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"""simple docstring""" import logging import os import sys from pathlib import Path from unittest.mock import patch from parameterized import parameterized from run_eval import run_generate from run_eval_search import run_search from transformers.testing_utils import CaptureStdout, TestCasePlus, slow from u...
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import math def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ): if 0 not in (x, y): # We use the relation x^y = y*log10(x), where 10 is the base. return y * math.logaa(lowerCamelCase__ ) else: if x == 0: # 0 raised to any number is 0 return 0 ...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available __A : List[Any] = { '''configuration_groupvit''': [ '''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GroupViTConfig''', '''GroupViTO...
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from __future__ import annotations from typing import Any class __A : def __init__( self : Optional[Any] , UpperCAmelCase_ : int ): lowerCAmelCase : Tuple = num_of_nodes lowerCAmelCase : list[list[int]] = [] ...
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"""simple docstring""" import numpy as np def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> np.ndarray: return np.where(vector > 0 , lowercase_ , (alpha * (np.exp(lowercase_ ) - 1)) ) if __name__ == "__main__": import doctest doctest.testmod() ...
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"""simple docstring""" def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]: if length <= 0 or not isinstance(lowercase_ , lowercase_ ): raise ValueError("Length must be a positive integer." ) return [n * (2 * n - 1) for n in range(lowercase_ )] if __name__ == "__ma...
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'''simple docstring''' from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...utils import logging ...
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'''simple docstring''' def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ): lowercase__ : Dict = len(UpperCAmelCase ) print('''The following activities are selected:''' ) # The first activity is always selected lowercase__ : str = 0 print(UpperCAmelCase , ...
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# 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-2.0 # # Unless r...
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"""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 ...
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'''simple docstring''' import inspect import os import unittest from pathlib import Path import torch import accelerate from accelerate.test_utils import execute_subprocess_async from accelerate.test_utils.testing import run_command class _A ( unittest.TestCase ): _SCREAMING_...
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'''simple docstring''' from __future__ import annotations from typing import Any class _A : def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 ) -> None: '''simple docstring''' __UpperCAmelCase , __UpperCAmelC...
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _A = { """configuration_groupvit""": [ """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GroupViTConfig""", """...
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"""simple docstring""" from string import ascii_uppercase _A = {str(ord(c) - 5_5): c for c in ascii_uppercase} def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> str: if isinstance(__UpperCAmelCase , __UpperCAmelCase ): raise TypeEr...
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'''simple docstring''' def UpperCAmelCase ( lowerCamelCase_ :list ): '''simple docstring''' if len(lowerCamelCase_ ) <= 1: return lst snake_case_ : Union[str, Any] = 1 while i < len(lowerCamelCase_ ): if lst[i - 1] <= lst[i]: i += 1 else: ...
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'''simple docstring''' import functools def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ): '''simple docstring''' snake_case_ : List[str] = len(lowerCamelCase_ ) snake_case_ : Dict = len(lowerCamelCase_ ) @funct...
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import argparse import os import gluonnlp as nlp import mxnet as mx import numpy as np import torch from gluonnlp.base import get_home_dir from gluonnlp.model.bert import BERTEncoder from gluonnlp.model.utils import _load_vocab from gluonnlp.vocab import Vocab from packaging import version from torch import nn from t...
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import tempfile import unittest import numpy as np import transformers from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from...
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"""simple docstring""" import pytest import datasets.config from datasets.utils.info_utils import is_small_dataset @pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] ) @pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**2...
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"""simple docstring""" from math import factorial, radians def _lowerCAmelCase ( lowercase_ , lowercase_ = 18 , lowercase_ = 10 ): UpperCAmelCase = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0) # Converting from degrees to radians Up...
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import gc import unittest import numpy as np import torch from diffusers import ( AudioDiffusionPipeline, AutoencoderKL, DDIMScheduler, DDPMScheduler, DiffusionPipeline, Mel, UNetaDConditionModel, UNetaDModel, ) from diffusers.utils import slow, torch_device from diffusers.utils.test...
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"""simple docstring""" from decimal import Decimal, getcontext from math import ceil, factorial def __lowerCamelCase ( __UpperCamelCase ) -> str: """simple docstring""" if not isinstance(__UpperCamelCase , __UpperCamelCase ): raise TypeError("Undefined for non-...
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"""simple docstring""" import math import random from typing import Any from .hill_climbing import SearchProblem def __a ( __lowerCamelCase, __lowerCamelCase = True, __lowerCamelCase = math.inf, __lowerCamelCase = -math.inf, __lowerCamelCase = math.inf, __lowerCamelCase = -math.inf, __lowe...
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"""simple docstring""" 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 _a = ( 'This metric will be removed from the library soon, met...
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"""simple docstring""" def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : list ): _enforce_args(UpperCamelCase__ , UpperCamelCase__ ) if n == 0: return 0 _UpperCAmelCase : List[str] = float('''-inf''' ) for i i...
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"""simple docstring""" import math from numpy import inf from scipy.integrate import quad def lowerCamelCase_ (UpperCamelCase__ : float ): if num <= 0: raise ValueError('''math domain error''' ) return quad(UpperCamelCase__ , 0 , UpperCamelCase__ , ...
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def __lowerCamelCase ( __a :int = 1_0_0_0 ) -> int: """simple docstring""" A__ = -1 A__ = 0 for a in range(1 , n // 3 ): # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c A__ = (n * n - 2...
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import math def __lowerCamelCase ( ) -> None: """simple docstring""" A__ = input("""Enter message: """ ) A__ = int(input(F'Enter key [2-{len(__a ) - 1}]: ' ) ) A__ = input("""Encryption/Decryption [e/d]: """ ...
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import pytest import requests from datasets.utils.file_utils import http_head from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline @pytest.mark.integration def A_ ( ) -> List[Any]: with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ): with pytest.rais...
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from sklearn.metrics import fa_score import datasets __lowerCamelCase : List[Any] = """ The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation: F1 = 2 * (precision * recall) / (precision + recall) """ __lowerCamelCase : List[Any] = ...
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import gc import importlib.metadata import tempfile import unittest from packaging import version from transformers import ( AutoModel, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig, pipeline, ) from transformers.testi...
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import inspect from typing import List, Optional, Tuple, Union import numpy as np import PIL import torch import torch.utils.checkpoint from ...models import UNetaDModel, VQModel from ...schedulers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteSc...
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import numpy as np from sklearn.datasets import fetch_california_housing from sklearn.metrics import mean_absolute_error, mean_squared_error from sklearn.model_selection import train_test_split from xgboost import XGBRegressor def A_ ( A__ ) -> tuple: return (data["data"], data["target"]) def A...
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"""simple docstring""" from typing import Dict, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image from ...image_utils import ( ...
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"""simple docstring""" def lowerCamelCase (a_ :str) -> list[int]: lowercase :List[Any] = [0 for i in range(len(a_))] # initialize interval's left pointer and right pointer lowercase :str = 0, 0 for i in range(1 , len(a_)): ...
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"""simple docstring""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging if TYPE_CHECKING: from ... import FeatureExtractionMixin, TensorType UpperCAmelCa...
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import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging __lowerCAmelCase : Optional[Any] ='▁' __lowerCAmelCase : Union[str, Any] ={'vocab_file': 'spiece.mode...
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from collections import Counter import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split __lowerCAmelCase : List[Any] =datasets.load_iris() __lowerCAmelCase : Tuple =np.array(data['data']) __lowerCAmelCase : Dict =np.array(data['target']) __lowerCAmelCase...
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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 TokenizerTesterMix...
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import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import...
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"""simple docstring""" from math import sqrt def UpperCamelCase ( _lowerCAmelCase : Dict ) -> bool: assert isinstance(_lowerCAmelCase, _lowerCAmelCase ) and ( number >= 0 ), "'number' must been an int and positive" _UpperCAmelCase : List[str] = Tru...
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import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, ) ...
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from ...configuration_utils import PretrainedConfig from ...utils import logging A : Dict = logging.get_logger(__name__) A : Dict = { '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''', # See all PEGASUS models at https://h...
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging A : Tuple = logging.get_logger(__name__) A : Optional[int] = { '''roberta-base''': '''https://huggin...
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import os import re import warnings from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_ta import TaTokenizer else: ...
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import warnings from functools import wraps from typing import Callable def UpperCAmelCase ( lowercase ): """simple docstring""" @wraps(lowercase ) def _inner_fn(*lowercase , **lowercase ): warnings.warn( (F"'{fn.__name__}' is experime...
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def a_ ( __lowercase : int ) -> bool: return str(__lowercase ) == str(__lowercase )[::-1] def a_ ( __lowercase : int ) -> int: return int(__lowercase ) + int(str(__lowercase )[::-1] ) def a_ ( __lowercase : int = 10_000 ) -> int: _snake_...
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def a_ ( __lowercase : List[Any] ) -> Tuple: _snake_case = len(__lowercase ) for i in range(length - 1 ): _snake_case = i for k in range(i + 1 , __lowercase ): if collection[k] < collection[least]: _snake_case = k ...
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from ...configuration_utils import PretrainedConfig from ...utils import logging _lowerCamelCase : int = logging.get_logger(__name__) _lowerCamelCase : Optional[int] = { "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config....
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import torch from diffusers import DDPMParallelScheduler from .test_schedulers import SchedulerCommonTest class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ): '''simple docstring''' lowercase_ = (DDPMParallelScheduler,) def SCREAMING_SNAKE_CASE_ (self : Any ,...
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available, ) lowerCAmelCase__ = { '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG...
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'''simple docstring''' lowerCAmelCase__ = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ''' def _A ( ): """simple docstring""" __lowercase = input('''Enter message: ''' ) __lowercase = input('''Enter key [alphanumeric]: ''' ) __lowercase = input('''Encrypt/Dec...
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def __UpperCAmelCase ( __a : List[str] ) -> Optional[Any]: """simple docstring""" if isinstance(_UpperCAmelCase ,_UpperCAmelCase ): raise TypeError('''\'float\' object cannot be interpreted as an integer''' ) if isinstance(_UpperCAmelCase ,_UpperCAmelCase ): ...
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"""simple docstring""" from copy import deepcopy class _UpperCAmelCase : '''simple docstring''' def __init__( self , snake_case_ = None , snake_case_ = None ): """simple docstring""" if arr is None and size is not None: A_ : Union[st...
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from pickle import UnpicklingError import jax import jax.numpy as jnp import numpy as np from flax.serialization import from_bytes from flax.traverse_util import flatten_dict from ..utils import logging snake_case : Tuple = logging.get_logger(__name__) def lowerCAmelCase_ ( _snake_case ...
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from .integrations import ( is_optuna_available, is_ray_available, is_sigopt_available, is_wandb_available, run_hp_search_optuna, run_hp_search_ray, run_hp_search_sigopt, run_hp_search_wandb, ) from .trainer_utils import ( HPSearchBackend, default_hp_space_optuna, default_hp...
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'''simple docstring''' import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( BertTokenizer, ViltConfig, ViltForImageAndTextRetrieval, ViltForImagesAndTextClassification, Vilt...
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'''simple docstring''' import unittest from transformers import PegasusTokenizer, PegasusTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow from transformers.utils import cached_property from ...test_tokenization_common import Tokenize...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) A : Optional[Any] = { '''configuration_blip''': [ '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''', ...
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from typing import List, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging A : Optional[Any] = logging.get_logger(__name__) A : List[str] = { '''huggingface/time-series-transformer-tourism-monthly''': ( '''https://hugging...
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'''simple docstring''' import argparse import os import sys from unittest.mock import patch import pytorch_lightning as pl import timeout_decorator import torch from distillation import SummarizationDistiller, distill_main from finetune import SummarizationModule, main from transformers import MarianMTModel f...
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'''simple docstring''' def lowerCAmelCase_ ( _lowerCamelCase: int ): __SCREAMING_SNAKE_CASE : str = int(_lowerCamelCase ) if n_element < 1: __SCREAMING_SNAKE_CASE : List[str] = ValueError("""a should be a positive number""" ) raise my_error ...
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def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[Any]: """simple docstring""" snake_case_ : Optional[Any] = int(_UpperCamelCase ) if n_element < 1: snake_case_ : List[str] = ValueError('''a should be a positive number''' )...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) lowerCAmelCase_ = {'''configuration_xglm''': ['''XGLM_PRETRAINED_...
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"""simple docstring""" from importlib import import_module from .logging import get_logger lowercase_ = get_logger(__name__) class __lowerCAmelCase : '''simple docstring''' def __init__( self , _a , _a=None ): __a = attrs or [] ...
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"""simple docstring""" from __future__ import annotations import unittest from transformers import EsmConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin,...
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) lowerCAmelCase :int = { '''configuration_speech_to_text'''...
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'''simple docstring''' from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel...
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'''simple docstring''' import torch from diffusers import EulerDiscreteScheduler from diffusers.utils import torch_device from .test_schedulers import SchedulerCommonTest class _snake_case ( _a ): _A : Optional[Any] = (EulerDiscreteScheduler,) _A : ...
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'''simple docstring''' from typing import Callable, List, Optional, Union import PIL import torch from transformers import ( CLIPImageProcessor, CLIPSegForImageSegmentation, CLIPSegProcessor, CLIPTextModel, CLIPTokenizer, ) from diffusers import DiffusionPipeline from diffusers...
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"""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...
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"""simple docstring""" from __future__ import annotations from fractions import Fraction from math import gcd, sqrt def lowercase ( _SCREAMING_SNAKE_CASE : int ): '''simple docstring''' _UpperCAmelCase = int(number**0.5 ) return n...
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"""simple docstring""" def A_ ( _lowerCAmelCase : int = 50_00_00_00 ): """simple docstring""" _a = set() _a = int((limit - 24) ** (1 / 2) ) _a = set(range(3, prime_square_limit + 1, 2 ) ) primes.add(2 ) for p in range(3, ...
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"""simple docstring""" def A_ ( _lowerCAmelCase : int = 50 ): """simple docstring""" _a = [1] * (length + 1) for row_length in range(3, length + 1 ): for block_length in range(3, row_length + 1 ): for block_start in range(row_...
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from collections.abc import Generator from math import sin def lowerCAmelCase_ ( __a ) -> bytes: """simple docstring""" if len(__a ) != 32: raise ValueError("Input must be of length 32" ) lowerCamelCase__: Optional[Any] =b"" for i in [3, 2, 1, 0]: little_...
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from math import pow def lowerCAmelCase_ ( __a , __a , __a , __a , __a , ) -> tuple[int, int]: """simple docstring""" if current_sum == needed_sum: # If the sum of the powers is equal to needed_sum, then we have a solution. solutions_count += ...
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"""simple docstring""" import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...utils import logging SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) SCREAMING_SNAKE_CASE__ = { "microsoft/git-base": "https://huggingface.co/microsoft/g...
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def _lowercase ( lowercase__ , lowercase__ ): if density <= 0: raise ValueError('''Impossible fluid density''' ) if bulk_modulus <= 0: raise ValueError('''Impossible bulk modulus''' ) return (bulk_modulus / density) ** 0.5 if __name__ == "__main__":...
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"""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,...
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"""simple docstring""" import unittest from transformers import LiltConfig, 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 i...
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"""simple docstring""" import argparse import json import requests import timm import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification def _A ( lowercase ): ...
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"""simple docstring""" from transformers import DistilBertTokenizer, DistilBertTokenizerFast from transformers.testing_utils import require_tokenizers, slow from ..bert.test_tokenization_bert import BertTokenizationTest @require_tokenizers class __A ( _SCREAMING_SNA...
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"""simple docstring""" from __future__ import annotations def lowercase ( _SCREAMING_SNAKE_CASE : list ): '''simple docstring''' if not nums: raise ValueError('''List is empty''' ) return sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE...
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"""simple docstring""" def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ): '''simple docstring''' return "\n".join( f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) ) if __name__ == "...
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import gc import random import tempfile import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel from diffusers.pipelines.stable_diffusion_saf...
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import unittest from .lib import ( Matrix, Vector, axpy, square_zero_matrix, unit_basis_vector, zero_vector, ) class __lowerCAmelCase ( unittest.TestCase ): def UpperCamelCase ( self : int ): """simple docstring""" _UpperC...
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'''simple docstring''' import logging import os import sys import warnings from dataclasses import dataclass, field from random import randint from typing import Optional import datasets import evaluate import numpy as np from datasets import DatasetDict, load_dataset import transformers from transformers import ...
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'''simple docstring''' import argparse import torch from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert from transformers.utils import logging logging.set_verbosity_info() def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ): # Initialise...
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'''simple docstring''' import importlib import os import sys # This is required to make the module import works (when the python process is running from the root of the repo) sys.path.append('.') def lowerCAmelCase_ ( snake_case_ : Union[str, Any] ) -> int: '''simple docstring...
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'''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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor from transformers.utils import ...
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import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import tor...
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging __UpperCAmelCase = logging.get_logger(__name__) __UpperCAmelCase = { '''facebook/data2vec-text-base''': '''https://hugg...
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'''simple docstring''' from __future__ import annotations import os from collections.abc import Mapping __snake_case =tuple[int, int] class UpperCAmelCase_ : def __init__( self : Optional[int] , UpperCAmelCase__ : set[int] , ...
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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, ) from transformers.model...
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import argparse import json import logging import os import shutil import sys import tempfile import unittest from unittest import mock import torch from accelerate.utils import write_basic_config from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device ...
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"""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/li...
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'''simple docstring''' from __future__ import annotations import string from itertools import cycle, product from pathlib import Path a__ : str = ( string.ascii_letters + string.digits + string.punctuation + string.whitespace ) a__ : list[int] = [ord(letter) for lette...
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'''simple docstring''' import json import os import unittest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.testing_utils import require_ftfy, require_tokenizers from ...test_tokenization_common import Toke...
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import torch from diffusers import StableDiffusionPipeline lowerCAmelCase_ = 'path-to-your-trained-model' lowerCAmelCase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda') lowerCAmelCase_ = 'A photo of sks dog in a bucket' lowerCAmelCas...
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import os from pathlib import Path from unittest.mock import patch import pytest import zstandard as zstd from datasets.download.download_config import DownloadConfig from datasets.utils.file_utils import ( OfflineModeIsEnabled, cached_path, fsspec_get, fsspec_head, ftp_get,...
116
1
'''simple docstring''' import json import os from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer from ...utils import logging lowercase : List[str] ...
42
from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _A = { '''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''], } try: if not is_torch_available(): raise Opt...
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from __future__ import annotations from collections.abc import Iterator from typing import Any class A__ : def __init__( self : Any , a : Any ): '''simple docstring''' lowerCAmelCase__ : Any ...
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import gc import random import unittest import numpy as np import torch from transformers import ( CLIPImageProcessor, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection, ) from diffusers import ( DiffusionPipeline,...
307
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'''simple docstring''' import numpy as np from cva import COLOR_BGR2GRAY, cvtColor, imread from numpy import array, uinta from PIL import Image from digital_image_processing import change_contrast as cc from digital_image_processing import convert_to_negative as cn from digital_image_processing import sepia as sp...
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import random def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> tuple: lowerCamelCase , lowerCamelCase , lowerCamelCase : Any = [], [], [] for element in data: if element < pivot: less.append(_SCREAMING_S...
48
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'''simple docstring''' import argparse import requests import torch from PIL import Image from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor def lowercase__( __UpperCamelCase: List[Any] ): """simple docstring""" if "c...
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'''simple docstring''' import copy import unittest from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common ...
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import os import tempfile import unittest from transformers import FlaubertConfig, is_torch_available from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra...
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# 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 # # Unless required by applica...
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import doctest import glob import importlib import inspect import os import re from contextlib import contextmanager from functools import wraps from unittest.mock import patch import numpy as np import pytest from absl.testing import parameterized import datasets from datasets import load_metric from .utils i...
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def lowerCamelCase__ ( ) -> int: '''simple docstring''' return 1 def lowerCamelCase__ ( UpperCamelCase__ : int ) -> int: '''simple docstring''' return 0 if x < 0 else two_pence(x - 2 ) + one_pence() def lowerCamelCase__ ...
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from __future__ import annotations from math import pi, sqrt def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : float ): """simple docstring""" if inductance <= 0: raise ValueError("Inductance cannot be 0 or negative" ) elif capacitance <= 0: raise ValueErro...
18
"""simple docstring""" from collections import defaultdict from typing import Optional from ..image_utils import load_image from ..utils import ( add_end_docstrings, is_torch_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, ChunkPipeline if is_torch_available(...
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from __future__ import annotations from math import pi from typing import Protocol import matplotlib.pyplot as plt import numpy as np class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ): '''simple docstring''' def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE ) -> ...
36
from typing import TYPE_CHECKING from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available from ...utils import OptionalDependencyNotAvailable lowercase : Any = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']} ...
36
1
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__ : int ={ '''facebook/xmod-bas...
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from __future__ import annotations import unittest from transformers import AutoTokenizer, MBartConfig, is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property from ...test_configuration_common import Config...
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"""simple docstring""" def __lowerCAmelCase ( lowercase : float , lowercase : list[float] ) -> float: """simple docstring""" if discount_rate < 0: raise ValueError("Discount rate cannot be negative" ) if not cash_flows: raise ValueError("C...
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"""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, ENV_VAR...
112
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"""simple docstring""" 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...
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'''simple docstring''' import os import re import warnings from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_ta import Ta...
2
0
import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): from transformers import AutoModelForSeqaSeqLM, AutoTokenizer @require_torch @require_sentencepiece @require...
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def __lowercase ( a__ = 10_00 ) -> int: __SCREAMING_SNAKE_CASE = -1 __SCREAMING_SNAKE_CASE = 0 for a in range(1 , n // 3 ): # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c __SCREAMING_SNAK...
118
1
"""simple docstring""" import unittest from parameterized import parameterized from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin ...
64
def __UpperCamelCase ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ): __a : str = len(lowerCAmelCase__ ) __a : Optional[int] = [] for i in range(len(lowerCAmelCase__ ) - pat_len + 1 ): __a : str = True for j in range(lowerCAmelCase__ ): if s[i + j]...
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from typing import List, Optional from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-mont...
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from .dependency_versions_table import deps from .utils.versions import require_version, require_version_core # define which module versions we always want to check at run time # (usually the ones defined in `install_requires` in setup.py) # # order specific notes: # - tqdm must be checked before tokenizers Upper...
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'''simple docstring''' from __future__ import annotations def __snake_case( _lowerCAmelCase ) -> None: create_state_space_tree(_lowerCAmelCase , [] , 0 , [0 for i in range(len(_lowerCAmelCase ) )] ) def __snake_case( _lowerCAmelCase , _lowerCAmelCa...
35
import argparse import os import pickle import sys import torch from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils from transformers.models.transfo_xl.tokenization_transfo_xl import...
205
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"""simple docstring""" import os import unittest from tempfile import TemporaryDirectory import torch import torch.nn as nn from accelerate.utils import ( OffloadedWeightsLoader, extract_submodules_state_dict, load_offloaded_weight, offload_state_dict, offload_weight, ) class snake...
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"""simple docstring""" def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int: """simple docstring""" if n == 0: return 1 elif n % 2 == 1: return (binary_exponentiation(UpperCAmelCase , n - 1 , UpperCAmelCase ) * a) % mod else: ...
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import json import unittest import numpy as np from huggingface_hub import hf_hub_download 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...
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import argparse snake_case : int = '''docs/source/_static/js/custom.js''' def __lowercase ( __lowerCAmelCase : Optional[Any] ): with open(__lowerCAmelCase , encoding='utf-8' , newline='\n' ) as f: a__ = f.readlin...
240
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'''simple docstring''' import sys import webbrowser import requests from bsa import BeautifulSoup from fake_useragent import UserAgent if __name__ == "__main__": print('Googling.....') a__ : Union[str, Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:...
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'''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/lice...
243
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"""simple docstring""" from collections.abc import Callable class __lowerCAmelCase : def __init__( self , __UpperCAmelCase = None ): '''simple docstring''' __UpperCamelCase = [] # Stores indexes of each item for supporting updates and deletion. __UpperCamel...
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"""simple docstring""" from __future__ import annotations import numpy as np def UpperCAmelCase__ ( lowerCAmelCase__ :list[float] ) -> Optional[Any]: '''simple docstring''' return np.maximum(0 , lowerCAmelCase__ ) if __name__ == "__main__": p...
197
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"""simple docstring""" import os def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]: '''simple docstring''' _A = len(grid[0] ) _A = len(_snake_case ) _A = 0 _A = 0 _...
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"""simple docstring""" from typing import Any, Dict, List, Optional, Tuple, Union import torch from torch import nn from torch.utils.data import DistributedSampler, RandomSampler from transformers import PreTrainedModel, Trainer, logging from transformers.integrations import is_fairscale_available from transfor...
271
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'''simple docstring''' import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Audio, ClassLabel, Features from .base import TaskTemplate @dataclass(frozen=snake_case__ ) class lowerCamelCase_ (snake_case__ ): '''simple docstring''' __Upper...
31
'''simple docstring''' from sklearn.metrics import fa_score, matthews_corrcoef import datasets from .record_evaluation import evaluate as evaluate_record _lowerCAmelCase = '''\ @article{wang2019superglue, title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems...
37
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"""simple docstring""" from collections import defaultdict from pathlib import Path import pandas as pd from rouge_cli import calculate_rouge_path from utils import calculate_rouge UpperCamelCase_ =[ """Prosecutor: \"No videos were used in the crash investigation\" German ...
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"""simple docstring""" import numpy as np import torch from imwatermark import WatermarkEncoder # Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66 UpperCamelCase_ =0b1_0_1_1_0_0_1_1_1_1_...
128
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging _lowercase : List[Any] = logging.get_logger(__name__) _lowercase : List[str] = { 'caidas/swin2sr-classicalsr-x2-64': ( 'https://huggingface.co/caidas/swin...
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"""simple docstring""" from __future__ import annotations from collections import deque from collections.abc import Sequence from dataclasses import dataclass from typing import Any @dataclass class _UpperCAmelCase : a__ : int a__ : Node | None = None a_...
332
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'''simple docstring''' # flake8: noqa # Lint as: python3 from typing import Dict, List, Optional, Type from .. import config from ..utils import logging from .formatting import ( ArrowFormatter, CustomFormatter, Formatter, PandasFormatter, PythonFormatter, TensorFormatter, for...
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'''simple docstring''' import collections import json import math import os import re import time from fnmatch import fnmatch from typing import Dict import requests from slack_sdk import WebClient lowerCAmelCase_ : Tuple = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN']) def _l...
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"""simple docstring""" import unittest import numpy as np from transformers.testing_utils import is_flaky, require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, p...
183
"""simple docstring""" from math import pow, sqrt def lowerCamelCase__ ( *_lowerCamelCase : float ) -> bool: lowerCamelCase_ = len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values ) return result def ...
183
1
"""simple docstring""" import unittest from transformers import MPNetConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor...
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"""simple docstring""" def __A ( a_ :int) -> Union[str, Any]: __a : int = [] __a : Dict = [] __a : str = { '''^''': 3, '''*''': 2, '''/''': 2, '''%''': 2, '''+''': 1, ...
188
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'''simple docstring''' import importlib.metadata import warnings from copy import deepcopy from packaging import version from ..utils import logging from .import_utils import is_accelerate_available, is_bitsandbytes_available if is_bitsandbytes_available(): import bitsandbytes as bnb ...
3
'''simple docstring''' from collections import deque from math import floor from random import random from time import time class lowerCAmelCase__ : def __init__( self : Union[str, Any] ) ->Union[str, Any]: '''simple docstring''' ...
234
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"""simple docstring""" def __a ( lowerCAmelCase_ : Dict ,lowerCAmelCase_ : str ,lowerCAmelCase_ : Optional[int] ) -> List[str]: '''simple docstring''' if n == 0: return 1 elif n % 2 == 1: return (binary_exponentiation...
352
import unittest from parameterized import parameterized from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ...
277
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"""simple docstring""" import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision...
220
"""simple docstring""" 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 ...
220
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from __future__ import annotations def __UpperCamelCase ( UpperCAmelCase ): if len(UpperCAmelCase ) < 2: raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' ) if any(i <= 0 for i in nums ): raise ValueError('''All values must be greater than 0''' ) l...
368
'''simple docstring''' def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ): lowercase__ : Dict = len(UpperCAmelCase ) print('''The following activities are selected:''' ) # The first activity is always selected lowercase__ : str = 0 print(UpperCAmelCase , ...
214
0
'''simple docstring''' import uuid from typing import Any, Dict, List, Optional, Union from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_tf_available(): import tensorflow as tf if is_torch_available(): import torch __l...
27
'''simple docstring''' from __future__ import annotations import inspect import unittest from math import floor import numpy as np from transformers import CvtConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_visio...
27
1
import argparse import torch from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration from transformers.utils import logging logging.set_verbosity_info() snake_case_ = logging.get_logger(__name__) snake_case_ = [ ['''attention''', '''attn'''], ['''encoder_attention'...
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from collections import OrderedDict from ...utils import logging from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update from .configuration_auto import CONFIG_MAPPING_NAMES snake_case_ = logging.get_logger(__name__) snake_case_ = OrderedDict( [ # Base mod...
216
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import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def lowerCAmelCase_ (lowerCAmelCase__: Optional[int] ): """simple docstring""" if ( (cp >= 0X4E_00 an...
147
from __future__ import annotations from typing import Any class _a : def __init__(self, SCREAMING_SNAKE_CASE_ = 6 ) -> None: UpperCAmelCase_: Node | None = None UpperCAmelCase_: Node | None = None self.create_linked_list(...
147
1
import os import tempfile from functools import partial from unittest import TestCase from unittest.mock import patch import numpy as np import pytest from datasets.arrow_dataset import Dataset from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex from .utils import requi...
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # U...
14
0
'''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 A ( SCREAMI...
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'''simple docstring''' from maths.prime_check import is_prime def __lowerCAmelCase ( snake_case__ ): if not isinstance(snake_case__ , snake_case__ ): __UpperCamelCase : Optional[int] = F"Input value of [number={number}] must be an intege...
298
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import os import re from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _snake_case = logging.get_logger(__name__) _snake_case = {"""v...
201
import time from dataclasses import dataclass from multiprocessing import Pool from unittest import TestCase from unittest.mock import patch import multiprocess import numpy as np import pytest from datasets.utils.py_utils import ( NestedDataStructure, asdict, iflatmap_unordered, map_nested, ...
201
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"""simple docstring""" import unittest from transformers import DebertaConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTes...
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"""simple docstring""" import unittest from .lib import ( Matrix, Vector, axpy, square_zero_matrix, unit_basis_vector, zero_vector, ) class lowercase_ ( unittest.TestCase ): '''simple docstring''' def lowerCAmelCase_ ( self : int ): ...
315
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'''simple docstring''' from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features from .utils import DataProcessor, InputExample, Inpu...
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'''simple docstring''' 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 _snake_case ( ...
283
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'''simple docstring''' from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, convert_to_rgb, get_resize_output_image_size, normalize, rescale, ...
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from __future__ import annotations lowerCamelCase__ = { """A""": ["""B""", """C""", """E"""], """B""": ["""A""", """D""", """E"""], """C""": ["""A""", """F""", """G"""], """D""": ["""B"""], """E""": ["""A""", """B""", """D"""], """F""": ["""C"""], """G""": ["""C"""], } class...
302
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"""simple docstring""" 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...
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"""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...
205
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import os import unittest from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer from transformers.testing_utils import get_tests_dir from ...test_tokenization_common import TokenizerTesterMixin _lowerCamelCase : List[str] = get_tests_dir("""fixtures/te...
14
'''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-2.0 #...
265
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'''simple docstring''' import math import torch from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from .attention_processor import Attention from .embeddings import get_timestep_embedding from .modeling_utils import ModelMixin class lowerCamelCase_ ...
299
'''simple docstring''' import itertools import random import unittest import numpy as np from transformers import ASTFeatureExtractor from transformers.testing_utils import require_torch, require_torchaudio from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_...
299
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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 : str = logging.get_logger(__name__) _l...
169
from ...configuration_utils import PretrainedConfig from ...utils import logging _lowerCAmelCase : List[str] = logging.get_logger(__name__) _lowerCAmelCase : Optional[Any] = { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json"...
169
1
"""simple docstring""" from __future__ import annotations from random import random class UpperCAmelCase_ : def __init__( self : Any , snake_case_ : int | None = None ) -> List[Any]: '''simple docstring''' A__ = value A__ = random() ...
354
"""simple docstring""" # 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 #...
230
0
'''simple docstring''' import logging import os import quant_trainer import torch from torch.utils.data import DataLoader from transformers import Trainer, is_torch_tpu_available from transformers.trainer_utils import PredictionOutput lowercase_ = logging.getLogger(__name__) if is_torch_tpu_available(ch...
58
from math import factorial, radians def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 1_8 , lowerCAmelCase : int = 1_0 ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[str] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0) ...
18
0
import math def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase ): if 0 not in (x, y): # We use the relation x^y = y*log10(x), where 10 is the base. return y * math.logaa(_lowerCamelCase ) else: if x == 0: # 0 raised to any number is 0 ret...
256
import argparse import logging import os import sys import numpy as np import onnxruntime import torch from bart_onnx.generation_onnx import BARTBeamSearchGenerator from bart_onnx.reduce_onnx_size import remove_dup_initializers import transformers from transformers import BartForConditionalGenerat...
256
1
class a_ : """simple docstring""" def __init__( self : Dict ,snake_case : list[int] ): SCREAMING_SNAKE_CASE =len(A_ ) SCREAMING_SNAKE_CASE =[0] * len_array if len_array > 0: SCREAMING...
334
"""simple docstring""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = { '''junnyu/roformer_chinese_sma...
74
0
"""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 _a : int = get_tests_dir('fixtures/...
126
"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging _a : Any = logging.get_logger(__name__) _a : Any = { 'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json', 'uclanlp/visualbert-vq...
126
1
'''simple docstring''' from __future__ import annotations import collections import pprint from pathlib import Path def _SCREAMING_SNAKE_CASE ( UpperCamelCase ): """simple docstring""" return "".join(sorted(UpperCamelCase ) ) def _SCREAMING_SNAKE_CASE ( UpperCamelCase...
37
'''simple docstring''' from math import sqrt def _SCREAMING_SNAKE_CASE ( UpperCamelCase ): """simple docstring""" assert isinstance(UpperCamelCase , UpperCamelCase ) and ( number >= 0 ), "'number' must been an int and positive" lowerCAmelCase__ : int ...
37
1
'''simple docstring''' from typing import List, Optional, Union import numpy as np import tensorflow as tf from .utils import logging lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__) def _lowerCamelCase ( lowercase : Union[tf.Tensor, np.ndarray] ...
363
'''simple docstring''' from typing import Union from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available()...
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