of_canvas:int>"""
lowercase_ = [0] * 100 + [1] * 10
random.shuffle(choice)
def lowerCamelCase ... | 58 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def UpperCamelCase ( ):
snake_case , snake_case : Union[str, Any] = 9, 14 # noqa: F841
snake_case : int = [
[0, 1, 4],
... | 59 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
snake_case__ : Optional[Any] = logging.getLogger(__name__)
def _snake_case ( ):
lowerCAmelCase : Tuple = argparse.ArgumentParser(
... | 60 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct... | 61 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
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 ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPo... | 62 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( lowercase : Optional[int] , lower... | 63 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : list[str] ):
"""simple docstring"""
_snake_case : int = """"""
for word_or_phrase in separated:
if not isinstance(snake_case__ , snake_case__ ):
... | 64 | """simple docstring"""
from math import factorial
def UpperCAmelCase ( UpperCAmelCase = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fa... | 69 | 0 |
import pprint
import requests
UpperCamelCase__ = 'https://zenquotes.io/api'
def lowerCAmelCase_ ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def lowerCAmelCase_ ( ) -> ... | 65 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def A_ ( _lowercase ):
'''simple docstring'''
create_state_space_tree(_lowercase, [], 0 )
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
if i... | 66 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ = 50 ) -> int:
__lowerCamelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 67 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 68 | """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 = logging.get_logger(__name__)
__Up... | 69 | 0 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
A__ : List[str] ={
'''f... | 70 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
A_ :int = 8.314_4598
def A ( a_ ,a_ ) -> float:
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
if molar_mass <= 0:
raise Exception('Molar mass cannot be less than or equal t... | 71 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, ... | 72 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
import argparse
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
__lowerCamelCase : List[str] = F"{file}_{class_name}_{test_name}"
done... | 73 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectr... | 74 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : float , __snake_case : float , __snake_case : float ) -> dict[str, float]:
"""simple docstring"""
if (voltage, current, resistance).count(... | 75 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = 'https://openaipublic.azureedge.net/jukebox/mode... | 76 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : list[list[int]] = [[0 for _ in range(_lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowercase__ : List[Any] = 1
... | 77 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm... | 78 | """simple docstring"""
import inspect
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_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTr... | 79 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[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],
... | 69 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : str = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/res... | 80 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_com... | 81 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils... | 82 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : List[str] = abs(UpperCAmelCase_ )
_UpperCamelCase : int = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def A__ ( UpperCAmelCase_ ):
... | 83 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _snake_case ( ) -> Dict:
'''simple docstring'''
import os as original_os
from os import path as original_path
from o... | 84 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
import requests
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(s... | 85 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
lowerCamelCase__ = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logging... | 86 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models fo... | 87 | """simple docstring"""
from math import factorial
def UpperCAmelCase ( UpperCAmelCase = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fa... | 69 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIG... | 88 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> set:
_a : Dict = set()
# edges = list of graph's edges
_a : Any = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (fr... | 89 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import Aud... | 90 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
"""simple docstring"""
from manim import *
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] ... | 91 | """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 = logging.get_logger(__name__)
__Up... | 69 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class a__ ( unittest.TestCase ):
_a : Optional[int] = inspect.getf... | 92 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_lowercase : Any = (7_2_0, 1_2_8_0) # Height, Width
_lowercase : List[Any] = (0.4, 0.6) # if height or width ... | 93 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
from __future__ import annotations
snake_case : List[Any] = tuple[int, int, int]
snake_case : Union[str, Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
snake_case : Optional[Any] = '''ABCDEFGHIJKLMNOP... | 94 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class __lowerCAmelCa... | 95 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"""configuration_unispeech""": ["""UNISPEECH_PRETRA... | 96 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__snake_case = 637_8137.0
__snake_case = 635_6752.31_4245
__snake_case = 6378137
def a ( __a , __a , __a , __a ) -> floa... | 97 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as ... | 98 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Any = logging.get_logger(__name__)
def A_ ( A__ ) -> List[str]:
a__ : Union[str, Any] = ... | 99 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
__magic_name__ = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PI... | 100 | """simple docstring"""
import inspect
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_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinis... | 101 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[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],
... | 69 | 0 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...... | 102 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
A__ : ... | 103 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
lowerCAmelCase__ = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
lowerCAmelCase__ = '''
Arg... | 104 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
a : Any = TypeVar('''KT''')
a : List[str] = TypeVar('''VT''')
class __UpperCamelCase ( Generic[KT, VT] ):
... | 105 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ = 1_00_00_00 ):
lowerCAmelCase__ : Optional[Any] = limit + 1
lowerCAmelCase__ : Dict = [0] * limit
for first_term in range(1 , A_ ):
for n in range(A_ , A_ , A_ ):
lowerCAmelCase__ : Union[s... | 106 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lo... | 107 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import h... | 108 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_... | 109 | """simple docstring"""
from math import factorial
def UpperCAmelCase ( UpperCAmelCase = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fa... | 69 | 0 |
import os
import sys
import unittest
lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fin... | 110 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def __magic_name__ ... | 202 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
def A_ ( A__ ) -> Dict:
a__ : str = len(A__ )
a__ : List[str] = sum(A__ )
a__ : Tuple = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
a__ : Union[str, Any] = True
fo... | 99 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
A_ : int = 65521
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
__UpperCAmelCase = 1
__UpperCAmelCase = 0
for plain_chr in plain_text:
__UpperCAmelCase = (a + ord(SCREAMING_SNAKE_CASE )) % MOD_ADLER
... | 333 | """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 = logging.get_logger(__name__)
__Up... | 69 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.... | 123 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONF... | 48 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCAmelCase : Union[str, Any] = R"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mod... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A__: Optional[int] = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
... | 276 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
_snake_case = list[tuple[int, int]]
_snake_case = [
[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],
[0, 0, 0, 0,... | 250 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
'''simple docstring'''
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
__a = logging.get_logger(__name__)
def __UpperCAmelCase ( a... | 145 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
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