code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
... | 15 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = [
"""encoder.version""",
... | 15 | 1 |
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int , __magic_name__ : bool , __magic_name__ : list[int] , __magic_name__ : float ) -> int:
"""simple docstr... | 15 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 1 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils imp... | 15 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 15 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[int] = logging.get_logger(__name__)
A : Any = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json... | 15 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A ( UpperCAmelCase__ ):
'''simple docstring'''
... | 15 | 1 |
from math import ceil
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = list(range(0 , __magic_name__ ) )
lowercase__ = [item for su... | 15 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
... | 15 | 1 |
A : List[Any] = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transfo... | 15 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
... | 15 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image... | 15 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A : ... | 15 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Optional[Any] = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class ... | 15 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import ... | 15 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 15 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 15 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__... | 15 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from .... | 15 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Tuple = {
'kssteven/ibert-roberta-base': ... | 15 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Optional[Any] = {'vocab_file': 'vocab.json'}
A : Optional[int] ... | 15 |
from math import log
from scipy.constants import Boltzmann, physical_constants
A : Any = 3_0_0 # TEMPERATURE (unit = K)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ) -> ... | 15 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_remb... | 15 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = N... | 15 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class A :
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : List[Any]=2 , _UpperCAmelCase ... | 15 |
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
A : Any = logging.get_logger(__name__)
A : Tuple = {
'sai... | 15 | 1 |
import math
import random
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : bool = False ) -> float:
"""simple docstring"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
A : Di... | 15 |
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 A ( Uppe... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : int , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] <... | 15 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvN... | 15 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : str = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise Opt... | 15 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : List[str]=7 ) -> Dict:
"""simple docstring"""
lowercase__ = ... | 15 | 1 |
from __future__ import annotations
A : Optional[Any] = []
def UpperCamelCase ( __magic_name__ : list[list[int]] , __magic_name__ : int , __magic_name__ : int ) -> bool:
"""simple docstring"""
for i in range(len(__magic_n... | 15 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 15 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = ArgumentParser(
... | 15 |
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_warmup, set_seed
from accelerate import Ac... | 15 | 1 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 |
import os
import sys
A : Tuple = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassific... | 15 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ... | 15 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 15 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
... | 15 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = [
"""encoder.version""",
... | 15 | 1 |
import doctest
from collections import deque
import numpy as np
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> None:
"""simple docstring"""
lowercase__ = [2, 1, 2, -1]
lowercase__ = ... | 15 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 1 |
from scipy.stats import spearmanr
import datasets
A : int = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlatio... | 15 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 15 | 1 |
import argparse
import struct
import unittest
class A :
'''simple docstring'''
def __init__(self : Union[str, Any] , _UpperCAmelCase : bytes ) -> None:
"""simple docstring"""
lowercase__ = data
# Init... | 15 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A ( UpperCAmelCase__ ):
'''simple docstring'''
... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : int ) -> int:
"""simple docstring"""
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError("""The length of profit and weight must be same.""" )
... | 15 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
... | 15 | 1 |
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 A ( Uppe... | 15 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
A : Optional[Any] = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', '... | 15 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 | 1 |
import warnings
from .generation import TFGenerationMixin
class A ( UpperCAmelCase__ ):
'''simple docstring'''
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
'''be remove... | 15 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A : ... | 15 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have ... | 15 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 1 |
from ..utils import DummyObject, requires_backends
class A ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''onnx''']
def __init__(self : List[Any] , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Any ) -> ... | 15 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 15 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A : Union[str, Any] = ['small', 'medium', 'large']
A : int = 'lm_head.decoder.weight'
A : str = 'lm_head.weight'
def UpperCamelCase ( __magic_nam... | 15 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__... | 15 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.uti... | 15 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Tuple = {
'kssteven/ibert-roberta-base': ... | 15 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ :... | 15 |
from math import log
from scipy.constants import Boltzmann, physical_constants
A : Any = 3_0_0 # TEMPERATURE (unit = K)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ) -> ... | 15 | 1 |
from collections.abc import Callable
import numpy as np
def UpperCamelCase ( __magic_name__ : Callable , __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> np.array:
... | 15 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = N... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the lea... | 15 |
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
A : Any = logging.get_logger(__name__)
A : Tuple = {
'sai... | 15 | 1 |
import math
import os
import sys
def UpperCamelCase ( __magic_name__ : str ) -> str:
"""simple docstring"""
lowercase__ = """"""
try:
with open(__magic_name__ , """rb""" ) as binary_file:
lowercase__ = binary_file.read(... | 15 |
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 A ( Uppe... | 15 | 1 |
from ..utils import DummyObject, requires_backends
class A ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : str , *_UpperCAmelCase : Tuple , **_UpperCAmelCa... | 15 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvN... | 15 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : List[str]=7 ) -> Dict:
"""simple docstring"""
lowercase__ = ... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
assert isinstance(__magic_name__ , __magic_name__ ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
lowercase__ = ... | 15 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 15 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 15 |
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_warmup, set_seed
from accelerate import Ac... | 15 | 1 |
import math
A : List[Any] = 1_0
A : Union[str, Any] = 7
A : List[str] = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( __magic_name__ : int = 20 ) -> str:
"""simple docstring"""
lowercase__ = math.comb(_... | 15 |
import os
import sys
A : Tuple = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassific... | 15 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase ( __magic_name__ : Union[dict, list, tuple, tor... | 15 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 15 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
A : i... | 15 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = [
"""encoder.version""",
... | 15 | 1 |
import numpy as np
import datasets
A : List[str] = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced ... | 15 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 1 |
import argparse
import os
import re
A : List[Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
A : Any = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=... | 15 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 15 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 15 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A ( UpperCAmelCase__ ):
'''simple docstring'''
... | 15 | 1 |
import datasets
from .evaluate import evaluate
A : Union[str, Any] = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n ... | 15 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
... | 15 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
A : Tuple = logging.getLogger(__name__)
class A ( UpperCAmelCase__ ):
'''simple do... | 15 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@requi... | 15 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available(... | 15 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A : ... | 15 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 15 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_arra... | 15 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : int , __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
if index == number_of_items:
return 0
lowerc... | 15 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__... | 15 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_com... | 15 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Tuple = {
'kssteven/ibert-roberta-base': ... | 15 | 1 |
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 must be checked before tokeni... | 15 |
from math import log
from scipy.constants import Boltzmann, physical_constants
A : Any = 3_0_0 # TEMPERATURE (unit = K)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ) -> ... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : int = 10**9 ) -> int:
"""simple docstring"""
lowercase__ = 1
lowercase__ = 2
lowercase__ = 0
lowercase__ = 0
lowercase__ = 0
while perimeter <= max_perimeter:
perimeters_s... | 15 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = N... | 15 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
... | 15 |
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
A : Any = logging.get_logger(__name__)
A : Tuple = {
'sai... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : str ) -> bool:
"""simple docstring"""
lowercase__ = 0
for ch in input_str:
lowercase__ = ord(__magic_name__ )
lowercase__ = pow(2 , __magic_name__ )
# If we already turned on bit fo... | 15 |
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 A ( Uppe... | 15 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_ten... | 15 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvN... | 15 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 15 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : List[str]=7 ) -> Dict:
"""simple docstring"""
lowercase__ = ... | 15 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Tuple = {
'kssteven/ibert-roberta-base': ... | 15 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 15 | 1 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase ( __magic_name__ : Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
... | 15 |
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_warmup, set_seed
from accelerate import Ac... | 15 | 1 |
class A :
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : str = "" , _UpperCAmelCase : bool = False ) -> None:
"""simple docstring"""
lowercase__ = {}
# A node will be a leaf if the... | 15 |
import os
import sys
A : Tuple = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassific... | 15 | 1 |
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_fu... | 15 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 15 | 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
| 15 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = [
"""encoder.version""",
... | 15 | 1 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import Gradie... | 15 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 1 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] , __magic_name__ : int ) -> list[int]:
"""simple docstring"""
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
while i < j:
if nums... | 15 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 15 | 1 |
import copy
import re
class A :
'''simple docstring'''
A__ = '''hp'''
A__ = {}
A__ = None
@classmethod
def lowerCamelCase__ (cls : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] ... | 15 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A ( UpperCAmelCase__ ):
'''simple docstring'''
... | 15 | 1 |
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
from transform... | 15 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
... | 15 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAut... | 15 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
A : Dict = logging.get_logger(__name__)
A :... | 15 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 | 1 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 15 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A : ... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : list ) -> list:
"""simple docstring"""
def merge(__magic_name__ : list , __magic_name__ : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 15 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : str ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
lowercase__ = sorted(string.lower() )
return len(__magic_name__ ) =... | 15 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 15 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
A : str = logging.get_logger(__name__)
A : str = {
'post_extract_proj': 'feature_projecti... | 15 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__... | 15 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : str = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 15 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Tuple = {
'kssteven/ibert-roberta-base': ... | 15 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
... | 15 |
from math import log
from scipy.constants import Boltzmann, physical_constants
A : Any = 3_0_0 # TEMPERATURE (unit = K)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ) -> ... | 15 | 1 |
def UpperCamelCase ( __magic_name__ : Optional[Any] , __magic_name__ : Tuple ) -> int:
"""simple docstring"""
lowercase__ = """"""
for i in table:
res += inp[i - 1]
return res
def UpperCamelCase ( __magic_name__ : int... | 15 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = N... | 15 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class A ( UpperCAmelCase__ , unittest.Test... | 15 |
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
A : Any = logging.get_logger(__name__)
A : Tuple = {
'sai... | 15 | 1 |
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_keras_nlp, req... | 15 |
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 A ( Uppe... | 15 | 1 |
import warnings
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 A ( UpperCAmelCase__ ):
... | 15 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvN... | 15 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elast... | 15 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : List[str]=7 ) -> Dict:
"""simple docstring"""
lowercase__ = ... | 15 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 15 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 15 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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... | 15 |
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_warmup, set_seed
from accelerate import Ac... | 15 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
f... | 15 |
import os
import sys
A : Tuple = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassific... | 15 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : List[Any] = logging.get_logger(__name__)
A : Union[str, Any... | 15 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 15 | 1 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
A : Optional[int] = logging.getLogger()
... | 15 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = [
"""encoder.version""",
... | 15 | 1 |
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_features_output_indices... | 15 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCamelCase ( __magic_name__ : int = 8 ) -> str:
"""simple docstring"""
lowercase__ = ascii_letters + digits + punctuat... | 15 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 15 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
A : Optional[int] = TypeVar('T')
class A ( Generic[T] ):
'''simple docstring'''
def __init__(self : int , _UpperCAmelCase ... | 15 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A ( UpperCAmelCase__ ):
'''simple docstring'''
... | 15 | 1 |
from __future__ import annotations
from math import pi
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).co... | 15 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
... | 15 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : List[str]=7 ) -> Dict:
"""simple docstring"""
lowercase__ = ... | 15 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''image_processor''', '''tokenizer''']
A__ = '''AutoImageProcessor'''
A__ ... | 15 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = N... | 15 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A : ... | 15 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : Union[str, Any] = logg... | 15 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 1 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : bool = False ) -> list[float]:
... | 15 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 15 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
A : List[str] = logging.getLogger(__name__)
if is_torch_tpu_available... | 15 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__... | 15 | 1 |
class A :
'''simple docstring'''
def __init__(self : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = {}
def lowerCamelCase__ (self : Union[str, Any] ) -> None:
"""simple ... | 15 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Tuple = {
'kssteven/ibert-roberta-base': ... | 15 | 1 |
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