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 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
_lowerCamelCase = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/... | 144 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCAmelCase__ ( a , a ):
"""simple docstring"""
@register_to_config
de... | 627 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENER... | 601 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)... | 627 | 0 |
from string import ascii_uppercase
_SCREAMING_SNAKE_CASE = {str(ord(c) - 55): c for c in ascii_uppercase}
def snake_case ( snake_case__ :Dict , snake_case__ :Union[str, Any]) -> List[str]:
if isinstance(a__ , a__):
raise TypeError("""in... | 401 |
'''simple docstring'''
import math
def a__ ( a__ ):
"""simple docstring"""
return math.sqrt(a__ ) * math.sqrt(a__ ) == num
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = n
while left <= ... | 627 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[int] = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_... | 572 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase : Any = 'src/transformers'
# This is to make sure the transfo... | 627 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int... | 635 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
__SCREAM... | 627 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def __lowerCAmelCase ( A_ : str ) -> Optional[Any]:
if num <= 0:
raise ValueError("math domain error" )
return quad(a__ , 0 , a__ , args=(a__) )[0]
def __lowerCAmelCase ( ... | 221 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 627 | 0 |
class A :
def __init__(self : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = name
UpperCAmelCase__ = v... | 486 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def a__ ( a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = math.sqrt(a__ )
__SCREAMING_SNAKE_CASE = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img /... | 627 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_... | 278 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if v... | 627 | 0 |
def lowerCamelCase__ ( _A = 1000 ):
'''simple docstring'''
return sum(e for e in range(3 , a__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 376 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfi... | 627 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_ut... | 386 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Any = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main... | 627 | 0 |
import math
def _snake_case ( __snake_case ):
return math.sqrt(a__ ) * math.sqrt(a__ ) == num
def _snake_case ( __snake_case ):
_UpperCamelCase = 0
_UpperCamelCase = n
while left <= right:
_UpperCamelCase = (left + right) // ... | 10 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittes... | 627 | 0 |
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 import... | 144 |
'''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 lowerCAmelCase__ ( ... | 627 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
a__ : int = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
a__ : ... | 601 |
'''simple docstring'''
UpperCAmelCase : Tuple = range(2, 2_0 + 1)
UpperCAmelCase : int = [1_0**k for k in range(ks[-1] + 1)]
UpperCAmelCase : dict[int, dict[int, list[list[int]]]] = {}
def a__ ( a__ , a__ , a__ , a__ ):
"""simple docstri... | 627 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
... | 401 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 627 | 0 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowerCAmelCase ( lowerCamelCase__ : int, lowerCamelCase__ : Union[str, Any] ) -> str:
_SCREAMING_SNAKE_CASE : Tuple = int(a__ )
assert noofcluster... | 572 |
'''simple docstring'''
from math import isqrt
def a__ ( a__ ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(a__ ) + 1 ) )
def a__ ( a__ = 10**6 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
... | 627 | 0 |
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 import (
BitConfig,
... | 635 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : Any = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class lowerCAmelCase__ ( a ):
... | 627 | 0 |
def __lowerCAmelCase ( A_ : str ) -> Union[str, Any]:
return "".join([hex(a__ )[2:].zfill(2 ).upper() for byte in list(a__ )] )
def __lowerCAmelCase ( A_ : List[Any] ) -> Union[str, Any]:
if (len(a__ ) % 2) != 0:
raise ValueError(
... | 221 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 627 | 0 |
import numpy as np
class A :
def __init__(self : Any ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = (0, 0)
UpperCAmelCase__ = None
UpperCAmelCase__ = 0
UpperCAmelCase__ = 0
... | 486 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( a ):
"""simple docstring"""
lowerCAmelCase__ = ["image_processor", "tokenizer"]
lowerCAmelCase__ = "CLIPImageProcesso... | 627 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __lowerCamelCase ( A__ : Dict ) -> Tuple:
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
... | 278 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
fr... | 627 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
... | 376 |
'''simple docstring'''
# Copyright 2021 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-2.0
#
# ... | 627 | 0 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto impor... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
# Copyright 2022 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-2.0
#
# Unless required ... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE :Dict = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLT... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :str = {
"""junnyu... | 628 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthe... | 628 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :str = {
"""SenseTime/deformable-detr""": """https://huggingfac... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_M... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( sn... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
SCREAMING_SNAKE_CASE :Any = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = """\
@InProc... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
from collections import defaultdict
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> bool:
"""simple docstring"""
UpperCamelCase_ = first_str.lower().strip()
UpperCamelCase_ = second_str.lower().strip()
# R... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
import torch
from torch import nn
class __magic_name__ ( nn.Module ):
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=1 , _lowercase=False )-> str:
super().__init__()
... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> List[Any]:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
UpperCamelCase_ , UpperCamelCase_ = min(SCREAMING_SNAKE_CASE_ ... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = (n * (n + 1) // 2) ** 2
UpperCamelCase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
SCREAMING_SNAKE_CASE :Dict = {
"""facebook/maskformer-swin-base-ade... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( snake_case , unittest.TestCase ):
UpperCamelCase_ :An... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
from math import sqrt
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
UpperCamelCase_ = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE_CASE_ ... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
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 __magic_name__ ( snake_case ):
Upp... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
from maths.prime_factors import prime_factors
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = f"Input value of [number={number... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
from timeit import timeit
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCamelCase_ = 0
while number:
number &= numbe... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
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 import (
BitConfig,... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
class __magic_name__ ( snake_case ):
def __init__( self , *_lowercase , *... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
from __future__ import annotations
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )-> tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise Value... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE :List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ ( snake_case ):
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( _lowercase )-> Optional[Any]:
raise NotImplementedError()
... | 628 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthe... | 628 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {
"""facebook... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE :str = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokeni... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
import qiskit
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 2 )-> qiskit.result.counts.Counts:
"""simple docstring"""
UpperCamelCase_ = qubits
# Using Aer's simulator
UpperCamelCase_ = qiskit.Aer.get_backend("aer_simulator" )
#... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( snake_case , unittest.TestCase ):
UpperCamelCase_... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import Gradient... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
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_common impor... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
SCREAMING_SNAKE_CASE :Optional[Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_load... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logg... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards ... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
class __magic_name__ :
def __init__( self )-> Any:
UpperCamelCase_ = {}
def UpperCAmelCase_ ( self )-> None:
print(self.vertex )
for i in self.vertex:
print(_lowercase , " -> " , " -> ".j... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Dict = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHI... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = f"Input value of [number={number}] must be an integer"
raise TypeError(SCREAMI... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipel... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCAmelCase... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __magic_name__ ( unitt... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
fro... | 628 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthe... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
SCREAMING_SNAKE_CASE :Any = (
"""This metric will be removed from the library so... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration ... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] +=... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Optional[Any] = (DDIMParallelScheduler,)
UpperCamelCase_ :List[Any] = (("""eta... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE :List[Any] ... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
from __future__ import annotations
from random import choice
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
return choice(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CA... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list[int]:
"""simple docstring"""
if num <= 0:
UpperCamelCase_ = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(S... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transform... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 2_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0]
UpperCamelCase_ = 42
for idx in range(1 , ... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
import logging
from transformers import PretrainedConfig
SCREAMING_SNAKE_CASE :Optional[Any] = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __magic_name__ ( unittest.TestCase ):
def UpperCAmelCase_ ( self )-> Optional[int]:
UpperCamelCase_ = [
"safety_checker/pyto... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :int = {
"""shi-labs/d... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthe... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_util... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )
@pytest.mark.parametri... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_C... | 628 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthe... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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
... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokeniz... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {
"""vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
# S... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
from __future__ import annotations
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> list[int]:
"""simple docstring"""
UpperCamelCase_ = 0
UpperCamelCase_ = len(SCREAMING_SNAKE_CASE_ ) - 1
while i < j:
... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list[int]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = 2
UpperCamelCase_ = int(math.sqrt(SCREAMING_SNAKE_CASE_ ) ) # Size of every segment
Up... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
import argparse
SCREAMING_SNAKE_CASE :str = """docs/source/_static/js/custom.js"""
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> List[Any]:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , encoding="utf-8" , newline="\n" ) as... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutpu... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import test... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
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
from jax import jit
from ... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
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