code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class ... | 508 |
"""simple docstring"""
import numpy as np
import datasets
a_ = '\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 distan... | 76 | 0 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowercase( _lowerCamelCase ):
"""simple docstring"""
def snake_case ( self: int ,a: int=None ,a: Optional[int]=None ,a: Union[str, Any]=None ,**a: ... | 396 |
"""simple docstring"""
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __UpperCAmelCase ( __UpperCamelCase ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
... | 76 | 0 |
import math
def a_ ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 , SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 ):
'''simple docstring'''
_lowerCamelCase : List[Any] =end or len(__UpperCamelCase )
for... | 464 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
... | 76 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMod... | 115 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , ... | 76 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase = logging.get_logger(__name__)
class _lowercase ( __lowerCamelCase ):
def __init__( self : List[str] , *lowerCamelCase__ : Dict , **lowerCamelCase__ :... | 203 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a... | 76 | 0 |
from collections import deque
from .hash_table import HashTable
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,*A : str ,**A : Optional[int] ):
super().__init__(*UpperCamelCase_ ,**UpperCamel... | 55 |
"""simple docstring"""
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_ = logging.getLogger(__name__)
if... | 76 | 0 |
from __future__ import annotations
import math
def lowerCamelCase ( a_ ) -> Optional[int]:
if num <= 0:
lowerCAmelCase_ = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(__UpperCamelCase )
l... | 318 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 , __UpperCamelCase = 1 , __UpperCamelCase = 1.0e4 , __UpperCamelCase = False , __UpperCame... | 76 | 0 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
_lowerCAmelCase : Optional[Any] = "src/transformers"
# Matches is_xxx_available()
_lowerCAmelCase : List[Any] = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-li... | 289 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : Dict = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/... | 627 |
"""simple docstring"""
from math import pi, sqrt, tan
def __UpperCAmelCase ( __UpperCamelCase ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def __UpperCAmelCase ... | 76 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase_ : Optional[Any] = {
'''configuration_encodec''': [
'''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 185 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): # noqa: E741
while r - l > 1:
__lowercase : int = (l + r) // 2
if v[m] >= k... | 76 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_h... | 321 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase = 4 ):
__lowercase : Dict = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]... | 76 | 0 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeni... | 508 |
"""simple docstring"""
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 import BertTokenizer
a_ = lo... | 76 | 0 |
'''simple docstring'''
def __snake_case ( lowerCAmelCase : Dict , lowerCAmelCase : Optional[Any] ):
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
if dist[i][j] != floa... | 396 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , **UpperCam... | 76 | 0 |
def a_ ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
_lowerCamelCase : Optional[int] ... | 464 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCAmelCase ( __UpperCamelCase ):
# encod... | 76 | 0 |
"""simple docstring"""
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_util... | 115 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 76 | 0 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = len(__UpperCamelCase )
A_ = len(__UpperCamelCase )
A_ = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A_ ... | 203 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if digit_amount > 0:
return round(number - int(__UpperCamelCase ) , __UpperCamelCase )
return number - int(__UpperCamelCase )
if __name__ == "__main__":
print(decimal_isolate(1.... | 76 | 0 |
from __future__ import annotations
from typing import TypedDict
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring""... | 55 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__lowercase : set[int] = set()
return any(
node not in vis... | 76 | 0 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 318 |
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRI... | 76 | 0 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE__ : Dict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase : int = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups... | 289 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 76 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from trans... | 627 |
"""simple docstring"""
a_ = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'd... | 76 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 185 |
"""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/... | 76 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class snake_case ( __snake_case ):
"""simple docstring"""
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase... | 321 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class UpperCAmelCase_ :
def __init__( self ) -> str:
__lowercase : List[Any] = psutil.Process()
__lowercase : Any = False
def ... | 76 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __snake_case ( lowercase : Optional[int] ... | 508 |
"""simple docstring"""
import numpy as np
import datasets
a_ = '\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 distan... | 76 | 0 |
'''simple docstring'''
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,
)
_UpperCamelCase : Any ... | 396 |
"""simple docstring"""
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __UpperCAmelCase ( __UpperCamelCase ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
... | 76 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def a_ ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str] = 1 , SCREAMING_SNAKE_CASE__ : List[Any] = 1 , SCREAMING_SNAKE_CASE__ : str = ... | 464 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
... | 76 | 0 |
"""simple docstring"""
def A_ (__a , __a ):
'''simple docstring'''
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __nam... | 115 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , ... | 76 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
__lowercase = logging.get_logger(__name__)
class _lowercase ( __lowerCamelCase ):
def __init__( self : str , *lowerCamelCase__ : int , **lowerCamelCase__ : A... | 203 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a... | 76 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTR... | 55 |
"""simple docstring"""
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_ = logging.getLogger(__name__)
if... | 76 | 0 |
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=a_ ):
'''simple docstring'''
__a: Dict = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *lowercase_ , **lo... | 318 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 , __UpperCamelCase = 1 , __UpperCamelCase = 1.0e4 , __UpperCamelCase = False , __UpperCame... | 76 | 0 |
"""simple docstring"""
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCAmelCase : Any = abspath(join(dirnam... | 289 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase : Union[str, Any] = 'docs/source/en/_toctree.yml'
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = defaultdict(__UpperCamelCase )
__SCREAMIN... | 627 |
"""simple docstring"""
from math import pi, sqrt, tan
def __UpperCAmelCase ( __UpperCamelCase ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def __UpperCAmelCase ... | 76 | 0 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase_ : Any = {
'''huggingface/autoformer-tourism-monthly''... | 185 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): # noqa: E741
while r - l > 1:
__lowercase : int = (l + r) // 2
if v[m] >= k... | 76 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=__snake_case ):
"""simple docstring"""
__lowerCAmelCase = ["""note_seq"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
requires_backends(self , ["note_seq"] ... | 321 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase = 4 ):
__lowercase : Dict = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]... | 76 | 0 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 508 |
"""simple docstring"""
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 import BertTokenizer
a_ = lo... | 76 | 0 |
'''simple docstring'''
def __snake_case ( lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] ):
return int((input_a, input_a).count(0 ) == 0 )
def __snake_case ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_g... | 396 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , **UpperCam... | 76 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
f... | 464 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCAmelCase ( __UpperCamelCase ):
# encod... | 76 | 0 |
"""simple docstring"""
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class __lowerCAmelCase ( _lowercase ):
"""simple docstring"""
snake_case = "openai/whisper-base"
snake_case = (
"Th... | 115 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 76 | 0 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return " ".join(
''''''.join(word[::-1] ) if len(__UpperCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(r... | 203 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if digit_amount > 0:
return round(number - int(__UpperCamelCase ) , __UpperCamelCase )
return number - int(__UpperCamelCase )
if __name__ == "__main__":
print(decimal_isolate(1.... | 76 | 0 |
import fire
from utils import calculate_rouge, save_json
def UpperCAmelCase ( a_ , a_ , a_=None , **a_ ) -> Optional[int]:
"""simple docstring"""
__A = [x.strip() for x in open(__UpperCamelCase ).readlines()]
__A = [x.strip() for x i... | 55 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__lowercase : set[int] = set()
return any(
node not in vis... | 76 | 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 t... | 318 |
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRI... | 76 | 0 |
"""simple docstring"""
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
_lowerCAmelCase : i... | 289 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 76 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowerCAmelCase__ ( a ):
"""si... | 627 |
"""simple docstring"""
a_ = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'd... | 76 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( _UpperCamelCase: List[str] = 4 ) -> Union[str, Any]:
"""simple docstring"""
_snake_case = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase... | 185 |
"""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/... | 76 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 321 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class UpperCAmelCase_ :
def __init__( self ) -> str:
__lowercase : List[Any] = psutil.Process()
__lowercase : Any = False
def ... | 76 | 0 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowercase__ = yaml.safe_load(
'''\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsectio... | 508 |
"""simple docstring"""
import numpy as np
import datasets
a_ = '\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 distan... | 76 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_UpperCamelCase : Optional[int] = datasets.logging.get_logger(__name__)
_UpperCamelCase : Dict = '\\n@inproceedings{bleurt,\n title={BLEURT:... | 396 |
"""simple docstring"""
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __UpperCAmelCase ( __UpperCamelCase ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
... | 76 | 0 |
import math
def a_ ( SCREAMING_SNAKE_CASE__ : List[Any] = 100 ):
'''simple docstring'''
_lowerCamelCase : List[Any] =sum(i * i for i in range(1 , n + 1 ) )
_lowerCamelCase : Any =int(math.pow(sum(range(1 , n + 1 ) ) ... | 464 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
... | 76 | 0 |
"""simple docstring"""
from math import pi, sqrt, tan
def A_ (__a ):
'''simple docstring'''
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def A_ (__a , __a , __a ):
'''simpl... | 115 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , ... | 76 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _lowercase :
_lowercase : int = None
def UpperCamelCase ( self : int ) -> str:
"""simple docstring"""
A_ = ... | 203 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a... | 76 | 0 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :str = logging.get_logg... | 55 |
"""simple docstring"""
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_ = logging.getLogger(__name__)
if... | 76 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class a_ ( a_ ):
'''simple do... | 318 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 , __UpperCamelCase = 1 , __UpperCamelCase = 1.0e4 , __UpperCamelCase = False , __UpperCame... | 76 | 0 |
"""simple docstring"""
import qiskit
def __snake_case ( SCREAMING_SNAKE_CASE__ : int = 2 ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] = qubits
# Using Aer's simulator
_UpperCAmelCase : Any ... | 289 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Optional[int] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 627 |
"""simple docstring"""
from math import pi, sqrt, tan
def __UpperCAmelCase ( __UpperCamelCase ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def __UpperCAmelCase ... | 76 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : List[Any] = logging.get_logger(__name__)
UpperCamelCase_ : Dict = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/con... | 185 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): # noqa: E741
while r - l > 1:
__lowercase : int = (l + r) // 2
if v[m] >= k... | 76 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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_input... | 321 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase = 4 ):
__lowercase : Dict = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]... | 76 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def __snake_case ( lowercase : Any ):
snake_case_ = {key: len(__UpperCamelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCamelCase , __UpperCamelCase )}
if len(set(lists_lengths.values(... | 508 |
"""simple docstring"""
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 import BertTokenizer
a_ = lo... | 76 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 396 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , **UpperCam... | 76 | 0 |
lowerCamelCase = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'dataclasses',
'datasets'... | 464 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCAmelCase ( __UpperCamelCase ):
# encod... | 76 | 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=_lowercase )
class __lowerCAmelCase ( _lowercase ):
"""simple docstring"""
... | 115 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 76 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__lowercase = HfArgumentParser(InitializationArguments)
__lowercase = parser.parse_args()
# Load codeparrot tokenizer trained for Pyt... | 203 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if digit_amount > 0:
return round(number - int(__UpperCamelCase ) , __UpperCamelCase )
return number - int(__UpperCamelCase )
if __name__ == "__main__":
print(decimal_isolate(1.... | 76 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
__A = HfArgumentParser(__UpperCamelCase )
__A = parser.parse_args_into_dataclasses()[0]
__A ... | 55 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__lowercase : set[int] = set()
return any(
node not in vis... | 76 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],... | 318 |
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRI... | 76 | 0 |
"""simple docstring"""
import numpy as np
def __snake_case ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return np.where(vector > 0 , __UpperCamelCase , (alpha * (np.exp(__Upp... | 289 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 76 | 0 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCAmelCase : Optional[int] = TypeVar('KEY')
UpperCAmelCase : List[Any] = TypeVar('VAL')
@dataclass(frozen=a , slots=a )
class... | 627 |
"""simple docstring"""
a_ = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'd... | 76 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( _UpperCamelCase: Any , _UpperCamelCase: Any , _UpperCamelCase: str ) -> Dict:
"""simple docstring"""
_snake_case = list(range(len(__UpperCamelCase ) ) )
... | 185 |
"""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/... | 76 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import F... | 321 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class UpperCAmelCase_ :
def __init__( self ) -> str:
__lowercase : List[Any] = psutil.Process()
__lowercase : Any = False
def ... | 76 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 508 |
"""simple docstring"""
import numpy as np
import datasets
a_ = '\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 distan... | 76 | 0 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_w... | 396 |
"""simple docstring"""
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __UpperCAmelCase ( __UpperCamelCase ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
... | 76 | 0 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transfo... | 464 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ : Optional[int] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place ... | 115 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , ... | 76 | 0 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__lowercase = namedtuple(
"""_TestCommandArgs""",
[
"""dataset"... | 203 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a... | 76 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase ( a_ ) -> str:
"""simple docstri... | 55 |
"""simple docstring"""
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_ = logging.getLogger(__name__)
if... | 76 | 0 |
lowerCamelCase_ = tuple[float, float, float]
lowerCamelCase_ = tuple[float, float, float]
def lowerCamelCase ( a_ , a_ ) -> Union[str, Any]:
lowerCAmelCase_ = end_pointa[0] - end_pointa[0]
lowerCAmelCase_ = end_pointa[1] - end_pointa[1]
... | 318 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 , __UpperCamelCase = 1 , __UpperCamelCase = 1.0e4 , __UpperCamelCase = False , __UpperCame... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class UpperCAmelCase_ :
def __init__( self : Union[str, Any] ):
_UpperCAmelCase : Tuple = {}
def snake_case_ ( se... | 289 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 | 0 |
'''simple docstring'''
UpperCAmelCase : List[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = input("""Enter message: """ )
__SCREAMING_SNAKE_CASE = input("""Enter key [alphanumeric]: """ )
__SCREAMING_S... | 627 |
"""simple docstring"""
from math import pi, sqrt, tan
def __UpperCAmelCase ( __UpperCamelCase ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def __UpperCAmelCase ... | 76 | 0 |
'''simple docstring'''
class _a :
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Dict:
_snake_case = name
_snake_case = val
def __str__( self ) -> Any:
return f"""{self.__class__.__na... | 185 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): # noqa: E741
while r - l > 1:
__lowercase : int = (l + r) // 2
if v[m] >= k... | 76 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {}
class snake_case ( __snake_case ):
"""simple docstring"""
__lowerCAmelCase = """llama"""
__lowerCAmelCase ... | 321 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase = 4 ):
__lowercase : Dict = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]... | 76 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 508 |
"""simple docstring"""
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 import BertTokenizer
a_ = lo... | 76 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common ... | 396 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , **UpperCam... | 76 | 0 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokeniz... | 464 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCAmelCase ( __UpperCamelCase ):
# encod... | 76 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(UpperCamelCase ) , UpperCamelCase )
return number - int(UpperCamelCase )
i... | 77 |
"""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 | 1 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class a__ ( __magic_name__ ):
def __init__( self : Union[str, Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Any):
"""simple docstring"""
super().__init__()
... | 77 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import random
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : int | None = None):
"""simple docstring"""
__UpperCAmelCase : str = value
__UpperCAm... | 77 |
"""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 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__UpperCAmelCase... | 77 |
"""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 | 1 |
"""simple docstring"""
import os
import sys
import unittest
A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_objec... | 77 |
"""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 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _UpperCamelCase ( ) -> None:
"""simple docstring"""
print("Making key files..." )
make_key... | 77 |
"""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 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""C... | 77 |
"""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 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is... | 77 |
"""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 | 1 |
"""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 t... | 77 |
"""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 | 1 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common i... | 77 |
"""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 | 1 |
"""simple docstring"""
from string import ascii_lowercase, ascii_uppercase
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
if not sentence:
return ""
__UpperCAmelCase : List[str] = dict(zip(UpperCamelCase , ... | 77 |
"""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 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
def wrapper(*UpperCamelCase , ... | 77 |
"""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 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase = 6008_5147_5143 ) -> int:
"""simple docstring"""
try:
__UpperCAmelCase : Dict = int(UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be... | 77 |
"""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 | 1 |
"""simple docstring"""
import os
import sys
import unittest
A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_... | 77 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import... | 77 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
def __init__( self : Tuple , *UpperCamelCase_ : Any , **UpperCamel... | 77 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
A = [8, 5, 9, 7]
A = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0]... | 77 |
"""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 | 1 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
A = get_logger(__name__)
A = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_le... | 77 |
"""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 | 1 |
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