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'''simple docstring''' from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging lowerCamelCase :Optional[Any] = logging.get_logger(__name__) lowerCamelCase :List[str] ...
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'''simple docstring''' import multiprocessing import os from typing import BinaryIO, Optional, Union import fsspec from .. import Dataset, Features, NamedSplit, config from ..formatting import query_table from ..packaged_modules.json.json import Json from ..utils import logging from ..utils.typing import Nest...
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'''simple docstring''' from dataclasses import dataclass, field from typing import Tuple from ..utils import cached_property, is_tf_available, logging, requires_backends from .benchmark_args_utils import BenchmarkArguments if is_tf_available(): import tensorflow as tf a : Union[str, Any] ...
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available a : List[str] = { """configuration_altclip""": [ """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AltCLIPConf...
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"""simple docstring""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase : int = logging.get_logger(__name__) UpperCAmel...
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lowerCAmelCase : str = '0.21.0' from .accelerator import Accelerator from .big_modeling import ( cpu_offload, cpu_offload_with_hook, disk_offload, dispatch_model, init_empty_weights, init_on_device, load_checkpoint_and_dispatch, ) from .data_loader import skip_first_batches...
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"""simple docstring""" from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available from .timesteps import ( fastaa_timesteps, smartaa_timesteps, ...
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"""simple docstring""" from __future__ import annotations class __UpperCamelCase : def __init__(self : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str): A , A = text, pattern A , A = len(__SCREAMING_SNAKE_C...
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging a = logging.get_logger(__name__) a = { 'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json', 'funnel-transfor...
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"""simple docstring""" from __future__ import annotations def lowercase (snake_case__ : list[int] ) -> list[int]: # This function is recursive '''simple docstring''' lowerCAmelCase = len(snake_case__ ) # If the array contains only...
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"""simple docstring""" import os import sys import tempfile import torch from .state import AcceleratorState from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=() , __SCREAMING_SNAKE_...
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"""simple docstring""" from __future__ import annotations from decimal import Decimal from math import * # noqa: F403 from sympy import diff def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 1_0**-1_0 ) -> float: __lowerCAmelCase: ...
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from __future__ import annotations def lowerCamelCase__ ( A__ : int , A__ : int ): '''simple docstring''' __lowerCamelCase = [] create_all_state(1 , A__ , A__ , [] , A__ ) return result def lowerCamelCase__ ...
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'''simple docstring''' from datetime import datetime import matplotlib.pyplot as plt import torch def a ( __a ) -> int: '''simple docstring''' for param in module.parameters(): UpperCamelCase__ :Dict = False def a ( ) -> Union[str, Any]: ...
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"""simple docstring""" from __future__ import annotations def __lowercase ( snake_case_ : Any ,snake_case_ : List[str] ) ->Any: '''simple docstring''' if b == 0: return (1, 0) (__A) : Optional[int] = extended_euclid(_UpperCAm...
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"""simple docstring""" from math import factorial def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int: '''simple docstring''' if n < k or k < 0: raise ValueError('''Please enter positive integers for n and k where n >= k''' ) re...
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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, resize, to_channel_dimen...
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"""simple docstring""" from __future__ import annotations def lowercase (SCREAMING_SNAKE_CASE_ : str ) -> list[int]: return [ord(SCREAMING_SNAKE_CASE_ ) - 96 for elem in plain] def lowercase (SCREAMING_SNAKE_CASE_ : list[int] ) -> str: return "...
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"""simple docstring""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging __UpperCamelCase : int = logging.get_logger(__name__) __UpperCame...
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"""simple docstring""" import math def __SCREAMING_SNAKE_CASE ( A_ , A_ = 0 , A_ = 0 ): lowerCAmelCase__ : Tuple = end or len(A_ ) for i in range(A_ , A_ ): lowerCAmelCase__ : Dict = i lowerCAmelCase__ : List[Any] = ...
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"""simple docstring""" from collections import OrderedDict from typing import List, Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging lowercase_ = logging.get_logger(__name__) lowe...
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import argparse import logging import os from datetime import datetime import numpy as np import torch from torch import nn from torch.utils.data import DataLoader, RandomSampler, TensorDataset from tqdm import tqdm from transformers import GPTaLMHeadModel a_ :Optional[Any] = logging.getLogger(__n...
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import math class __magic_name__ : def __init__( self : int , lowerCamelCase__ : Any=0 ) -> List[Any]: # a graph with Node 0,1,...,N-1 '''simple docstring''' UpperCamelCase__ : List[Any] = n UpperCamelCase__ : Opti...
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import inspect from typing import Callable, List, Optional, Union import torch from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, WhisperForConditionalGeneration, WhisperProcessor, ) from diffusers import ( AutoencoderKL, DDIMScheduler, DiffusionPipeline, ...
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import argparse import collections import os import re from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_table.py _lowerCamelCase ="src/transformers" _lowerCamelCase ...
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from ...configuration_utils import PretrainedConfig from ...utils import logging _lowerCamelCase =logging.get_logger(__name__) _lowerCamelCase ={ "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json", # See all ViT MAE models at https://h...
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from ...configuration_utils import PretrainedConfig from ...utils import logging __SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) __SCREAMING_SNAKE_CASE : List[Any] = { 'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json', ...
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def snake_case (__lowercase ) -> list[int]: '''simple docstring''' if num <= 0: raise ValueError("Input must be a positive integer" ) _snake_case : Any = [True] * (num + 1) _snake_case : str = 2 while p * p <= num: i...
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import argparse import json import os import sys import tempfile import unittest from argparse import Namespace from dataclasses import dataclass, field from enum import Enum from pathlib import Path from typing import List, Literal, Optional import yaml from transformers import HfArgumentParser, TrainingArguments fr...
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def lowercase_( SCREAMING_SNAKE_CASE_ ): '''simple docstring''' for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 , 0 , -1 ): lowerCamelCase : Tuple = False for j in range(SCREAMING_SNAKE_CASE_ , 0 , -1 ): if unsorted[j] < unsorted[j...
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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 SCREAMING_SNAKE_CASE__ ( ) -> List[str]: '''simple docstring''' with offline(OfflineSimulat...
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from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices __A : Any = logging.get_logger(__name__) __A : Union[str, Any] = { '''shi-labs/dinat-mini-...
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import unittest import numpy as np from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import tor...
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def _lowercase ( lowercase__ ): if not all(x.isalpha() for x in string ): raise ValueError('''String must only contain alphabetic characters.''' ) __lowerCAmelCase : int = sorted(string.lower() ) return len(lowercase__ ) == len(set(lowerca...
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import re import tempfile from pathlib import Path import pytest import yaml from datasets.utils.readme import ReadMe # @pytest.fixture # def example_yaml_structure(): SCREAMING_SNAKE_CASE_ = yaml.safe_load( """\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsub...
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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_ge...
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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, ) fro...
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available A : Optional[int] = { "configuration_altclip": [ "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "AltCLIPConfig", ...
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'''simple docstring''' # flake8: noqa # Lint as: python3 _lowercase : int = [ "VerificationMode", "Version", "disable_progress_bar", "enable_progress_bar", "is_progress_bar_enabled", "experimental", ] from .info_utils import VerificationMode from .logging import disable_pro...
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'''simple docstring''' import os import numpy import onnx def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple: lowercase_ : Tuple = a.name lowercase_ : Tuple = b.name lowercase_ : Any ...
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import random import unittest from torch.utils.data import BatchSampler, DataLoader, IterableDataset from accelerate import Accelerator from accelerate.data_loader import ( BatchSamplerShard, DataLoaderDispatcher, DataLoaderShard, IterableDatasetShard, SkipBatchSampler, SkipDat...
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"""simple docstring""" 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__ = ''...
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"""simple docstring""" import functools def snake_case (A_ :Tuple , A_ :str ): '''simple docstring''' if not isinstance(a__ , a__ ) or not all(isinstance(a__ , a__ ) for day in days ): raise ValueError('The parameter days should be a list ...
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"""simple docstring""" def snake_case (A_ :str , A_ :bool = False ): '''simple docstring''' if not isinstance(A_ , A_ ): a : Union[str, Any] = f'''Expected string as input, found {type(A_ )}''' raise ValueError(A_ ) if not isins...
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from __future__ import annotations import math def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multi...
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"""simple docstring""" import numpy as np import torch from torch.utils.data import Dataset, IterableDataset from ..utils.generic import ModelOutput class __magic_name__ ( UpperCAmelCase__ ): '''simple docstring''' def __init__( self , _a , _a , _a ): ...
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'''simple docstring''' import multiprocessing from typing import TYPE_CHECKING, Optional, Union from .. import Dataset, Features, config from ..formatting import query_table from ..packaged_modules.sql.sql import Sql from ..utils import logging from .abc import AbstractDatasetInputStream if TYPE...
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'''simple docstring''' class __snake_case: '''simple docstring''' def __init__( self ) -> None: lowerCAmelCase = {} # Mapping from char to TrieNode lowerCAmelCase = False def __snake_case ( self , A_ ) -> None: ...
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from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import KarrasVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class a_ ( _lowercase ): """simple docstring"...
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"""simple docstring""" from dataclasses import dataclass, field from typing import Tuple from ..utils import cached_property, is_tf_available, logging, requires_backends from .benchmark_args_utils import BenchmarkArguments if is_tf_available(): import tensorflow as tf _lowercase = logging.get_logg...
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from pathlib import PurePosixPath from typing import Optional import fsspec from fsspec import AbstractFileSystem from huggingface_hub.hf_api import DatasetInfo from ..utils.file_utils import get_authentication_headers_for_url from ..utils.hub import hf_hub_url class lowercase__ ( _UpperCAmelCase ):...
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from __future__ import annotations from fractions import Fraction from math import gcd, sqrt def _lowercase ( UpperCamelCase_ ) -> bool: '''simple docstring''' SCREAMING_SNAKE_CASE__ = int(number**0.5 ) return number == sq * sq def _lowercase ...
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import gc import random import tempfile import unittest import numpy as np import torch from PIL import Image from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMInverseScheduler, DDIMScheduler, DPMSolverMultistepInverseScheduler, DPM...
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from typing import Optional, Tuple, Union import torch from einops import rearrange, reduce from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput from diffusers.schedulers.s...
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"""simple docstring""" import itertools import string from collections.abc import Generator, Iterable def __SCREAMING_SNAKE_CASE ( A_ , A_ ): lowerCAmelCase__ : Optional[int] = iter(A_ ) while True: lowerCAmelCase__ : Union[str, Any] = tuple(itertools.i...
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"""simple docstring""" import inspect import re from hashlib import shaaaa from typing import Dict, List from .arrow import arrow from .audiofolder import audiofolder from .csv import csv from .imagefolder import imagefolder from .json import json from .pandas import pandas from .parquet import parquet from .sq...
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import argparse import os.path as osp import re import torch from safetensors.torch import load_file, save_file # =================# # UNet Conversion # # =================# _a = [ # (stable-diffusion, HF Diffusers) ('''time_embed.0.weight''', '''time_embedding.linear_1.we...
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import os from typing import List, Optional, Union from ...tokenization_utils import PreTrainedTokenizer from ...tokenization_utils_base import AddedToken from ...utils import logging _lowerCamelCase : Optional[Any] = logging.get_logger(__name__) _lowerCamelCase : Dict = {"voca...
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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...
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"""simple docstring""" import gc import unittest from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline from transformers.pipelines import PipelineException from transformers.testing_utils import ( is_pipeline_test, is_torch_available, ...
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'''simple docstring''' import pytest import requests from datasets.utils.file_utils import http_head from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline @pytest.mark.integration def __A ( ): """simple docstring""" with offline(OfflineSimulationMode.C...
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'''simple docstring''' def __A ( lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" return number | (1 << position) def __A ( lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" return number & ~(1 << position) ...
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"""simple docstring""" def UpperCAmelCase__ ( _snake_case : int , _snake_case : int ) -> str: '''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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"""simple docstring""" from sklearn.metrics import fa_score import datasets a = ''' 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) ''' a = ''' Args: predictions (`list` of ...
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import copy import inspect import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import VideoMAEConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import c...
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from __future__ import annotations import math def UpperCamelCase__( UpperCamelCase__ : int )->bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, ...
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'''simple docstring''' from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_fea...
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'''simple docstring''' import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) imp...
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from scipy.stats import pearsonr, spearmanr from sklearn.metrics import fa_score, matthews_corrcoef import datasets SCREAMING_SNAKE_CASE : Optional[int] = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n a...
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import unittest from transformers import JukeboxTokenizer from transformers.testing_utils import require_torch class _lowerCamelCase( unittest.TestCase ): lowercase_ : Dict = JukeboxTokenizer lowercase_ : Dict = { """artist""": """Zac Brown Band""", """ge...
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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_...
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'''simple docstring''' from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging lowercase =logging.get_logger(__name__) lowercase ={ 'facebook...
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def UpperCamelCase ( __lowerCamelCase : int = 1000 ): snake_case : List[Any] = 2**power snake_case : Any = 0 while n: snake_case , snake_case : Tuple = r + n % 10, n // 10 return r if _...
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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, ) UpperCamelCase = { """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", "...
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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, StableDiffus...
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import math def __lowerCamelCase ( __a :int ) -> bool: """simple docstring""" A__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 ) return exponent == int(__a ) def __lowerCamelCase ( _...
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import time import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from transformers.generation import ( MaxLengthCriteria, ...
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import argparse import json import math import os import time import traceback import zipfile from collections import Counter import requests def lowerCamelCase__ ( _A , _A=None ): '''simple docstring''' snake_case_ = None if token is n...
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"""simple docstring""" import os import re import shutil import sys import tempfile import unittest import black __SCREAMING_SNAKE_CASE =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, "utils")) import check_copies # noqa: E402 ...
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"""simple docstring""" import argparse import tensorflow as tf import torch from transformers import BertConfig, BertForMaskedLM from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertPooler, BertSelfAttention, BertSelfOutput, ) from transforme...
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"""simple docstring""" from collections import defaultdict def UpperCamelCase__ ( lowercase__ : str , lowercase__ : str ): snake_case : List[str] = first_str.lower().strip() snake_case : List[str] = second_str.lower().strip() # Remove ...
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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"...
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase__ = logging.get_logger(__name__) UpperCamelCase__ = { 'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json', } class A ( 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 a...
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"""simple docstring""" import argparse import os import torch from transformers import FlavaImageCodebook, FlavaImageCodebookConfig def lowercase ( _snake_case : Union[str, Any] , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case ...
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"""simple docstring""" import argparse import struct import unittest class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Tuple ,A_ : bytes ) -> None: A = data # Initialize hash values A = [ ...
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from pathlib import Path import cva import numpy as np from matplotlib import pyplot as plt def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> np.ndarray: """simple docstring""" A__ = cva.getAffineTransf...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) _lowerCamelCase : Tuple = { """configuration_speech_to_text""": ["""SPEECH_TO...
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"""simple docstring""" import warnings from ...utils import logging from .image_processing_mobilevit import MobileViTImageProcessor __lowercase = logging.get_logger(__name__) class _A ( _a ): """simple docstring""" def __in...
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"""simple docstring""" class _A : """simple docstring""" def __init__( self : int , __UpperCAmelCase : int): a : Tuple = size a : Dict = [0] * size a : Optional[int] ...
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import copy from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING UpperCAmelCase : int = logging.get_logger(__name__)...
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase : Dict = logging.get_logger(__name__) UpperCAmelCase : Any = { "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json", } class __lowercase ( ...
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"""simple docstring""" import unittest import numpy as np import torch from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad class _UpperCAmelCase ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE (self ): ...
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'''simple docstring''' from __future__ import annotations __lowerCAmelCase = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0] __lowerCAmelCase = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1] def UpperCAmelCase_ (__a : list[float] ): ...
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'''simple docstring''' import colorsys from PIL import Image # type: ignore def _lowerCamelCase ( lowercase : float , lowercase : float , lowercase : int ) -> float: _a = x _a = y for step in range(lo...
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'''simple docstring''' from manim import * class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ): """simple docstring""" def UpperCamelCase__ ( self : Dict ): _a = Rectangle(height=0.5 , width=0.5 ) _a = Rectangle(height=0.46 ...
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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, rescal...
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'''simple docstring''' import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase_ = logging.get_logger(__name__) UpperCamelCase_ = { "microsoft/git-base": "https://huggingface...
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"""simple docstring""" import os from collections import namedtuple import pytest from datasets import ClassLabel, Features, Sequence, Value from datasets.commands.test import TestCommand from datasets.info import DatasetInfo, DatasetInfosDict snake_case_ = namedtuple( """_TestCo...
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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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"""simple docstring""" import unittest from datasets import load_dataset from transformers import BloomTokenizerFast from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class _lowerCamelCase ( a_ , unittest...
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"""simple docstring""" import math def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> int: lowerCAmelCase__ : Any = len(__UpperCAmelCase ) lowerCAmelCase__ : int = int(math.floor(math.sqrt(__UpperCAmelCase ) ) ) ...
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import gc import random import tempfile import unittest import numpy as np import torch from PIL import Image from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMInverseScheduler, DDIMScheduler, DPMSolverMultistepInverseScheduler, DPM...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available lowerCAmelCase__ :List[str] = { '''configuration_x_clip''': [ '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XCLIPConfig''', '''XCLIPTextConfig''', ...
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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 lowerCAmelCase__ = logging.get_logger(__name__) lowerCAmelCase__ = { 'microsoft/beit-base...
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'''simple docstring''' class A__ : def __init__( self :List[Any] ) -> None: '''simple docstring''' _a : dict[str, TrieNode] ={} # Mapping from char to TrieNode _a : List[str] =False ...
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import numpy as np def _SCREAMING_SNAKE_CASE ( lowercase : np.array ): '''simple docstring''' return (2 / (1 + np.exp(-2 * vector ))) - 1 if __name__ == "__main__": import doctest doctest.testmod()
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def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : int ): '''simple docstring''' return [sentence[i : i + ngram_size] for i in range(len(lowercase ) - ngram_size + 1 )] if __name__ == "__main__": from doctest import testmod ...
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'''simple docstring''' import os import re import shutil import sys import tempfile import unittest import black SCREAMING_SNAKE_CASE__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, 'utils')) import che...
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available SCREAMING_SNAKE_CASE__ = { 'configuration_xlm_roberta_xl': [ 'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMRobertaXLCo...
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import os import time import pytest from datasets.utils.filelock import FileLock, Timeout def lowerCamelCase__ ( _lowercase ): '''simple docstring''' UpperCAmelCase_ : int = FileLock(str(tmpdir / '''foo.lock''' ) ) UpperCAmelCase_ : Op...
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import argparse import logging import pickle from collections import Counter logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO ) __a = logging.getLogger(__name__) if __name__ == "__main__": __a = arg...
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import sys import webbrowser import requests from bsa import BeautifulSoup from fake_useragent import UserAgent if __name__ == "__main__": print("Googling.....") _lowerCamelCase ='''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:]) _lowerCamelCase =requests.get(url, heade...
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import unittest from datasets import load_dataset from transformers.pipelines import pipeline from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow @is_pipeline_test @require_torch class __snake_case ( unittest.TestCase ): @require_torch ...
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"""simple docstring""" import numpy as np def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ) -> Optional[Any]: """simple docstring""" _UpperCamelCase : int = int(np.ceil((x_end - xa) / h ) ) _UpperCamelC...
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"""simple docstring""" import webbrowser from sys import argv from urllib.parse import parse_qs, quote import requests from bsa import BeautifulSoup from fake_useragent import UserAgent if __name__ == "__main__": lowerCamelCase__ = "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search...
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from __future__ import annotations import queue class _lowerCAmelCase : def __init__( self , _UpperCamelCase ) -> str: lowerCAmelCase_ = data lowerCAmelCase_ = None lowerCAmelCase_ = None def lowerCamelCase__ ( ): """sim...
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import functools def lowerCamelCase__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ): """simple docstring""" lowerCAmelCase_ = len(__lowerCAmelCase ) lowerCAmelCase_ = len(__lowerCAmelCase ) @functools.cache def min_distance(__lowerCAmelCase : ...
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'''simple docstring''' import logging from dataclasses import dataclass, field from pathlib import Path from typing import Optional, Union from .generation.configuration_utils import GenerationConfig from .training_args import TrainingArguments from .utils import add_start_docstrings _lowe...
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'''simple docstring''' import qiskit def snake_case_ ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ): """simple docstring""" lowercase_ : List[Any] = qiskit.Aer.get_backend('''aer_simulator''' ) # Cre...
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"""simple docstring""" import argparse import json import os import fairseq import torch from torch import nn from transformers import ( SpeechaTextaConfig, SpeechaTextaForCausalLM, SpeechaTextaTokenizer, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFea...
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"""simple docstring""" import argparse import json import os import pickle import shutil import numpy as np import torch from distiller import Distiller from lm_seqs_dataset import LmSeqsDataset from transformers import ( BertConfig, BertForMaskedLM, BertTokenizer, DistilBertConfig, DistilBert...
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available __A : Optional[int] = { 'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'], 'processing_git': ['GitProces...
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'''simple docstring''' import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow if is_torch_available(): import torch from transformers import XLMRobertaModel @require_sentencepiece @requi...
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'''simple docstring''' import colorsys from PIL import Image # type: ignore def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = x UpperCAmelCase__ ...
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'''simple docstring''' import numpy as np import torch from torch.nn import CrossEntropyLoss from transformers import AutoModelForCausalLM, AutoTokenizer import datasets from datasets import logging UpperCAmelCase_ = '\\n\n' UpperCAmelCase_ = '\nPerplexity (PPL) is one of the most common me...
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import re import jax.numpy as jnp from flax.traverse_util import flatten_dict, unflatten_dict from jax.random import PRNGKey from ..utils import logging _UpperCamelCase = logging.get_logger(__name__) def UpperCamelCase_( snake_case__: Dict ) -> Any: UpperCAmelCase__ ...
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import warnings from ...utils import logging from .image_processing_mobilevit import MobileViTImageProcessor _UpperCamelCase = logging.get_logger(__name__) class lowercase ( _UpperCamelCase ): '''simple docstring''' def __init__(self , *__a , **__a ) -> None: ...
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'''simple docstring''' import gc import random import unittest import numpy as np import torch from transformers import ( CLIPImageProcessor, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection, ) from diffusers impo...
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCamelCase__ = { '''configuration_re...
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snake_case : str = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" def lowerCAmelCase_ ( _snake_case : bytes ) -> bytes: '''simple docstring''' if not isinstance(_snake_case , _snake_case ): __magic_name__ : Any = ...
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from __future__ import annotations import unittest from transformers import LEDConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin impor...
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"""simple docstring""" import os import pytest from attr import dataclass _UpperCamelCase : Any = "us-east-1" # defaults region @dataclass class UpperCAmelCase_ : lowerCamelCase__ : str lowerCamelCase__ : List[Any] = "arn:aws:iam::558105141721:role/sagemaker_executi...
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'''simple docstring''' def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] ) -> str: __lowerCamelCase : Tuple = 0 __lowerCamelCase : Optional[int] = len(UpperCAmelCase_ ) for i in range(n - 1 ): for j in range(i + 1 , UpperCA...
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import pytest from datasets.splits import SplitDict, SplitInfo from datasets.utils.py_utils import asdict @pytest.mark.parametrize( "split_dict" , [ SplitDict(), SplitDict({"train": SplitInfo(name="train" , num_bytes=13_37 , num_examples=42 , dataset_n...
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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 AddedToken, PreTrainedTokenizer from ...utils import logging _lowerCamelCase : int = logging.get_logger(__name__) _lowerCamelCas...
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"""simple docstring""" import sys import warnings from os.path import abspath, dirname, join # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. lowercase__ : Dict = a...
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'''simple docstring''' from __future__ import annotations import math from collections.abc import Callable def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = 100 ,) -> float: __lowerCamelCase : Dict ...
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"""simple docstring""" import unittest from parameterized import parameterized from transformers import OpenLlamaConfig, is_torch_available, set_seed from transformers.testing_utils import require_torch, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configurati...
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"""simple docstring""" import argparse import json from typing import List from ltp import LTP from transformers import BertTokenizer def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any: '''simple docstring''' if ( (cp >= 0x4e00 and cp <= 0x9fff) ...
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def __UpperCAmelCase ( __a : int ) -> int: """simple docstring""" assert isinstance(__a ,__a ), F"""The input value of [n={number}] is not an integer""" if number == 1: return 2 elif number < 1: _a : Optional[Any] = F"""The input value...
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import json import os import unittest from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin ...
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import gc import unittest from transformers import CTRLConfig, 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...
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from __future__ import annotations def lowercase__ ( __snake_case : list[int] , __snake_case : int ): '''simple docstring''' if len(__snake_case ) < k or k < 0: raise ValueError('Invalid Input' ) UpperCAmelCase_ : int ...
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import tempfile import unittest import numpy as np from huggingface_hub import HfFolder, delete_repo from requests.exceptions import HTTPError from transformers import BertConfig, is_flax_available from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax if is_flax_avai...
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import torch from transformers import AutoModel class __lowerCamelCase (torch.nn.Module ): def __init__( self: Union[str, Any],A_: Tuple="sayef/fsner-bert-base-uncased" ): '''simple docstring''' super(A_,self...
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import random from .binary_exp_mod import bin_exp_mod def lowerCamelCase_ ( lowerCAmelCase: Dict , lowerCAmelCase: Dict=10_00 )-> Union[str, Any]: if n < 2: return False if n % 2 == 0: return n == 2 # this means n is odd _snake_case : str = n - 1 ...
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import itertools import json import os import unittest from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common impor...
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"""simple docstring""" from __future__ import annotations def __lowercase ( _a , _a , _a , ): if (stress, tangential_force, area).count(0 ) != 1: raise ValueError('''You cannot supply more or less than 2 values''' ) elif stress < 0: raise ValueError('''Stress cann...
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"""simple docstring""" import pickle import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_pro...
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__UpperCAmelCase = [ (1_000, """M"""), (900, """CM"""), (500, """D"""), (400, """CD"""), (100, """C"""), (90, """XC"""), (50, """L"""), (40, """XL"""), (10, """X"""), (9, """IX"""), (5, """V"""), (4, """IV"""), (1, """I"""), ] def...
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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(): ...
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"""simple docstring""" import os import unittest from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer from transformers.testing_utils import require_jieba, tooslow from ...test_tokenization_common import TokenizerTesterMixin @require_jieba class snake_case (...
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from decimal import Decimal, getcontext from math import ceil, factorial def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ): if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): raise TypeError('''Undefined for non-integers''' ) elif pre...
8
0
"""simple docstring""" def UpperCAmelCase__ (snake_case__ : int ): """simple docstring""" stooge(snake_case__ , 0 , len(snake_case__ ) - 1 ) return arr def UpperCAmelCase__ (snake_case__ : Optional[Any] , snake_case__ : str , snake_cas...
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"""simple docstring""" def UpperCAmelCase__ (snake_case__ : int ): """simple docstring""" _snake_case : Optional[Any] = (1 + 24 * n) ** 0.5 return ((1 + root) / 6) % 1 == 0 def UpperCAmelCase__ (snake_case__ : int = 50_00 ): """simple docstring""" ...
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"""simple docstring""" import re import jax.numpy as jnp from flax.traverse_util import flatten_dict, unflatten_dict from jax.random import PRNGKey from ..utils import logging SCREAMING_SNAKE_CASE_ : Tuple = logging.get_logger(__name__) def _snake_case ( Upp...
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"""simple docstring""" import math def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ): if initial_intensity < 0: raise ValueError("""The value of intensity cannot be negative""" ) # handling of negative values of ini...
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'''simple docstring''' def _A ( snake_case = 50_00_00_00 ) -> int: _lowercase : List[Any] = set() _lowercase : Optional[int] = int((limit - 24) ** (1 / 2) ) _lowercase : int = set(range(3 , prime_square_lim...
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'''simple docstring''' import os import sys import unittest _snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, 'utils')) import check_dummies # noqa: E402 from check_dummies import create_dummy_files, create_dummy_object,...
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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 ImageProcessingSavingTestMixi...
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'''simple docstring''' from __future__ import annotations import copy import inspect import json import math import os import tempfile import unittest from importlib import import_module import numpy as np from transformers import ViTMAEConfig from transformers.file_utils i...
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import os import posixpath import uuid from dataclasses import dataclass from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union import numpy as np import pyarrow as pa import datasets from datasets.arrow_writer import ArrowWriter, ParquetWriter from datasets.config import MAX_SHARD_SIZE from data...
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import argparse import csv import logging import os import random import numpy as np import torch from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from tqdm import tqdm, trange from transformers import ( CONFIG_NAME, WEIGHTS_NAME, AdamW, Ope...
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"""simple docstring""" import inspect import unittest from transformers import MobileViTConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration...
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"""simple docstring""" 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...
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import argparse import json import os import pickle import shutil import numpy as np import torch from distiller import Distiller from lm_seqs_dataset import LmSeqsDataset from transformers import ( BertConfig, BertForMaskedLM, BertTokenizer, DistilBertConfig, DistilBertForMaskedLM, DistilBer...
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import unittest import numpy as np import torch from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class lowerCAmelCase__ ( unittest.TestCase )...
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'''simple docstring''' from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available A__: str = { '''configuration_informer''': [ '''INFORMER_PRETRAINED_CONFIG_ARCHIV...
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'''simple docstring''' import logging import os from dataclasses import dataclass from enum import Enum from typing import List, Optional, Union from filelock import FileLock from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available A__: int = ...
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'''simple docstring''' from math import isqrt, loga def UpperCAmelCase_ ( __lowercase : int ) -> list[int]: '''simple docstring''' _UpperCAmelCase = [True] * max_number for i in range(2 , isqrt(max_number - 1 ) + 1 ): if is_prime[i]:...
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'''simple docstring''' import copy import inspect import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import VideoMAEConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_vision, slow, torch_device fr...
156
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'''simple docstring''' import time import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from transformers.generation import ( MaxLen...
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'''simple docstring''' import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...utils import logging __a = logging.get_logger(__name__) __a = { 'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/b...
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import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDC...
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def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ): if height >= 1: move_tower(height - 1 , __magic_name__ , __magic_name__ , __magic_name__ ) move_disk(__magic_name__ , __magic_name__ ) move_tower(height - 1 , __magic_name__ , __magic_name__ , __ma...
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"""simple docstring""" import argparse from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird from transformers.utils import logging logging.set_verbosity_info() def lowercase ( __snake_case : Dict , ...
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"""simple docstring""" import os from typing import BinaryIO, Optional, Union import numpy as np import pyarrow.parquet as pq from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config from ..features.features import FeatureType, _visit from ..formatting import query_table from .....
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import copy import os import tempfile from unittest import TestCase from unittest.mock import patch import numpy as np import pyarrow as pa import pyarrow.parquet as pq import pytest from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence from datase...
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from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Features, Value from .base import TaskTemplate @dataclass(frozen=UpperCAmelCase ) class SCREAMING_SNAKE_CASE (UpperCAmelCase ): _UpperCamelCase : str = field(default='language-m...
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'''simple docstring''' import argparse import shutil import time from json import JSONDecodeError from logging import getLogger from pathlib import Path from typing import Dict, List import torch from torch.utils.data import DataLoader from tqdm import tqdm from transformers import AutoModelForSeqa...
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'''simple docstring''' from __future__ import annotations def A_ ( snake_case , snake_case , snake_case , ): if (stress, tangential_force, area).count(0 ) != 1: raise ValueError("You cannot supply more or less than 2 values" ) elif stress < 0...
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import fire from utils import calculate_rouge, save_json def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , **_UpperCamelCase ) -> Optional[int]: """simple docstring""" _snake_case = [x.strip() for x in open(_UpperCAmelCase ).readlines()]...
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import warnings from ...utils import logging from .image_processing_deformable_detr import DeformableDetrImageProcessor __A = logging.get_logger(__name__) class lowercase_ ( __lowercase ): def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ...
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"""simple docstring""" from math import isqrt, loga def _lowercase ( __lowerCAmelCase ) -> list[int]: SCREAMING_SNAKE_CASE__ : Any = [True] * max_number for i in range(2 , isqrt(max_number - 1 ) + 1 ): if is_prime[i]: ...
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"""simple docstring""" from __future__ import annotations from typing import TypedDict class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :str _SCREAMING_SNAKE_CASE :int def _lowercase ( __lowerCAmelCase ) -> list[str]: if not ...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available __UpperCamelCase = { "configuration_instructblip": [ "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "InstructBlipConfig", "InstructBlipQFormerConfig", "In...
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import inspect from typing import Callable, List, Optional, Union import torch from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, WhisperForConditionalGeneration, WhisperProcessor, ) from diffusers import ( AutoencoderKL, DDIMScheduler, DiffusionPipeline, ...
51
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"""simple docstring""" import itertools import random import unittest import numpy as np from transformers import is_speech_available from transformers.testing_utils import require_torch, require_torchaudio from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_speech_...
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import unittest from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTeste...
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'''simple docstring''' from .configuration_bert_masked import MaskedBertConfig from .modeling_bert_masked import ( MaskedBertForMultipleChoice, MaskedBertForQuestionAnswering, MaskedBertForSequenceClassification, MaskedBertForTokenClassification, MaskedBertModel, ) from .m...
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'''simple docstring''' import colorsys from PIL import Image # type: ignore def _A (lowerCAmelCase__ :float , lowerCAmelCase__ :float , lowerCAmelCase__ :int ) -> float: '''simple docstring''' _a = x _a ...
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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/licenses/LICENSE-2.0 # ...
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging __lowerCamelCase = logging.get_logger(__name__) __lowerCamelCase = { '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.js...
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'''simple docstring''' class a__ : def __init__( self : int , a : Any , a : Any , a : Union[str, Any] ): """simple docstring""" __lowerCamelCase = name __lowerCamelCase = value __lowerCamelCase = weight...
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'''simple docstring''' import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging __UpperCAmelCase =logging.get_logger(__name__) __UpperCAmelCase ={ "microsoft/unispeech-sat-base-100h-libri-ft": ( "https://huggingface.co/mi...
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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_ )-> str: # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wik...
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def lowerCAmelCase ( lowerCAmelCase_ = 10**9 )-> int: lowerCAmelCase_ : List[Any] = 1 lowerCAmelCase_ : Optional[int] = 2 lowerCAmelCase_ : Any = 0 lowerCAmelCase_ : str = 0 lowerCAmelCase_ : str = 0 whi...
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import json import os from collections import Counter import torch import torchvision import torchvision.transforms as transforms from PIL import Image from torch import nn from torch.utils.data import Dataset lowercase_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (...
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import sys lowercase_ = ( """73167176531330624919225119674426574742355349194934""" """96983520312774506326239578318016984801869478851843""" """85861560789112949495459501737958331952853208805511""" """12540698747158523863050715693290963295227443043557""" """6689664895044...
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