code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a = (7_2_0, 1_2_8_0) # Height, Width
a = (0.4, 0.6) # if height or width lower than this scale, drop it.
a = 1 / 1_0_0
a = ''
a = ''
a = ''
a ... | 412 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Tuple = {
'configuration_distilbert': [
... | 460 | 0 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from... | 715 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""vocab_file""": """vocab.json""",
... | 150 | 0 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__lo... | 288 |
'''simple docstring'''
import os
import sys
import unittest
__lowerCamelCase = 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, c... | 288 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowerCamelCase = Lock()
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ,... | 307 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {'vocab_file': 'vocab.txt'}
__lowerCamelCase = {
... | 307 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_sta... | 42 | '''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... | 427 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()... | 705 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCas... | 81 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowercase = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_lowercase = _LazyModule(__... | 356 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_tenso... | 356 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.ut... | 720 | from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( A ):
UpperCamelCase__ , UpperCamelCase__ = analyze_text(A )
UpperCamelCase__ = list(''' '''... | 469 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __lowerCAmelCase ( a_ ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Uni... | 251 | '''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizer... | 251 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warm... | 700 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
f... | 68 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : List[str] = logging.get_logger(__name__)
_A : List[Any] = {
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/... | 361 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : str = logging.get_logger(__name__)
_A : Tuple = {
"""goog... | 361 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__... | 117 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def __lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : int ) -> tuple[complex, complex]:
"""simple docstring"""
if a == 0:
raise... | 117 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
def wrapper(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Union[str, Any]... | 669 |
# 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 A_ ( __U... | 669 | 1 |
from torch import nn
def a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "ge... | 643 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : int = "▁"... | 643 | 1 |
"""simple docstring"""
from manim import *
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : int = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase : ... | 160 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import It... | 160 | 1 |
'''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.
SCREAMING_SNAKE_CASE__ = ... | 700 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : List[str] ): # noqa: E741
'''simple docstring'''
lowercase__ = len(_snake_case )
lowercase__ = 0
lowercase__ = [0] * n
lowercase__ ... | 539 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_obje... | 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 36 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Dict = {
'''configuration_roformer''': [''... | 330 | '''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...tes... | 330 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int, UpperCAmelCase_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
A__ = str(... | 104 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__na... | 104 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 567 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( _lowercase ):
"""simple docstring"""
UpperCAmelCase__ = ["image_processor", "tokenizer"]
UpperCAmelCase__ = "AutoImageProcessor"
UpperCAmelCase... | 567 | 1 |
'''simple docstring'''
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.conf... | 427 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(snake_case_ , snake_case_ ):
return 0
elif n == 2:
return 1
else:
__lowerCAmelCase = [0, 1]
for i in range(2 , n + 1 ):
se... | 427 | 1 |
"""simple docstring"""
def _lowerCamelCase ( __a = 10 ):
if not isinstance(__a, __a ) or n < 0:
raise ValueError('''Invalid input''' )
SCREAMING_SNAKE_CASE_ = 10**n
SCREAMING_SNAKE_CASE_ = 28_433 * (pow(2, 7_830_457, __a )) + 1
return str(number % modulu... | 715 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case ( __lowercase , unittest.TestCase ):
UpperCAmelC... | 628 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet impo... | 445 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
fr... | 445 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
... | 629 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
SCREAMING_SNAKE_CASE__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 629 | 1 |
'''simple docstring'''
import math
def _UpperCAmelCase ( _lowerCamelCase : int ) -> bool:
assert isinstance(_lowerCamelCase , _lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif ... | 384 |
'''simple docstring'''
class a_ :
def __init__( self , snake_case_ ):
_lowerCAmelCase : Tuple = len(snake_case_ )
_lowerCAmelCase : Optional[int] = [0] * len_array
if len_array > 0:
_lowerCAmelCase : Dict = array[0]... | 384 | 1 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttenti... | 160 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCAmelCase = datasets.logging.get_logger(__name__)
_lowerCAmelCase = '''\
@InProceedings{moosavi2019m... | 160 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCAmelCase_ ( un... | 79 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Union[str, Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""... | 349 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=a__ ):
__UpperCAmelCase = ['torch']
def __init__( self , *a , **a ):
requires_backends(self , ["torch"] )
@classmetho... | 701 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
a__ : Any = logging.get_logger(__name__)
class lowercase_ ( a__ ):
def __init__( self , *a , **a ):
warnings.warn(
... | 223 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 84 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 84 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : List[Any] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise ... | 486 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate imp... | 486 | 1 |
'''simple docstring'''
UpperCamelCase_ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .f... | 28 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : str = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
... | 348 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConf... | 705 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsof... | 347 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import... | 611 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class a ( unittest.TestCase , __UpperCAmelC... | 611 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
_A = l... | 682 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Confi... | 682 | 1 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
a = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''',... | 7 | """simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)... | 516 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@requir... | 293 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
UpperCamelCase : Union[str, Any] = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase : Union... | 293 | 1 |
import datasets
__UpperCAmelCase = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and S... | 40 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowerCAmelCase_ : Optional[Any] = datasets.logging.get_logger(__name__)
lowerCAmelCase_ : ... | 692 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case () -> Dict:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT):
with pyte... | 712 |
from math import isqrt, loga
def _snake_case (_snake_case : int) -> list[int]:
_lowercase =[True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _snake_case , _snake_case):
... | 557 | 0 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
impo... | 3 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 3 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_lowercase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), ... | 96 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _A (UpperCamelCase : str ) ->None:
'''simple docstring'''
lowerCamelCase__ ,lowerCamelCase__ : List[str] = analyze_text(UpperCamelCase )
l... | 96 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.toke... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Optional[int] = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 615 | 1 |
import os
import sys
import unittest
UpperCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_... | 677 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
Uppe... | 677 | 1 |
import math
import sys
import cva
import numpy as np
def __a ( lowerCAmelCase_ : np.ndarray ,lowerCAmelCase_ : float ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase_= math.sqrt(lowerCAmelCase_ )
UpperCAmelCase_= 1 / (sigma * math.sqrt(2 * ma... | 593 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils impor... | 593 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , ar... | 530 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise V... | 530 | 1 |
import os
import unicodedata
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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase ... | 699 |
lowerCamelCase_ : Tuple = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Expo... | 548 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCAmelCase_ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try:
if not i... | 519 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 519 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, flo... | 119 |
"""simple docstring"""
lowerCAmelCase_ = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kil... | 560 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms impo... | 718 |
'''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
__A : Optional[Any] = logging.get_logger(__name__)
__A... | 187 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import BackboneTest... | 37 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'junnyu/roformer_... | 485 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
lowerCamelCase__ : str = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvaila... | 495 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowerCamelCase__ : Dict = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author... | 495 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__magic_name__ : Optional[Any] = logging.get_logger(__name__)
class __snake_case (lowerCamelCase ):
def __init__( self: T... | 281 |
"""simple docstring"""
from collections.abc import Callable
def a_ ( lowercase__ :Callable[[float], float], lowercase__ :float, lowercase__ :float ):
__lowerCamelCase = a
__lowerCamelCase = b
if function(lowercase__ ) == 0: # one of th... | 281 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : str = {
"configurati... | 711 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_ta... | 39 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
SCREAMING_SN... | 582 |
'''simple docstring'''
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 ... | 582 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__SCREAMING_SNAKE_CASE = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the le... | 703 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =8.9_8_8E9 # units = N * m^s * C^-2
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
__SCREAMING_SNAKE_CASE = abs(c... | 690 | 0 |
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 transformers.... | 402 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | 0 |
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
A : Optional[Any] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def __UpperCamelCase ( _lowerCAmelCase = 5000 ) -> int:
"""simple docstring"""
A : in... | 520 |
from __future__ import annotations
from math import gcd
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase = 2 , _lowerCAmelCase = 1 , _lowerCAmelCase = 3 , ) -> int | None:
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot b... | 520 | 1 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase_( _A :str = "AAPL" )-> str:
UpperCamelCase__ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
UpperCamelCase__ = BeautifulSoup(requests.get(_A ).text , "html.parser" )
UpperCamelCase__ ... | 551 |
__UpperCamelCase = 2_5_6
# Modulus to hash a string
__UpperCamelCase = 1_0_0_0_0_0_3
def UpperCamelCase_( _A :str , _A :str )-> bool:
UpperCamelCase__ = len(_A )
UpperCamelCase__ = len(_A )
if p_len > t_len:
return False
UpperCamelCa... | 551 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A = TypeVar("""T""")
class _UpperCamelCase ( Generic[T] ):
"""simple docstring"""
snake_case_ = 4_2 # Cache store of keys
snake_case_ = ... | 721 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100... | 147 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtrac... | 324 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class snake_case__ :
lowercase__ : int
lowercase__ : int
class snake_case__ :
def __init__( self , lowerCAmelCase__ ) -> D... | 324 | 1 |
def UpperCamelCase__ ( A__ , A__ , A__ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case__ : Dict = _modexpt(A__ , exponent // 2 , A__ ) % modulo_value
return (x * x) % modulo_value
... | 699 | import numpy as np
import qiskit
def UpperCamelCase__ ( A__ = 8 , A__ = None ) -> str:
snake_case__ : Optional[int] = np.random.default_rng(seed=A__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
sn... | 699 | 1 |
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
lowercase__ =logging.get_logger(__name__)
lowercase__ ='▁'
lowercase__ ={'vo... | 521 |
import numpy as np
from transformers import Pipeline
def __UpperCamelCase ( lowerCAmelCase__ : Tuple ):
__a : Union[str, Any] = np.max(lowerCAmelCase__ , axis=-1 , keepdims=lowerCAmelCase__ )
__a : List[Any] = np.exp(outputs - maxes )... | 521 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params i... | 705 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : Tuple = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any ... | 157 | 0 |
from string import ascii_uppercase
lowerCAmelCase__ = {str(ord(c) - 55): c for c in ascii_uppercase}
def _UpperCAmelCase (UpperCamelCase__ : int , UpperCamelCase__ : int ):
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise TypeError(... | 503 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = """▁"""
lowercase ... | 240 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase__ ( __lowerCamelCase : int ):
'''simple docstring'''
_UpperCAmelCase : int =int(number**0.5 )
return number =... | 716 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/confi... | 331 | 0 |
'''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE_: Optional[Any] =1.6_0_2_1E-1_9 # units = C
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ) -> int:
'''simple docst... | 78 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import... | 297 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common im... | 717 |
"""simple docstring"""
def snake_case ( ) -> Tuple:
_snake_case = 0
for i in range(1 , 1001 ):
total += i**i
return str(lowerCAmelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 404 | 0 |
'''simple docstring'''
import string
from math import logaa
def lowercase__ ( __lowercase : str , __lowercase : str ) -> Dict:
"""simple docstring"""
__UpperCamelCase = document.translate(
str.maketrans('' , '' , string.punctua... | 399 |
import argparse
import os
import re
__lowerCamelCase : int = """src/diffusers"""
# Pattern that looks at the indentation in a line.
__lowerCamelCase : List[str] = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCamelCase : Union[str, Any] =... | 297 | 0 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 65 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_ten... | 65 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CO... | 409 | from itertools import permutations
def a__ ( __UpperCamelCase ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAKE_CASE_ = [7, 1_1, 1_3, 1_7]
for i, tes... | 140 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_avail... | 711 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if not isinstance(__a, __a ):
SCREAMING_SNAKE_CASE_ = F'Input value of [number={number}] must be an integer'
raise TypeError(__a )
if number < 1:
SCREAMING_SNAKE_CASE_ = F'Input value of [number={number}] must b... | 628 | 0 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
return int(input_a == input_a == 0 )
def A ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
p... | 45 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Optional[int] = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"configuration_maskformer... | 709 |
from __future__ import annotations
def lowerCAmelCase ( _lowerCAmelCase : int = 4 ):
"""simple docstring"""
UpperCAmelCase__ = abs(_lowerCAmelCase ) or 4
return [[1 + x + y * row_size for x in range(_lowerCAmelCase )] for y in range(_lowerCAmelCase )]
def lower... | 364 | 0 |
from ...configuration_utils import PretrainedConfig
_A : Dict = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tapas-bas... | 315 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avail... | 315 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor,... | 704 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
a_ = logging.getLogger()
def _a ( UpperCamelCase_ : Union[str, An... | 115 | 0 |
"""simple docstring"""
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 to... | 259 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e im... | 259 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
re... | 720 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
A_ = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL... | 465 | 0 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def a_ (__A , __A , __A ) -> List[str]:
"""simple docstring"""
__a : Any = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обуче... | 351 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.... | 351 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 702 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizati... | 190 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
"""simple docstring"""
return len(set(SCREAMING_SNAKE_CASE_ ) ) == len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctes... | 51 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acceler... | 228 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_t... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class ... | 665 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class lowerCamelCase_ :
a__ = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''} )
a__ = field(
de... | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
... | 117 | 0 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowerCamelCase__ : Dict[str, torch.Tensor] ):
_a = []
_a = []
_a ... | 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : int = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],... | 691 | 0 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import Pretraine... | 532 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acce... | 532 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_co... | 321 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 321 | 1 |
def _a ( __lowercase ) -> str:
"""simple docstring"""
__UpperCamelCase = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _a ( __lowe... | 383 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
f... | 383 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversatio... | 299 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
UpperCAmelCase : int = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://hu... | 299 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Trai... | 695 | import random
class _lowercase :
"""simple docstring"""
@staticmethod
def _UpperCAmelCase ( UpperCAmelCase ):
'''simple docstring'''
_lowercase = [ord(UpperCAmelCase ) for i in text]
_lowercase = ... | 398 | 0 |
def _UpperCAmelCase ( UpperCamelCase: int , UpperCamelCase: Optional[Any] ):
"""simple docstring"""
__lowerCAmelCase = 0
__lowerCAmelCase = len(UpperCamelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[lef... | 376 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedB... | 376 | 1 |
class a_ :
def __init__( self , SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = size
SCREAMING_SNAKE_CASE_ = [0] * size
SCREAMING_SNAKE_CASE_ = [0] * size
@staticmethod
def ... | 205 | import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_configurati... | 417 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = {
'facebook/xmod-ba... | 34 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Tuple = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer'],
}
try:
... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : str ={
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'... | 101 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...util... | 290 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowercase_ (A : bytes , A : int ):
snake_case__ : int = F'''{sampling_rate}'''
snake_case__ : Union[str, Any] = '1'
sna... | 243 |
def lowercase_ (A : list[int] ):
snake_case__ : Tuple = []
if len(A ) == 1:
return [nums.copy()]
for _ in range(len(A ) ):
snake_case__ : str = nums.pop(0 )
snake_case__ : Optional[int] ... | 243 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTe... | 25 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chi... | 25 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__l... | 713 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def SCREAMING_SNAKE_CASE ( snake_case_ : dict )... | 25 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class snake_case_ ( unittest.TestCase ):
"""simple docstr... | 34 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( _A ):
'''simple docstr... | 529 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= ['image_processor', 'tokenizer']
A__= 'AutoImageProcessor'
A__= 'AutoTokenizer'
def __init__( self : Union[... | 719 |
from __future__ import annotations
import math
def __UpperCAmelCase ( __A , __A ) -> float:
'''simple docstring'''
UpperCAmelCase__ = u
for i in range(1 , __A ):
UpperCAmelCase__ = temp * (... | 277 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str ... | 613 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : Any = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _lowercase ( UpperCAmelCase__ ):
... | 613 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch_available():
raise... | 650 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 1 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
UpperCAmelCase__ =datasets.logging.get_logger(__name__)
UpperCAmelCase__ ="\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},... | 616 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available... | 616 | 1 |
def __UpperCamelCase ( snake_case = 1_0_0_0 ) -> int:
'''simple docstring'''
__A , __A = 1, 1
__A = 2
while True:
__A = 0
__A = fa + fa
__A , __A = fa, f
index += 1
for _ in s... | 341 |
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , UpperCAmelCase )-> None:
__A = len(UpperCAmelCase )
__A = [0] * len_array
if len_array > 0:
__A = array[0]
for i in range(1 , Upper... | 341 | 1 |
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