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def lowerCamelCase_ ( lowerCAmelCase: int = 50_00_00_00 )-> int: _snake_case : Dict = set() _snake_case : Dict = int((limit - 24) ** (1 / 2) ) _snake_case : List[Any] = set(range(3 , prime_square_limit + 1 , 2 ) )...
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import copy from dataclasses import dataclass from pathlib import Path from typing import Dict, Optional, Union @dataclass class _lowerCAmelCase : '''simple docstring''' a_ : Optional[Union[str, Path]] =None a_ : bool =False a_ : bool ...
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import warnings from ...utils import logging from .image_processing_donut import DonutImageProcessor lowerCAmelCase_ = logging.get_logger(__name__) class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : Any , *Up...
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import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpta.tokenization_gpta import GPTaTokenizer from transformers.testing_utils import require_keras_nlp, require_t...
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import unittest import numpy as np import requests 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...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> list: _snake_case : List[Any] = int(lowerCAmelCase ) if n_element < 1: _snake_case : int = ValueError('a should be a positive number' ) raise my_error _snake_case : Union[str, Any] ...
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import gc import unittest import numpy as np import torch from torch.backends.cuda import sdp_kernel from diffusers import ( CMStochasticIterativeScheduler, ConsistencyModelPipeline, UNetaDModel, ) from diffusers.utils import randn_tensor, slow, torch_device from diffusers.utils.testing_utils import...
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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, prep...
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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_video_inputs if is_torch_available(): import...
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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 app...
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from functools import lru_cache @lru_cache def lowerCamelCase_ ( lowerCAmelCase: int )-> int: if num < 0: raise ValueError('Number should not be negative.' ) return 1 if num in (0, 1) else num * factorial(num - 1 ) if __name__ == "__main__": import doctest doctest.test...
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# Function to print upper half of diamond (pyramid) def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> List[str]: for i in range(0 , lowerCAmelCase ): for _ in range(0 , n - i - 1 ): # printing spaces print(' ' , end='' ) for _ in range(0 ...
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def lowerCamelCase_ ( lowerCAmelCase: List[str] , lowerCAmelCase: List[str] , lowerCAmelCase: Dict , lowerCAmelCase: Dict )-> str: if height >= 1: move_tower(height - 1 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) move_disk(lo...
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """MIT/ast-finetuned-audioset-10-10-0.4593""": ( """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c...
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def lowerCamelCase_ ( lowerCAmelCase: int = 3 , lowerCAmelCase: int = 7 , lowerCAmelCase: int = 1_00_00_00 )-> int: _snake_case : int = 0 _snake_case : Optional[Any] = 1 for current_denominator in range(1 , limit + 1 ): _sna...
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import math import random from typing import Any from .hill_climbing import SearchProblem def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: bool = True , lowerCAmelCase: float = math.inf , lowerCAmelCase: float = -math.inf , lowerCAmelCase: float = math....
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from __future__ import annotations from typing import Generic, TypeVar lowerCAmelCase_ = TypeVar("""T""") class _lowerCAmelCase ( Generic[T] ): '''simple docstring''' def __init__( self : Union[str, Any] , UpperCamelCase : T ): ...
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from dataclasses import dataclass from typing import Dict, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .attention_processor imp...
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from __future__ import annotations import inspect import unittest import numpy as np from transformers import ResNetConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_vision_available from ...test_configuration_com...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> int: if not isinstance(lowerCAmelCase , lowerCAmelCase ): _snake_case : Union[str, Any] = F"""Input value of [number={number}] must be an integer""" raise TypeError(lowerCAmelCase ) if number < 1: _snake_...
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def lowerCamelCase_ ( lowerCAmelCase: float , lowerCAmelCase: float )-> float: 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__ == "__ma...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_lxmert import LxmertTokenizer lowerCAmelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} ...
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from __future__ import annotations class _lowerCAmelCase : '''simple docstring''' def __init__( self : List[str] , UpperCamelCase : str , UpperCamelCase : str ): '''simple docstring''' _snake_case , _snake_cas...
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from __future__ import annotations from random import random class _lowerCAmelCase : '''simple docstring''' def __init__( self : Dict , UpperCamelCase : int | None = None ): '''simple docstring''' _snake_case : str = ...
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from dataclasses import dataclass, field from typing import Tuple from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends from .benchmark_args_utils import BenchmarkArguments if is_torch_available(): import torch if is_torch_tpu_available(check_device=...
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from functools import reduce lowerCAmelCase_ = ( """73167176531330624919225119674426574742355349194934""" """96983520312774506326239578318016984801869478851843""" """85861560789112949495459501737958331952853208805511""" """12540698747158523863050715693290963295227443043557""" """66...
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import copy import json import os import tempfile from transformers import is_torch_available from .test_configuration_utils import config_common_kwargs class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : Tuple , UpperCam...
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from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def lowerCamelCase_ ( )-> Any: _snake_case : List[str] = { 'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'], 'path': ['test_...
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from __future__ import annotations def lowerCamelCase_ ( lowerCAmelCase: list , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int )-> list: _snake_case : Tuple = [] _snake_case , _snake_case : List[Any] = inp...
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import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' a_ : Union[str, Any] =["""image_processor""", """tokenizer"""] a_ : ...
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import math_equivalence # From: git+https://github.com/hendrycks/math.git import datasets lowerCAmelCase_ = """\ @article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Aror...
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import json import os from pathlib import Path import pytest from datasets.download.download_config import DownloadConfig from datasets.download.download_manager import DownloadManager from datasets.utils.file_utils import hash_url_to_filename lowerCAmelCase_ = """http://www.mocksite.com/file1.txt"...
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# Function to print upper half of diamond (pyramid) def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> List[str]: for i in range(0 , lowerCAmelCase ): for _ in range(0 , n - i - 1 ): # printing spaces print(' ' , end='' ) for _ in range(0 ...
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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 lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """roberta-base""": """https://huggingface...
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import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_availab...
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from random import randint, random def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , lowerCAmelCase: int = 5 , )-> list: _snake_case : Dict ...
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from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, apply_forward_hook from .modeling_utils import ModelMixin from .vae import Decoder, DecoderOutput, Enco...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, logging from .tokenization_realm import RealmTokenizer lowerCAmelCas...
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import unittest from transformers import AlbertTokenizer, AlbertTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin lowerCAmelCase_ = get_tests_dir("""fixtures/spiece.model""...
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import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = {name: getattr(transformers, name + """Fast""") for name i...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_bert import BertTokenizer lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = ...
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def lowerCamelCase_ ( lowerCAmelCase: bytes )-> str: return "".join([hex(lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase )] ) def lowerCamelCase_ ( lowerCAmelCase: str )-> bytes: # Check data validity, following RFC3548 # https://www.ietf.org/rf...
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from __future__ import annotations def lowerCamelCase_ ( lowerCAmelCase: list )-> float: if not nums: raise ValueError('List is empty' ) return sum(lowerCAmelCase ) / len(lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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import csv import tweepy # Twitter API credentials lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" def lowerCamelCase_ ( lowerCAmelCase: str )-> None: # authorize twitter, initialize tweepy _snake_...
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from ..utils import DummyObject, requires_backends class _lowerCAmelCase ( metaclass=UpperCAmelCase_ ): '''simple docstring''' a_ : List[str] =["""torch""", """transformers""", """onnx"""] def __init__( self : str , *UpperCamelCase ...
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import copy from dataclasses import dataclass from pathlib import Path from typing import Dict, Optional, Union @dataclass class _lowerCAmelCase : '''simple docstring''' a_ : Optional[Union[str, Path]] =None a_ : bool =False a_ : bool ...
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from collections.abc import Generator def lowerCamelCase_ ( )-> Generator[int, None, None]: _snake_case , _snake_case : Optional[Any] = 0, 1 while True: _snake_case , _snake_case : str = b, a + b yield b def lowerCamelCase_ ( ...
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import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpta.tokenization_gpta import GPTaTokenizer from transformers.testing_utils import require_keras_nlp, require_t...
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from __future__ import annotations lowerCAmelCase_ = """Muhammad Umer Farooq""" lowerCAmelCase_ = """MIT""" lowerCAmelCase_ = """1.0.0""" lowerCAmelCase_ = """Muhammad Umer Farooq""" lowerCAmelCase_ = """contact@muhammadumerfarooq.me""" lowerCAmelCase_ ...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> list: _snake_case : List[Any] = int(lowerCAmelCase ) if n_element < 1: _snake_case : int = ValueError('a should be a positive number' ) raise my_error _snake_case : Union[str, Any] ...
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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 app...
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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, prep...
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from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class _lowerCAmelCase ( UpperCAmelCase_ ): '...
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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 app...
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import inspect from typing import Callable, List, Optional, Union import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers import DiffusionPipeline from diffusers.models import AutoencoderKL, UNetaDConditionModel from diffusers.pipelines.stable_diffusion import Stabl...
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# Function to print upper half of diamond (pyramid) def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> List[str]: for i in range(0 , lowerCAmelCase ): for _ in range(0 , n - i - 1 ): # printing spaces print(' ' , end='' ) for _ in range(0 ...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> int: if not isinstance(lowerCAmelCase , lowerCAmelCase ): _snake_case : Union[str, Any] = F"""Input value of [number={number}] must be an integer""" raise TypeError(lowerCAmelCase ) if number < 1: _snake_...
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """MIT/ast-finetuned-audioset-10-10-0.4593""": ( """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c...
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import json import os from datetime import date from pathlib import Path from tabulate import DataRow, TableFormat, tabulate lowerCAmelCase_ = TableFormat( lineabove=None, linebelowheader=None, linebetweenrows=None, linebelow=None, headerrow=DataRow("""""", """|""", """|"""), ...
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import math import random from typing import Any from .hill_climbing import SearchProblem def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: bool = True , lowerCAmelCase: float = math.inf , lowerCAmelCase: float = -math.inf , lowerCAmelCase: float = math....
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import warnings from transformers import AutoTokenizer from transformers.utils import is_torch_available from transformers.utils.generic import ExplicitEnum from ...processing_utils import ProcessorMixin if is_torch_available(): import torch class _lowerCAmelCase ( UpperCAmelCase_ )...
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from dataclasses import dataclass from typing import Dict, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .attention_processor imp...
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import tempfile import unittest from transformers import TaConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_comm...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> int: if not isinstance(lowerCAmelCase , lowerCAmelCase ): _snake_case : Union[str, Any] = F"""Input value of [number={number}] must be an integer""" raise TypeError(lowerCAmelCase ) if number < 1: _snake_...
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import warnings from ...utils import logging from .image_processing_deit import DeiTImageProcessor lowerCAmelCase_ = logging.get_logger(__name__) class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : Optional[int] ...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_lxmert import LxmertTokenizer lowerCAmelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} ...
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from __future__ import annotations import math def lowerCamelCase_ ( lowerCAmelCase: float , lowerCAmelCase: int )-> float: _snake_case : Tuple = u for i in range(1 , lowerCAmelCase ): _snake_case : Optional[int] = temp * (u - i) r...
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from __future__ import annotations from random import random class _lowerCAmelCase : '''simple docstring''' def __init__( self : Dict , UpperCamelCase : int | None = None ): '''simple docstring''' _snake_case : str = ...
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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 lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """junnyu/roformer_chinese_small""": """ht...
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from functools import reduce lowerCAmelCase_ = ( """73167176531330624919225119674426574742355349194934""" """96983520312774506326239578318016984801869478851843""" """85861560789112949495459501737958331952853208805511""" """12540698747158523863050715693290963295227443043557""" """66...
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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 lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """andreasmadsen/efficient_mlm_m0.40""": (...
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from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def lowerCamelCase_ ( )-> Any: _snake_case : List[str] = { 'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'], 'path': ['test_...
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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 app...
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import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' a_ : Union[str, Any] =["""image_processor""", """tokenizer"""] a_ : ...
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class _low...
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import json import os from pathlib import Path import pytest from datasets.download.download_config import DownloadConfig from datasets.download.download_manager import DownloadManager from datasets.utils.file_utils import hash_url_to_filename lowerCAmelCase_ = """http://www.mocksite.com/file1.txt"...
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """microsoft/trocr-base-handwritten""": ( """https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json""" ...
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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 lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """roberta-base""": """https://huggingface...
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from __future__ import annotations from random import random class _lowerCAmelCase : '''simple docstring''' def __init__( self : Dict , UpperCamelCase : int | None = None ): '''simple docstring''' _snake_case : str = ...
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from random import randint, random def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , lowerCAmelCase: int = 5 , )-> list: _snake_case : Dict ...
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import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class ...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, logging from .tokenization_realm import RealmTokenizer lowerCAmelCas...
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lowerCAmelCase_ = { """Pillow""": """Pillow""", """accelerate""": """accelerate>=0.11.0""", """compel""": """compel==0.1.8""", """black""": """black~=23.1""", """datasets""": """datasets""", """filelock""": """filelock""", """flax""": """flax>=0.4.1""", """hf-doc-builder...
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import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = {name: getattr(transformers, name + """Fast""") for name i...
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import jax.numpy as jnp from ...utils import logging from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel from .configuration_mta import MTaConfig lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = """T5Config""" def lowerC...
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def lowerCamelCase_ ( lowerCAmelCase: bytes )-> str: return "".join([hex(lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase )] ) def lowerCamelCase_ ( lowerCAmelCase: str )-> bytes: # Check data validity, following RFC3548 # https://www.ietf.org/rf...
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import logging import os from dataclasses import dataclass, field from typing import Dict, Optional import datasets import numpy as np import tensorflow as tf from transformers import ( AutoConfig, AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizer, TFAutoModelForSequen...
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import csv import tweepy # Twitter API credentials lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" def lowerCamelCase_ ( lowerCAmelCase: str )-> None: # authorize twitter, initialize tweepy _snake_...
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from typing import Dict import numpy as np from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException if is_tf_available(): import tensorflow as tf from ..tf_utils import stable_softmax if...
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import copy from dataclasses import dataclass from pathlib import Path from typing import Dict, Optional, Union @dataclass class _lowerCAmelCase : '''simple docstring''' a_ : Optional[Union[str, Path]] =None a_ : bool =False a_ : bool ...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) lowerCAmelCase_ = { """configuration_mobilebert""": [ """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", ...
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import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpta.tokenization_gpta import GPTaTokenizer from transformers.testing_utils import require_keras_nlp, require_t...
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import os import shutil from pathlib import Path from typing import Optional, Union import numpy as np from huggingface_hub import hf_hub_download from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging if is_onnx_available(): import onnxruntime as ort lowerCAmelC...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> list: _snake_case : List[Any] = int(lowerCAmelCase ) if n_element < 1: _snake_case : int = ValueError('a should be a positive number' ) raise my_error _snake_case : Union[str, Any] ...
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from graphs.minimum_spanning_tree_kruskal import kruskal def lowerCamelCase_ ( )-> Any: _snake_case : int = 9 _snake_case : Dict = [ [0, 1, 4], [0, 7, 8], [1, 2, 8], [7, 8, 7], [7, 6, 1], [2, 8, 2], [8, 6, 6], [2, ...
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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, prep...
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import numpy as np class _lowerCAmelCase : '''simple docstring''' def __init__( self : Dict ): '''simple docstring''' _snake_case : Any = (0, 0) _snake_case : Any = None _snake_case : List[Any] ...
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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 app...
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import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Audio, ClassLabel, Features from .base import TaskTemplate @dataclass(frozen=UpperCAmelCase_ ) class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' a_ ...
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# Function to print upper half of diamond (pyramid) def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> List[str]: for i in range(0 , lowerCAmelCase ): for _ in range(0 , n - i - 1 ): # printing spaces print(' ' , end='' ) for _ in range(0 ...
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import copy import fnmatch import json import os import pickle as pkl import shutil import sys import tarfile import tempfile from collections import OrderedDict from contextlib import contextmanager from functools import partial from hashlib import shaaaa from io import BytesIO from pathlib import Path from urll...
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """MIT/ast-finetuned-audioset-10-10-0.4593""": ( """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c...
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import logging import math from functools import partial from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union import torch from .tensor_utils import tensor_tree_map, tree_map def lowerCamelCase_ ( lowerCAmelCase: Union[dict, list, tuple, torch.Tensor] )-> List[...
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import math import random from typing import Any from .hill_climbing import SearchProblem def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: bool = True , lowerCAmelCase: float = math.inf , lowerCAmelCase: float = -math.inf , lowerCAmelCase: float = math....
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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 logging logging.set_verb...
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from dataclasses import dataclass from typing import Dict, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .attention_processor imp...
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import math from datetime import datetime, timedelta def lowerCamelCase_ ( lowerCAmelCase: int )-> datetime: _snake_case : Optional[Any] = year % 19 _snake_case : List[Any] = year % 4 _snake_case : Optional[Any] = year % 7 _snake_case ...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> int: if not isinstance(lowerCAmelCase , lowerCAmelCase ): _snake_case : Union[str, Any] = F"""Input value of [number={number}] must be an integer""" raise TypeError(lowerCAmelCase ) if number < 1: _snake_...
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import logging from pathlib import Path import numpy as np import pytorch_lightning as pl import torch from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.utilities import rank_zero_only from utils_rag import save_json def lowerCamelCase_ ( lowerCAmelCase: Lis...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_lxmert import LxmertTokenizer lowerCAmelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} ...
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import numpy as np def lowerCamelCase_ ( lowerCAmelCase: np.ndarray , lowerCAmelCase: float )-> np.ndarray: return np.where(vector > 0 , lowerCAmelCase , (alpha * (np.exp(lowerCAmelCase ) - 1)) ) if __name__ == "__main__": import doctest doctest.tes...
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from __future__ import annotations from random import random class _lowerCAmelCase : '''simple docstring''' def __init__( self : Dict , UpperCamelCase : int | None = None ): '''simple docstring''' _snake_case : str = ...
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import qiskit def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int )-> qiskit.result.counts.Counts: _snake_case : Any = qiskit.Aer.get_backend('aer_simulator' ) # Create a Quantum Circuit acting on the q register _snake_case : Union[str, Any] ...
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from functools import reduce lowerCAmelCase_ = ( """73167176531330624919225119674426574742355349194934""" """96983520312774506326239578318016984801869478851843""" """85861560789112949495459501737958331952853208805511""" """12540698747158523863050715693290963295227443043557""" """66...
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import itertools import os import random import tempfile import unittest import numpy as np from datasets import load_dataset from transformers import is_speech_available from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio from transformers.utils.import_u...
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from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def lowerCamelCase_ ( )-> Any: _snake_case : List[str] = { 'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'], 'path': ['test_...
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import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = {name: getattr(transformers, name + """Fast""") for name i...
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import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' a_ : Union[str, Any] =["""image_processor""", """tokenizer"""] a_ : ...
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import argparse import re from flax.traverse_util import flatten_dict, unflatten_dict from tax import checkpoints from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model from transformer...
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import json import os from pathlib import Path import pytest from datasets.download.download_config import DownloadConfig from datasets.download.download_manager import DownloadManager from datasets.utils.file_utils import hash_url_to_filename lowerCAmelCase_ = """http://www.mocksite.com/file1.txt"...
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import logging import os import sys from dataclasses import dataclass, field from importlib import import_module from typing import Dict, List, Optional, Tuple import numpy as np from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score from torch import nn from utils_ner import Split, ...
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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 lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """roberta-base""": """https://huggingface...
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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 im...
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from random import randint, random def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , lowerCAmelCase: int = 5 , )-> list: _snake_case : Dict ...
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import warnings from ...utils import logging from .image_processing_beit import BeitImageProcessor lowerCAmelCase_ = logging.get_logger(__name__) class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] ...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, logging from .tokenization_realm import RealmTokenizer lowerCAmelCas...
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import os import random import sys from . import cryptomath_module as cryptomath from . import rabin_miller lowerCAmelCase_ = 3 def lowerCamelCase_ ( lowerCAmelCase: int )-> int: print('Generating primitive root of p' ) while True: _snake_case : int = rando...
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import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = {name: getattr(transformers, name + """Fast""") for name i...
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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_ ( lo...
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def lowerCamelCase_ ( lowerCAmelCase: bytes )-> str: return "".join([hex(lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase )] ) def lowerCamelCase_ ( lowerCAmelCase: str )-> bytes: # Check data validity, following RFC3548 # https://www.ietf.org/rf...
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from typing import Any, Callable, Dict, List, Optional, Union import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DiffusionPipeline, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPipeline, ...
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import csv import tweepy # Twitter API credentials lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" def lowerCamelCase_ ( lowerCAmelCase: str )-> None: # authorize twitter, initialize tweepy _snake_...
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from manim import * class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' def UpperCamelCase_ ( self : Optional[Any] ): '''simple docstring''' _snake_case : Optional[int] = Rectangle(height=0.5 , width=0.5 ...
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import copy from dataclasses import dataclass from pathlib import Path from typing import Dict, Optional, Union @dataclass class _lowerCAmelCase : '''simple docstring''' a_ : Optional[Union[str, Path]] =None a_ : bool =False a_ : bool ...
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import warnings from contextlib import contextmanager from ...processing_utils import ProcessorMixin class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' a_ : Union[str, Any] ="""Speech2TextFeatureExtractor""" a_ : Union[str, Any] ...
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import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpta.tokenization_gpta import GPTaTokenizer from transformers.testing_utils import require_keras_nlp, require_t...
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import random import sys import numpy as np from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap lowerCAmelCase_ = """Usage of script: script_name <size_of_canvas:int>""" lowerCAmelCase_ = [0] * 100 + [1] * 10 random.shuffle(choice) def lowerCamelCase_ (...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> list: _snake_case : List[Any] = int(lowerCAmelCase ) if n_element < 1: _snake_case : int = ValueError('a should be a positive number' ) raise my_error _snake_case : Union[str, Any] ...
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import unittest import torch from diffusers import VQModel from diffusers.utils import floats_tensor, torch_device from diffusers.utils.testing_utils import enable_full_determinism from .test_modeling_common import ModelTesterMixin, UNetTesterMixin enable_full_determinism() class _lowerCAmelCase ...
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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, prep...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) lowerCAmelCase_ = { """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_M...
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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 app...
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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 lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """bert-base-uncased""": """https://huggin...
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# Function to print upper half of diamond (pyramid) def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> List[str]: for i in range(0 , lowerCAmelCase ): for _ in range(0 , n - i - 1 ): # printing spaces print(' ' , end='' ) for _ in range(0 ...
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import sys def lowerCamelCase_ ( lowerCAmelCase: Any )-> str: _snake_case : Optional[Any] = len(lowerCAmelCase ) _snake_case : Dict = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )] _snake_case : Dict = [[0 f...
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """MIT/ast-finetuned-audioset-10-10-0.4593""": ( """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c...
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import argparse import torch from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert from transformers.utils import logging logging.set_verbosity_info() def lowerCamelCase_ ( lowerCAmelCase: Dict , lowerCAmelCase: List[Any] , lowerCAmelCase: str )-> in...
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import math import random from typing import Any from .hill_climbing import SearchProblem def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: bool = True , lowerCAmelCase: float = math.inf , lowerCAmelCase: float = -math.inf , lowerCAmelCase: float = math....
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import unittest from knapsack import knapsack as k class _lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' def UpperCamelCase_ ( self : Dict ): '''simple docstring''' _snake_case : Tuple = 0 _snake_case : ...
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from dataclasses import dataclass from typing import Dict, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .attention_processor imp...
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import unittest import numpy as np import timeout_decorator # noqa from transformers import BlenderbotConfig, is_flax_available from transformers.testing_utils import jax_device, require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import Fla...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> int: if not isinstance(lowerCAmelCase , lowerCAmelCase ): _snake_case : Union[str, Any] = F"""Input value of [number={number}] must be an integer""" raise TypeError(lowerCAmelCase ) if number < 1: _snake_...
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import argparse from argparse import Namespace import torch from torch import nn from transformers import XGLMConfig, XGLMForCausalLM def lowerCamelCase_ ( lowerCAmelCase: Dict )-> List[str]: _snake_case : str = [ 'decoder.version', 'decoder.output_projection.weigh...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_lxmert import LxmertTokenizer lowerCAmelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} ...
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import numpy as np from cva import destroyAllWindows, imread, imshow, waitKey class _lowerCAmelCase : '''simple docstring''' def __init__( self : List[Any] , UpperCamelCase : List[str] , UpperCamelCase : int , UpperCamelCase : ...
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from __future__ import annotations from random import random class _lowerCAmelCase : '''simple docstring''' def __init__( self : Dict , UpperCamelCase : int | None = None ): '''simple docstring''' _snake_case : str = ...
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import logging import torch from accelerate import Accelerator from arguments import EvaluationArguments from datasets import load_dataset from torch.utils.data import IterableDataset from torch.utils.data.dataloader import DataLoader from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParse...
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from functools import reduce lowerCAmelCase_ = ( """73167176531330624919225119674426574742355349194934""" """96983520312774506326239578318016984801869478851843""" """85861560789112949495459501737958331952853208805511""" """12540698747158523863050715693290963295227443043557""" """66...
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import tempfile import torch from diffusers import ( DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler, ) from .test_schedulers import SchedulerCommonTest class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docst...
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from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def lowerCamelCase_ ( )-> Any: _snake_case : List[str] = { 'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'], 'path': ['test_...
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import pprint import requests lowerCAmelCase_ = """https://zenquotes.io/api""" def lowerCamelCase_ ( )-> list: return requests.get(API_ENDPOINT_URL + '/today' ).json() def lowerCamelCase_ ( )-> list: return requests.get(API_ENDPOINT_URL + '/random' ).json() if __na...
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import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' a_ : Union[str, Any] =["""image_processor""", """tokenizer"""] a_ : ...
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def lowerCamelCase_ ( lowerCAmelCase: bytes )-> str: return "".join([hex(lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase )] ) def lowerCamelCase_ ( lowerCAmelCase: str )-> bytes: # Check data validity, following RFC3548 # https://www.ietf.org/rf...
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import json import os from pathlib import Path import pytest from datasets.download.download_config import DownloadConfig from datasets.download.download_manager import DownloadManager from datasets.utils.file_utils import hash_url_to_filename lowerCAmelCase_ = """http://www.mocksite.com/file1.txt"...
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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, prep...
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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 lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """roberta-base""": """https://huggingface...
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import builtins import sys from ...utils.imports import _is_package_available from . import cursor, input from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor from .keymap import KEYMAP lowerCAmelCase_ = False try: lowerCAmelCase_ = ...
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from random import randint, random def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , lowerCAmelCase: int = 5 , )-> list: _snake_case : Dict ...
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import random def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: float , lowerCAmelCase: bool = False )-> dict: _snake_case : dict = {i: [] for i in range(lowerCAmelCase )} # if probability is greater or equal than 1, then generate a complete gra...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, logging from .tokenization_realm import RealmTokenizer lowerCAmelCas...
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import json import os import unittest from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers from ...test_tokenization_common import To...
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import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = {name: getattr(transformers, name + """Fast""") for name i...
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import argparse import pytorch_lightning as pl import torch from torch import nn from transformers import LongformerForQuestionAnswering, LongformerModel class _lowerCAmelCase ( pl.LightningModule ): '''simple docstring''' def __init__( self : List[Any] , ...
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def lowerCamelCase_ ( lowerCAmelCase: bytes )-> str: return "".join([hex(lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase )] ) def lowerCamelCase_ ( lowerCAmelCase: str )-> bytes: # Check data validity, following RFC3548 # https://www.ietf.org/rf...
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from abc import ABC, abstractmethod from argparse import ArgumentParser class _lowerCAmelCase ( UpperCAmelCase_ ): '''simple docstring''' @staticmethod @abstractmethod def UpperCamelCase_ ( UpperCamelCase : ArgumentParser ): '''simple docstring''' ...
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import csv import tweepy # Twitter API credentials lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" lowerCAmelCase_ = """""" def lowerCamelCase_ ( lowerCAmelCase: str )-> None: # authorize twitter, initialize tweepy _snake_...
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def lowerCamelCase_ ( lowerCAmelCase: str )-> bool: if not all(x.isalpha() for x in string ): raise ValueError('String must only contain alphabetic characters.' ) _snake_case : Optional[Any] = sorted(string.lower() ) return len(lowerCAmelCase ) == len(set(lowe...
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import copy from dataclasses import dataclass from pathlib import Path from typing import Dict, Optional, Union @dataclass class _lowerCAmelCase : '''simple docstring''' a_ : Optional[Union[str, Path]] =None a_ : bool =False a_ : bool ...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available lowerCAmelCase_ = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]} try: if not is_vision...
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import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpta.tokenization_gpta import GPTaTokenizer from transformers.testing_utils import require_keras_nlp, require_t...
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from __future__ import annotations from typing import Any def lowerCamelCase_ ( lowerCAmelCase: list[Any] )-> None: create_state_space_tree(lowerCAmelCase , [] , 0 ) def lowerCamelCase_ ( lowerCAmelCase: list[Any] , lowerCAmelCase: list[Any] , lo...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> list: _snake_case : List[Any] = int(lowerCAmelCase ) if n_element < 1: _snake_case : int = ValueError('a should be a positive number' ) raise my_error _snake_case : Union[str, Any] ...
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from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def lowerCamelCase_ ( )-> Any: _snake_case : List[str] = { 'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'], 'path': ['test_...
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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, prep...
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1
import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_lxmert import LxmertTokenizer lowerCAmelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} ...
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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 app...
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from __future__ import annotations from collections.abc import Generator import requests from bsa import BeautifulSoup lowerCAmelCase_ = """https://www.indeed.co.in/jobs?q=mobile+app+development&l=""" def lowerCamelCase_ ( lowerCAmelCase: str = "mumbai" )-> Generator[tuple[str, str], ...
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# Function to print upper half of diamond (pyramid) def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> List[str]: for i in range(0 , lowerCAmelCase ): for _ in range(0 , n - i - 1 ): # printing spaces print(' ' , end='' ) for _ in range(0 ...
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1
import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, A...
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """MIT/ast-finetuned-audioset-10-10-0.4593""": ( """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c...
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1
from random import randint, random def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , lowerCAmelCase: int = 5 , )-> list: _snake_case : Dict ...
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import math import random from typing import Any from .hill_climbing import SearchProblem def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: bool = True , lowerCAmelCase: float = math.inf , lowerCAmelCase: float = -math.inf , lowerCAmelCase: float = math....
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1
import gc import tempfile import unittest import numpy as np import torch from diffusers import VersatileDiffusionPipeline from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device lowerCAmelCase_ = False class _lowerCAmelCase ( unittest.TestCase ...
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from dataclasses import dataclass from typing import Dict, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .attention_processor imp...
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase_ = logging.get_logger(__name__) lowerCAmelCase_ = { """MIT/ast-finetuned-audioset-10-10-0.4593""": ( """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c...
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def lowerCamelCase_ ( lowerCAmelCase: int )-> int: if not isinstance(lowerCAmelCase , lowerCAmelCase ): _snake_case : Union[str, Any] = F"""Input value of [number={number}] must be an integer""" raise TypeError(lowerCAmelCase ) if number < 1: _snake_...
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import contextlib import importlib import io import unittest import transformers # Try to import everything from transformers to ensure every object can be loaded. from transformers import * # noqa F406 from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch fro...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_lxmert import LxmertTokenizer lowerCAmelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} ...
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import re import string from collections import Counter import sacrebleu import sacremoses from packaging import version import datasets lowerCAmelCase_ = """ @inproceedings{xu-etal-2016-optimizing, title = {Optimizing Statistical Machine Translation for Text Simplification}, authors={Xu, W...
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from __future__ import annotations from random import random class _lowerCAmelCase : '''simple docstring''' def __init__( self : Dict , UpperCamelCase : int | None = None ): '''simple docstring''' _snake_case : str = ...
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