code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
def _lowerCamelCase ( lowercase : list ) -> list:
if len(lowercase ) <= 1:
return [tuple(lowercase )]
_a = []
def generate(lowercase : int , lowercase : list ):
if k == 1:
... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/m... | 63 | 1 |
'''simple docstring'''
import numpy
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Any , __a : numpy.ndarray , __a : numpy.ndarray ):
_a = input_array
# Random initial weights are assign... | 63 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase_ : Any = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_ : List[str] = ... | 63 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_dev... | 63 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple ... | 63 | 1 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
... | 63 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
@register_to_con... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __lt__( self : int ,... | 63 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamel... | 63 | 1 |
'''simple docstring'''
from math import factorial
def _lowerCamelCase ( lowercase : int = 20 ) -> int:
_a = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_a = n // 2
return int(factorial(lowercase ) / (... | 63 |
'''simple docstring'''
import math
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : List[str] , __a : list[list[float]] , __a : list[int] ):
_a = 0.0
_a = 0.0
fo... | 63 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple ... | 63 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE (lowerCamelCa... | 63 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCa... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> list:
if n_term == "":
return []
_a = []
for temp in range(int(lowercase ) ):
series.append(F'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main_... | 63 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , __a : Optional[Any]=2 ... | 63 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase_ : List[s... | 63 | 1 |
'''simple docstring'''
import numpy as np
def _lowerCamelCase ( lowercase : np.ndarray ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def _lowerCamelCase ( lowercase : np.ndarray ) -> np.ndarray:
return vector * sigmoid(lowercase )
i... | 63 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 63 | 1 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTes... | 63 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _lowerCamelCase ( lowercase : Callable[[int | float], int | float] , lowercase : int | float , lowercase : int | float , lowerca... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
if num < 0:
return False
_a = num
_a = 0
while num > 0:
_a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main... | 63 | 1 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : int , __a : Union[str, Any] ):
# we need a list not a string, so do something to change the type
_a = arr.split("," )
de... | 63 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ : int = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT... | 63 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
_a = len(bin(lowercase )[3:] )
_a = bin(abs(lowercase ) - (1 << binary_number_length) )[3... | 63 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transf... | 63 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( lowercase : Optional[int] , lower... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacod... | 63 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Genera... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
_a = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 63 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : list[int] , lowercase : list[int] ) -> None:
_a = len(lowercase )
print("The following activities are selected:" )
# The first activity is always selected
_a = 0
print... | 63 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[int] = {
'ut/deta': 'https://huggingfa... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 63 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : int , lowerc... | 63 | 1 |
'''simple docstring'''
import math
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : List[str] , __a : list[list[float]] , __a : list[int] ):
_a = 0.0
_a = 0.0
fo... | 63 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : str ):
_a = [
"... | 63 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bar... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : bytes ) -> str:
return "".join([hex(lowercase )[2:].zfill(2 ).upper() for byte in list(lowercase )] )
def _lowerCamelCase ( lowercase : str ) -> bytes:
# Check data validity, fol... | 63 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> str:
return "".join(chr(ord(lowercase ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 63 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from ... | 63 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowerCAmelCase_ : Opt... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/m... | 63 | 1 |
'''simple docstring'''
from timeit import timeit
lowerCAmelCase_ : int = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our te... | 63 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase_ : Any = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_ : List[str] = ... | 63 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCAmelCase_ : Any = logging.get_... | 63 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple ... | 63 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowerCamelCase ( lowercase : Any , ... | 63 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
@register_to_con... | 63 | 1 |
'''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/LIC... | 63 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamel... | 63 | 1 |
'''simple docstring'''
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():
im... | 63 |
'''simple docstring'''
import math
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : List[str] , __a : list[list[float]] , __a : list[int] ):
_a = 0.0
_a = 0.0
fo... | 63 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__)
low... | 63 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE (lowerCamelCa... | 63 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_fu... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> list:
if n_term == "":
return []
_a = []
for temp in range(int(lowercase ) ):
series.append(F'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main_... | 63 | 1 |
'''simple docstring'''
import os
def _lowerCamelCase ( ) -> Tuple:
with open(os.path.dirname(lowercase ) + "/grid.txt" ) as f:
_a = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
... | 63 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase_ : List[s... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 63 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 63 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple do... | 63 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 63 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase_ : Tuple = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'toke... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
if num < 0:
return False
_a = num
_a = 0
while num > 0:
_a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main... | 63 | 1 |
'''simple docstring'''
import functools
def _lowerCamelCase ( lowercase : list[int] , lowercase : list[int] ) -> int:
# Validation
if not isinstance(lowercase , lowercase ) or not all(isinstance(lowercase , lowercase ) for day... | 63 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ : int = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT... | 63 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import i... | 63 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transf... | 63 | 1 |
'''simple docstring'''
import os
from distutils.util import strtobool
def _lowerCamelCase ( lowercase : str , lowercase : int ) -> str:
for e in env_keys:
_a = int(os.environ.get(lowercase , -1 ) )
if val >= 0:
... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacod... | 63 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase_ : Any = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
lowerCAmelCase_ : str = ... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
_a = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 63 | 1 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowerCAmelCase_ : List[Any] = 6_378_137.0
lowerCAmelCase_ : List[Any] = 6_356_752.314_245
lowerCAmelCase_ : int = 6_37_81_37
def _low... | 63 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[int] = {
'ut/deta': 'https://huggingfa... | 63 | 1 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE (pl.LightningModule ):
"""simple docstring"""
def __init__( self ... | 63 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : int , lowerc... | 63 | 1 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ : Tupl... | 63 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : str ):
_a = [
"... | 63 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ : Any = {'configuration_vit': ['VIT_PRETRAINED... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : bytes ) -> str:
return "".join([hex(lowercase )[2:].zfill(2 ).upper() for byte in list(lowercase )] )
def _lowerCamelCase ( lowercase : str ) -> bytes:
# Check data validity, fol... | 63 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalyti... | 63 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from ... | 63 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
Wav... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/m... | 63 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ... | 63 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase_ : Any = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_ : List[str] = ... | 63 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 63 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple ... | 63 | 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
lowerCAmelCase_ : Optional[Any] = get_logger(__name__)
lowerCAmelCase_ : Tuple = R'\n Args:\n... | 63 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
@register_to_con... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _lowerCamelCase ( lowercase : str = "" ) -> dict[str, float]:
_a = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
_a = BeautifulSo... | 63 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamel... | 63 | 1 |
'''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 loggi... | 63 |
'''simple docstring'''
import math
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : List[str] , __a : list[list[float]] , __a : list[int] ):
_a = 0.0
_a = 0.0
fo... | 63 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : List[str] ) -> List[str]:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: ... | 63 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE (lowerCamelCa... | 63 | 1 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowerCamelCase ( lowercase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == ... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> list:
if n_term == "":
return []
_a = []
for temp in range(int(lowercase ) ):
series.append(F'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main_... | 63 | 1 |
'''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 __SCREAMING_SNAKE_CASE (lowerCam... | 63 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase_ : List[s... | 63 | 1 |
'''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... | 63 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 63 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
... | 63 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 63 | 1 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version im... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
if num < 0:
return False
_a = num
_a = 0
while num > 0:
_a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main... | 63 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCAmelCase_ : Any = {
# 1536-bit
5: {
... | 63 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ : int = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT... | 63 | 1 |
'''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 diffu... | 63 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transf... | 63 | 1 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _lowerCamelCase ( lowercase : List[str] , lowercase : Optional[Any] , lowercase : Dict ) -> Optional[Any]:
... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacod... | 63 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ : Any = {'configuration_reformer': ['REFORMER_PRETRAIN... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
_a = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 63 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : list , lowercase : int , lowercase : int = 0 , lowercase : int = 0 ) -> int:
_a = right or len(lowercase ) - 1
if left > right:
return -1
elif... | 63 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[int] = {
'ut/deta': 'https://huggingfa... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 63 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : int , lowerc... | 63 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generatio... | 63 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : str ):
_a = [
"... | 63 | 1 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase_ : Dict = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk ... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : bytes ) -> str:
return "".join([hex(lowercase )[2:].zfill(2 ).upper() for byte in list(lowercase )] )
def _lowerCamelCase ( lowercase : str ) -> bytes:
# Check data validity, fol... | 63 | 1 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNo... | 63 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from ... | 63 | 1 |
'''simple docstring'''
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... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/m... | 63 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixi... | 63 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase_ : Any = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_ : List[str] = ... | 63 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, ... | 63 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple ... | 63 | 1 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
... | 63 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
@register_to_con... | 63 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : str = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-... | 63 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamel... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a =42
__a =42
def _lowerCamelCase ( lowercase : str ) -> list[s... | 63 |
'''simple docstring'''
import math
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : List[str] , __a : list[list[float]] , __a : list[int] ):
_a = 0.0
_a = 0.0
fo... | 63 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_f... | 63 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE (lowerCamelCa... | 63 | 1 |
'''simple docstring'''
import math
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = sum(i * i for i in range(1 , n + 1 ) )
_a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> list:
if n_term == "":
return []
_a = []
for temp in range(int(lowercase ) ):
series.append(F'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main_... | 63 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE (metaclass=lowerCamelCase_ ):
"""simple docstring"""
__a =['torch']
def __init__( self : Tuple , *__a : Tuple , **__a :... | 63 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase_ : List[s... | 63 | 1 |
'''simple docstring'''
from math import sqrt
def _lowerCamelCase ( lowercase : int ) -> bool:
assert isinstance(lowercase , lowercase ) and (
number >= 0
), "'number' must been an int and positive"
_a = True
# 0 and 1 are none primes.
... | 63 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 63 | 1 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from tr... | 63 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers impo... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 63 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : str = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple = {
'Salesforce/blip-vqa-base': 'https://... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
if num < 0:
return False
_a = num
_a = 0
while num > 0:
_a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main... | 63 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ : Dict = ... | 63 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ : int = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT... | 63 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacod... | 63 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transf... | 63 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacod... | 63 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
_a = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 63 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_m... | 63 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[int] = {
'ut/deta': 'https://huggingfa... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_... | 63 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : int , lowerc... | 63 | 1 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
cla... | 63 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : str ):
_a = [
"... | 63 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 200 ) -> int:
_a = [1, 2, 5, 10, 20, 50, 100, 200]
_a = [0] * (pence + 1)
_a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowercase , pence +... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : bytes ) -> str:
return "".join([hex(lowercase )[2:].zfill(2 ).upper() for byte in list(lowercase )] )
def _lowerCamelCase ( lowercase : str ) -> bytes:
# Check data validity, fol... | 63 | 1 |
'''simple docstring'''
import inspect
import unittest
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : Optional[Any] ):
try:
import diffusers # noqa: F401
except ImportError:
... | 63 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from ... | 63 | 1 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators impor... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/m... | 63 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowerCamelCase ( lowercase : Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
_a = []
... | 63 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase_ : Any = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_ : List[str] = ... | 63 | 1 |
'''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,
)
lowerCAmelCase_ : Union[str, Any] =... | 63 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple ... | 63 | 1 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passe... | 63 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
@register_to_con... | 63 | 1 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( lowercase : Any , lowercase : Tuple , lowe... | 63 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamel... | 63 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase_ : Union[str, Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_... | 63 |
'''simple docstring'''
import math
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : List[str] , __a : list[list[float]] , __a : list[int] ):
_a = 0.0
_a = 0.0
fo... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowercase : list[int] , lowercase : int ) -> bool:
if len(lowercase ) == 0:
return False
_a = len(lowercase ) // 2
if a_list[midpoint] == item:
... | 63 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE (lowerCamelCa... | 63 | 1 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, i... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> list:
if n_term == "":
return []
_a = []
for temp in range(int(lowercase ) ):
series.append(F'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main_... | 63 | 1 |
'''simple docstring'''
from functools import reduce
lowerCAmelCase_ : Optional[Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'125406987471585238630507156... | 63 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase_ : List[s... | 63 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ : Optional[Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE... | 63 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 63 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : float , lowercase : int ) -> float:
if digit_amount > 0:
return round(number - int(lowercase ) , lowercase )
return number - int(lowercase )
if __name__ == "__main__":
... | 63 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowercase : float , lowercase : float , lowercase : float , ) -> tuple[str, float]:
if (stress, tangential_force, area).count(0 ) != 1:
raise V... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 63 | 1 |
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