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 |
|---|---|---|---|---|
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def A ( __snake_case: List[Any] , __snake_case: bool = True , __snake_case: float = math.inf , __snake_case: float = -math.inf , __snake_case: float = ... | 545 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __lowerCAmelCase ( a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_... | 283 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
_lowercase = TypeVar("""_T""")
class UpperCamelCase__( Generic[_T] ):
def __init__( self : Dict , lowerCAmelCase : Tuple = None )-> Optional[int]:
"""simple d... | 707 |
'''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,
DistilBe... | 50 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_lowerCAmelCase = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to c... | 264 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDCon... | 465 | 0 |
"""simple docstring"""
from manim import *
class _UpperCAmelCase ( __UpperCAmelCase ):
def a_ ( self ) -> Tuple:
UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase = Rectangle... | 716 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _UpperCAmelCase ( SCREAMING_SNAKE_CASE_ ):
@staticmethod
@abstractmethod
def a_ ( lowercase_ ) -> Optional[Any]:
raise NotIm... | 183 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCamelCase (_UpperCAmelCase ):
__A = ['''image_processor''', '''tokenizer''']
__A = '''CLIPImageProcessor'''
__A ... | 588 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : str = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
... | 588 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSeq... | 692 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.te... | 692 | 1 |
def _SCREAMING_SNAKE_CASE ( a = 1_00_00_00 ) -> int:
__A : str = limit + 1
__A : Any = [0] * limit
for first_term in range(1 , a ):
for n in range(a , a , a ):
__A : Dict = first_term ... | 239 |
def _SCREAMING_SNAKE_CASE ( a ) -> list:
if len(a ) <= 1:
return lst
__A : Any = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__A , __A : str = lst[i]... | 239 | 1 |
from __future__ import annotations
from random import choice
def lowerCAmelCase__ ( lowerCamelCase_ : str):
'''simple docstring'''
return choice(lowerCamelCase_)
def lowerCAmelCase__ ( lowerCamelCase_ : list[int] ,lowerCamelCase_ : int):
'''simple docstring'''
... | 90 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__snake_case : Optional[int] =(3, 9, -1_1, 0, 7, 5, 1, -1)
__snake_case : str =(4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class lowerCamelCase__ :
'''simpl... | 90 | 1 |
from __future__ import annotations
def UpperCamelCase ( _UpperCAmelCase : int ) -> list[int]:
'''simple docstring'''
_lowercase : Optional[Any] = [True] * limit
_lowercase : List[str] = False
_lowercase : Tuple = ... | 461 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
UpperCamelCase_ : List[Any] = 3
def UpperCamelCase ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
print("Generating primitive root of p... | 461 | 1 |
import json
import pathlib
import unittest
import numpy as np
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 ImageProcessingSavingTestMixin, prepare_imag... | 535 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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_availab... | 535 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ) ... | 71 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/bl... | 327 | 0 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_... | 160 |
'''simple docstring'''
from math import factorial
_lowerCAmelCase = {str(d): factorial(d) for d in range(10)}
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCamelCase ) )
def _SCREAMING_SNAKE_CA... | 160 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import requir... | 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 __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_t... | 540 |
'''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 AutoModelForSeqaSeqLM, AutoToken... | 540 | 1 |
def _UpperCAmelCase ( UpperCamelCase: float , UpperCamelCase: float ):
"""simple docstring"""
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__ma... | 611 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@req... | 611 | 1 |
def __lowerCamelCase ( A__ ) -> list:
"""simple docstring"""
if any(not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(lowerC... | 706 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...... | 324 | 0 |
"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 265 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''KEY''')
lowerCAmelCase__ = TypeVar('''VAL''')
@dataclass(frozen=lowerCamelCase__ , slots=lowerCamelCase... | 41 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autoforme... | 701 |
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
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase... | 407 | 0 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase_ ( _lowerCamelCase: List[str] , _lowerCamelCase: List[str] , _lowerCamelCase: List[str] , _lowerCamelCase: List[str]=10_24 ):... | 578 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 578 | 1 |
"""simple docstring"""
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
a_ = logging.get_logger(__name__)
a_ = ... | 716 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a_ = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
... | 523 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import night... | 558 | import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_UpperCAmelCase = collections.namedtuple("""_Datasets""", ["""t... | 558 | 1 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
... | 707 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE =1.0_5_4_5_7_1_8_1_7e-3_4 # unit of ℏ : J * s
__SCREAMING_SNAKE_CASE =3e8 # unit of c : m * s^-1
def a ... | 89 | 0 |
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 ( __A ):
UpperCamelC... | 21 | def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : List[Any] ,__lowercase : Tuple ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__lowercase ,n - 1 ,__lowercase ) * a) % mod
else:
A_ ... | 558 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, t... | 713 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 292 | 0 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> int:
"""simple docstring"""
if not isinstance(lowercase_ ,lowercase_ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be... | 624 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 624 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def _snake_case ( A = None ) -> str:
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
lowerCAmelCase__ = nums[0]
for i i... | 721 |
'''simple docstring'''
import numpy as np
import qiskit
def _snake_case ( A = 8 , A = None ) -> str:
lowerCAmelCase__ = np.random.default_rng(seed=A )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
... | 98 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import... | 38 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...te... | 67 | 0 |
def UpperCAmelCase ( UpperCamelCase__ ) -> None:
'''simple docstring'''
__lowerCAmelCase = generate_pascal_triangle(UpperCamelCase__ )
for row_idx in range(UpperCamelCase__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ... | 715 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A : Tuple = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller... | 334 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassifica... | 141 | 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,
)
import transformers
from transformers impor... | 576 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta... | 160 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_lowerCAmelCase = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-lar... | 160 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
A = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
... | 125 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_N... | 125 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase__ = {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/... | 716 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_avai... | 639 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {
'ksst... | 226 |
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 impo... | 226 | 1 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _lowerCamelCase( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple=5 ):
'''simple docstring'''
assert... | 97 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _lowerCamelCase( lowerCAmelCase__ : Optional[Any] ):
'''simple docstring'''
if "cls_token" in name:
SCREAMING_S... | 97 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_ava... | 68 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
snake_case_ : Optional[int] = l... | 138 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( __A : list[int] ):
'''simple docstring'''
return len(set(__A ) ) == len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod() | 692 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
__UpperCAmelCase = 6378137.0
__UpperCAmelCase = 6356752.314245
__UpperCAmelCase = 6_378_137
def lowerCAmelCase_ ( __A : float , __A : float , __A : float ... | 692 | 1 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_lowerCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_:
'''simple docs... | 565 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""... | 676 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vis... | 522 | from __future__ import annotations
__a : str = """Muhammad Umer Farooq"""
__a : Optional[Any] = """MIT"""
__a : int = """1.0.0"""
__a : Optional[int] = """Muhammad Umer Farooq"""
__a : Dict = """contact@muhammadumerfaro... | 522 | 1 |
'''simple docstring'''
a_ : Union[str, Any] = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLanguages""",
... | 676 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : int = 'opena... | 319 | 0 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__A : Optional[int] = logging.get_logger('transformers.models.speecht5')
def __UpperCamelCase ( _A : List[str] , _A : ... | 75 |
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 : Any = logging.get_logger(__name__)
__A : Dict = {
'hustvl/yolos-small': 'https... | 75 | 1 |
'''simple docstring'''
def UpperCamelCase__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = False ) -> str:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
snake_case__ : Optional[int] = f"Expected string as input, fo... | 270 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_ = "."
# Internal TensorFlow ops... | 270 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : Any = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at ht... | 715 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 | 0 |
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,
)
_SCREAMING_SNAKE_CASE = {'configuration_mbart': ['MBART_PRETRAINED_CON... | 537 | """simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a : int = get_tests_dir('fixtures/test_sentencepiece_... | 213 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case_ ( ):
'''simple docstring'''
from torch.utils.cpp_extension import load
_lowerCamelCase : Optional[Any] = Path(A_ ).resolve().parent.parent.paren... | 714 |
"""simple docstring"""
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : list[list[int]] = [[0 for _ in range(A_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_lowerCamelCase : Union[str, Any] ... | 598 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCamelCase( SCREAMING_SNAKE_CASE_ = "isbn/0140328726" ) -> dict:
A_ = olid.strip().strip('/' ) # Remove leading/trailing whitespace & slashes
... | 366 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( lowerCAmelCase ,unittest.TestCase ):
'''simple docstring... | 366 | 1 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, Inpu... | 713 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( _UpperCAmelCase : str = "base_exp.txt" ) -> int:
__snake_case = 0
__snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase )... | 680 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import req... | 52 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
__a : Optional[int] ... | 52 | 1 |
"""simple docstring"""
def a ( __UpperCAmelCase : str ) -> list:
__magic_name__: Union[str, Any] = [0] * len(__UpperCAmelCase )
for i in range(1 , len(__UpperCAmelCase ) ):
# use last results for bette... | 213 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = "ClapFeatureExtractor"
UpperCAmelCase__ = ("RobertaTokenizer", "Rob... | 213 | 1 |
"""simple docstring"""
import pprint
import requests
_UpperCamelCase = 'https://zenquotes.io/api'
def lowerCAmelCase_ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def low... | 179 |
import unittest
from knapsack import greedy_knapsack as kp
class A_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self: List[Any] ):
__lowerCamelCase : str = [10, 20, 30, 40, 50, 60]
__lowerCamelCase : List... | 669 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Tuple = TypeVar("""T""")
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE ):
return (position - 1) // ... | 708 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE_ :List[str] = AutoConfig.from_pretraine... | 233 | 0 |
"""simple docstring"""
import torch
def lowercase ():
if torch.cuda.is_available():
__lowerCAmelCase = torch.cuda.device_count()
else:
__lowerCAmelCase = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main... | 465 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/conf... | 465 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case ( lowercase__ : List[str] ... | 720 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/w... | 256 | 0 |
'''simple docstring'''
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 a ( unittest.Tes... | 640 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Optional[Any] = Down... | 43 | 0 |
"""simple docstring"""
def _A (__a ) -> Optional[int]: # noqa: E741
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = len(__a )
SCREAMING_SNAKE_CASE_ : str = 0
SCREAMING_SNAKE_CASE_ : str = [0] * n
SCREAM... | 176 |
"""simple docstring"""
from math import isqrt
def _A (__a ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 176 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase :Tuple = logging.get_logger(__name__)
lowerCamelCase :Optional[Any] ... | 667 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord i... | 245 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : int = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT... | 716 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_lo... | 136 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCAmelCase_ : Any = pd.read_csv('... | 527 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaP... | 527 | 1 |
"""simple docstring"""
snake_case = tuple[float, float, float]
snake_case = tuple[float, float, float]
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> Vectorad:
_snake_case = end_pointa[0] - end_pointa[0]
_snake_case ... | 404 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''... | 404 | 1 |
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 transformers.utils import logging
logg... | 547 | 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 SPIECE_UNDERLINE, logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCa... | 547 | 1 |
"""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_features_o... | 370 |
"""simple docstring"""
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 Fl... | 370 | 1 |
def a_ ( lowerCAmelCase_ : int = 200_0000 ):
__lowerCAmelCase = [0 for i in range(n + 1 )]
__lowerCAmelCase = 1
__lowerCAmelCase = 1
for i in range(2, int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for... | 53 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import W... | 53 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, B... | 420 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
lowercase__ = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m... | 420 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelera... | 288 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__UpperCamelCase = input('''Enter image url: ''').strip()
print(f'''Downloading image from {url} ...''')
__UpperCamelCase = BeautifulSoup(requests.get(url).content,... | 247 | 0 |
'''simple docstring'''
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowercase : int = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class _lo... | 702 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __a ( A__ ) -> Any:
# encoder.embeddings are double copied in ori... | 159 | 0 |
"""simple docstring"""
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__ ( ... | 77 |
def lowerCAmelCase_ ( __a = 50000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =set()
lowerCamelCase__: int =int((limit - 24) ** (1 / 2) )
lowerCamelCase__: Tuple =set(range(3 , prime_square_limit + 1 , 2 ) ... | 59 | 0 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 714 |
def a ( A__ = 6_0_0_8_5_1_4_7_5_1_4_3 ) -> int:
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : str = int(A__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
... | 250 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list:
if len(SCREAMING_SNAKE_CASE__ ) <= 1:
return lst
lowercase : List[str] = 1
while i < len(SCREAMING_SNAKE_CASE__ ):
if lst[i - 1] <= lst[i]:
i += 1
... | 336 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
_UpperCamelCase : Optional[int] = 'src/transformers'
# Matches is_xxx_available()
_UpperCamelCase : int = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {x... | 541 | 0 |
'''simple docstring'''
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 ( ... | 719 |
'''simple docstring'''
import os
import sys
UpperCAmelCase = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenc... | 555 | 0 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class ... | 610 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __lowerCamelCase ( A__ , A__ ):
'''simple docstring'''
@register_to_config
def __init__( self : ... | 610 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
A : Optional[Any] = logging.get_logger(__name... | 5 |
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, loggi... | 5 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( a_ : int , a_ : float , a_ : float ):
return round(float(moles / volume ) * nfactor )
def SCREAMING_SNAKE_CASE ( a_ : float , a_ : float ... | 539 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Traject... | 539 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = (UnCLIPScheduler,)
def __a ( self : Dict , **_lowercase : Tuple ):
"""simple do... | 716 | from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = "EncodecFeatureExtractor"
lowerCAmelCase_ = ("T5Tokenizer... | 379 | 0 |
'''simple docstring'''
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 acce... | 349 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization ... | 349 | 1 |
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 , datas... | 714 |
def UpperCamelCase__( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] )->List[str]:
A__ = [1]
for i in range(2 , UpperCamelCase__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k ou... | 212 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int , _lowercase : float , _lowercase : float ) ->float:
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _SC... | 633 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.nu... | 633 | 1 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
SCREAMING_SNAKE_... | 700 | from math import log
from scipy.constants import Boltzmann, physical_constants
__lowerCamelCase : int = 300 # TEMPERATURE (unit = K)
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float , ) -... | 379 | 0 |
import torch
from diffusers import StableDiffusionPipeline
_snake_case : str = 'path-to-your-trained-model'
_snake_case : List[str] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
_snake_case : List[str] = 'A... | 53 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 53 | 1 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCamelCase ( _lowerCAmelCase : dict , _lowerCAmelCase : str , _lowerCAmelCase : set , _lowerCAmelCase : set , _lowerCAm... | 173 | """simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
__a = (boundary[1] - boundary[0]) / steps
__a = boundary[0]
__a = ... | 173 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils imp... | 13 | """simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDi... | 277 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
a : str = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies... | 672 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 672 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Tuple =logging.get_logger(__name__)
a__ : Optional[Any] ={
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/... | 399 |
'''simple docstring'''
def lowercase__ ( __lowercase : int | float | str ) -> tuple[int, int]:
"""simple docstring"""
try:
__UpperCamelCase = float(__lowercase )
except ValueError:
raise ValueError('Please enter a valid number' )
... | 399 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 707 |
def lowercase_ ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
snake_case__ : Optional[int] =len(SCREAMING_SNAKE_CASE )
snake_case__ : int =sum(SCREAMING_SNAKE_CASE )
snake_case__ : str =[[False for x in range(s + 1 )] for y in range(n + 1 )]
for i... | 408 | 0 |
'''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
_lowercase = datasets.logging.get_logger(__name__)
_lowercase = '''\
@InProceedings{moosavi2019... | 5 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 493 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowercase__ =[
'good first issue',
'feature request',
'wip',
]
def UpperCamelCase_ ( ):
a_ = Github(os.environ["""GITHUB_TOKEN"""] )
a_ = g.get_repo("""huggin... | 511 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( A__ , A__ , A__ , A__ ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
a_ , a_ = array[indexa], array[indexa]
... | 511 | 1 |
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
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"""vocab_file""":... | 569 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _i... | 569 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__a ... | 718 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__a = '\\n\n'
__a = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt... | 300 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__ ) , 'Tatoeba dir... | 20 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, Bli... | 20 | 1 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_... | 714 |
"""simple docstring"""
import math
def __A ( a_ : list , a_ : int )-> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = len(a_ )
SCREAMING_SNAKE_CASE : Optional[Any] = int(math.floor(math.sqrt(a_ ) ) )
... | 18 | 0 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( _a ,_a ):
... | 506 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : str , lowerCAmelCase : int = 16 ... | 169 | 0 |
from math import pi, sqrt
def a__ ( snake_case ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(snake_case ) not in (0, 0.5):
raise NotImplemen... | 131 |
def a__ ( snake_case , snake_case ):
"""simple docstring"""
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 131 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCamelCase__ ( ):
__UpperCAmelCase : int = {
"""repo_name""": ["""test_repo1""", """test_repo2""", """test_r... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils impor... | 701 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def UpperCamelCase ( lowercase_ : np.ndarray ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase , lowercase =np.shape(lowercase_ )
if rows != columns:
lowercase ... | 145 | 0 |
'''simple docstring'''
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_visi... | 38 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 0 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _conc... | 714 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if... | 350 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
snake_case : str = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/res... | 124 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinis... | 124 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=__lowercase ):
UpperCAmelCase = ["keras_nlp"]
def __init__( self : Any , *_A : Dict , **_A : List[str] ):
requires_backends(self , ['''keras_nlp'... | 710 | import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCAmelCase_ :
@property
def UpperCamelCase_ ( self : ... | 71 | 0 |
"""simple docstring"""
from math import pi, sqrt
def _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(_UpperCamelCase ) not in... | 353 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
A__ : List[Any] = logging.get_logger(__name__)
class __magic_name__ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self , *A_ , **A_ ... | 353 | 1 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from tr... | 718 |
_A : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def __lowerCAmelCase ( ) -> None:
__lowerCamelCase: Optional[int] = input("""Enter message: """ )
__lowerCamelCase: Dict = input("""Enter key [alphanumeric]: """ )
__lowerCamelCase: List[Any] =... | 189 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
lo... | 408 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | 0 |
import math
import os
import sys
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
_lowerCAmelCase : Dict = ""
try:
with open(lowerCAmelCase__ , "rb" ) as binary_file:
_lowerCAmelCase : List[str] = binary_file.rea... | 587 | import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_u... | 587 | 1 |
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