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 |
|---|---|---|---|---|
def a__ ( A__, A__ ):
return number | (1 << position)
def a__ ( A__, A__ ):
return number & ~(1 << position)
def a__ ( A__, A__ ):
return number ^ (1 << position)
def a__ ( A__, A__ ):
... | 101 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 0 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
__magic_name__ : Tuple = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def UpperCamelCase ():
UpperCamelCase : List[Any] = os.path.dirname(os.path.realpath... | 102 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import ... | 103 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""... | 104 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 0 |
def __UpperCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int , lowerCamelCase_ : int = 0 , lowerCamelCase_ : int = 0 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = right or len(lowerCamelCas... | 105 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
... | 106 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 107 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 0 |
import os
import sys
__a: Union[str, Any] = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassi... | 108 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
_enforce_args(__UpperCAmelCase , __UpperCAmelCase )
if n == 0:
return 0
__SCREAMING_SNAKE_CASE = float("""-inf""" )
... | 109 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 0 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ,_snake_case ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path... | 110 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int ... | 79 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ = list[list[int]]
# assigning initial values to the grid
SCREAMING_SNAKE_CASE__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6,... | 631 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Ba... | 77 | 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 = inspect.getfile(accelerate.test_u... | 205 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torch_avai... | 481 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,... | 635 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 408 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 0 |
def snake_case (UpperCAmelCase__ ) -> str:
return " ".join(
''.join(word[::-1] ) if len(UpperCAmelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('Hey wollef sr... | 57 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import ... | 77 | 0 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def lowerCamelCase ( _snake_case : Tuple ,_snake_case : Optional[Any] ,_snake_case : List[str] ):
'''simple docstring'''
if a == 0:
raise ValueErro... | 267 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 434 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 0 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ... | 182 |
"""simple docstring"""
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 PegasusTokeni... | 77 | 0 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase_ : Dict = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def UpperCamelCase ( )-> str:... | 491 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import float... | 79 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 0 |
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Union[str, Any] = 0
SCREAMING_SNAKE_CASE_ :Tuple = len(a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right... | 631 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowercase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = AutoCo... | 205 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __A ( unittest.TestCase ):
'''simple docstring'''
def __lowerCamelCase ( self ):
'''simple ... | 481 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 0 |
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class UpperCamelCase ( __a ):... | 635 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 0 |
import re
from ..utils import cached_file
# docstyle-ignore
snake_case__ : Union[str, Any] = '\nHuman: <<task>>\n\nAssistant: '
snake_case__ : str = 'huggingface-tools/default-prompts'
snake_case__ : Optional[int] = {'chat': 'chat_prompt_template.txt', 'run': 'run_prom... | 408 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
fro... | 57 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUE... | 267 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->List[str]: # noqa: E741
while r - l > 1:
_lowerCamelCase : Dict = (l + r)... | 434 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 0 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def a__ ( lowerCAmelCase , lowerCAmelCase = 0.0 , lowerCAmelCase = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testm... | 182 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 0 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impo... | 491 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 79 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 0 |
def lowercase ( ):
'''simple docstring'''
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(a , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 631 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Ba... | 77 | 0 |
def lowercase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(SCREAMING_SNAKE_CASE ... | 205 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAme... | 481 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 0 |
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int ):
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("""String lengths must match!""" )
UpperCamelCase_ : str = 0
for char... | 635 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 0 |
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 transformers.utils import ... | 408 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 0 |
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
while a != 0:
UpperCamelCase_: str = b % a, a
return b
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
if gcd(UpperCAmelCase__ , Up... | 57 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import ... | 77 | 0 |
'''simple docstring'''
import re
def lowerCamelCase ( _snake_case : str ):
'''simple docstring'''
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" ,str_ )]
def lowerCamelCase ( _snake_case : Optional[int... | 267 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 0 |
"""simple docstring"""
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ = 1000 ) ->int:
_lowerCamelCase : List[Any] = 2**power
_lowerCamelCase : str = str(SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : Optional[int] = list(SCREAMING_SNAKE_CASE_ )
_lowerC... | 434 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase ( lowerCAmelCase__ , ... | 182 |
"""simple docstring"""
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 PegasusTokeni... | 77 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 491 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import... | 79 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 631 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM... | 205 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert im... | 481 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 0 |
from collections import namedtuple
SCREAMING_SNAKE_CASE : str = namedtuple("from_to", "from_ to")
SCREAMING_SNAKE_CASE : Tuple = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1000),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.00454, 264.172),
"cubicy... | 635 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 0 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension... | 408 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 0 |
def snake_case (UpperCAmelCase__ ) -> float:
UpperCamelCase_: List[Any] = 0
while len(UpperCAmelCase__ ) > 1:
UpperCamelCase_: Optional[int] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
UpperCam... | 57 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ ... | 267 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 0 |
"""simple docstring"""
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... | 434 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCamelCase ( yaml.SafeLoader ):
'''simple docstring'''
def _a (self , _lowerCamelCase ):
"""simple docstring"""
Upp... | 182 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 0 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as comput... | 491 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 0 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
SCREAMING_SNAKE_CASE__ ... | 79 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 0 |
def lowercase ( a = 10**12 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Tuple = 1
SCREAMING_SNAKE_CASE_ :List[Any] = 0
SCREAMING_SNAKE_CASE_ :str = 1
SCREAMING_SNAKE_CASE_ :Union[str, Any] = 1
while numerator <= 2 * min_t... | 631 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Ba... | 77 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
loggin... | 205 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__(... | 481 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 0 |
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
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : ... | 635 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
lo... | 408 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 0 |
def snake_case (UpperCAmelCase__ ) -> str:
UpperCamelCase_: List[str] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case (UpperCAmelCase__ ) -> dict[str, st... | 57 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import ... | 77 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def lowerCamelCase ( _snake_case : Tuple ,_snake_case : Dict ,_snake_case : Tuple ,_snake_case : Any = 100 ,):
'''simple docstring'''
lowercase... | 267 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=a_ )
class _UpperCAmelCase ( a_ ):
"""simple docstring"""
... | 434 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 0 |
"""simple docstring"""
import argparse
import os
import re
_A = """src/transformers"""
# Pattern that looks at the indentation in a line.
_A = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_A = re.compile(R"""^\s*\"([^\"]+)\":""")
# Pattern that matches... | 182 |
"""simple docstring"""
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 PegasusTokeni... | 77 | 0 |
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class UpperCamelCase ( _UpperCAmelCase ):
lowerCAmelCase : Optional[int] = ... | 491 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 0 |
def _lowerCamelCase ( __lowerCamelCase ) -> int:
'''simple docstring'''
UpperCAmelCase__ : list[list[int]] = [[0 for _ in range(__lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase__ : ... | 79 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import... | 631 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 205 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a = logging.get_logger(__name__)
_a = {
"facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convn... | 481 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 0 |
from math import factorial
class UpperCamelCase :
def __init__(self , __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
UpperCamelCase_ : Tuple = real
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
... | 635 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 0 |
from typing import Any
class _a :
"""simple docstring"""
def __init__( self , _snake_case ):
_UpperCAmelCase =data
_UpperCAmelCase =None
class _a :
"""simple docstring"""
def __init__( self ):
_UpperCA... | 408 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
... | 57 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 0 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : Optional[int] ):
'''simple docstring'''
if not isinstance(_snake_case ,_snake_case ):
raise ValueError("check_bouncy() accepts only integer arguments" )
lowercase__ ... | 267 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Dict ={
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig'... | 434 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE... | 182 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 0 |
def UpperCamelCase ( _A : int )-> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] +... | 491 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggin... | 79 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 0 |
def lowercase ( a , a = 0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[str] = length or len(a )
SCREAMING_SNAKE_CASE_ :List[str] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE_ :int ... | 631 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Ba... | 77 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
SCREAMING_SNAKE_CASE__ : int = logging.getLogger(__name__)
class a_ :
def __init__( self ) -... | 205 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "att... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertConfig",... | 481 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : Dict = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConf... | 635 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive co... | 77 | 0 |
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def lowerCamelCase__ ( _lowerCamelCase=None ) ->int:
if subparsers is not None:
_UpperCAmelCase =subparsers.add_parser("test" )
else:
_UpperCAmelCase =argparse.ArgumentParser("Accelerate test command" )... | 408 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : Optional[Any] = logging.get_logger(__name__)
class _lowerCAmelCase( UpperCAmelCase_ ):
... | 57 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import ... | 77 | 0 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : int ,_snake_case : int ):
'''simple docstring'''
lowercase__ = [[] for _ in range(_snake_case )]
lowercase__ = key - 1
if key <= 0:
raise ValueErr... | 267 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in ... | 77 | 0 |
"""simple docstring"""
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()
SCREAMING_SNAKE_CASE__ : Dict =logging.get_logger(__n... | 434 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )... | 77 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import ... | 182 |
"""simple docstring"""
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 PegasusTokeni... | 77 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
A... | 491 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 77 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vi... | 79 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str,... | 77 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab_fi... | 631 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = ... | 77 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_ava... | 205 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = str(id_ )
lowerCamelCase__ = ... | 481 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : Optional[int] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIV... | 635 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 77 | 0 |
import numpy as np
from PIL import Image
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) ->np.ndarray:
_UpperCAmelCase =np.array(_lowerCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
_UpperCAmelCase ... | 408 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
... | 77 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
A_ : str = 'sshleifer/ba... | 57 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 0 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transform... | 267 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 0 |
"""simple docstring"""
import sys
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->Tuple:
_lowerCamelCase : Optional[Any] = len(SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : str = [[0 for x in range(SCREAMING_SNAKE_CASE_ )] for x in range(SCREAMING_SNAKE_CASE_ )]
... | 434 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", ""... | 77 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : O... | 182 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 77 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-ki... | 491 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
SCREAMING_SNAKE_CASE__ : Optional[int] = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig ... | 79 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffuser... | 77 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UN... | 631 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Ba... | 77 | 0 |
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