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'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def A_ ( _lowerCamelCase : str ):
_lowerCAmelCase = analyze_text(__lowerCAmelCase )
_lowerCAmelCase = list(' ' + ascii_lowercase ... | 309 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _A ( UpperCAme... | 269 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : int = {}
class UpperCAmelCase ( lowercase_):
"""simple docstring"""
lowerC... | 700 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.... | 271 | 0 |
"""simple docstring"""
def snake_case ( A__ ,A__ ,A__ ,A__ ):
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = len(A__ ), len(grid[0] )
if (
min(A__ ,A__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
... | 95 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProces... | 392 | 0 |
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 accelerate.utils import patch_enviro... | 455 |
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 ...test_backbone_commo... | 455 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _UpperCAmelCase :
def __init__( self , a__ , a__=sys.maxsize ):
... | 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 |
import argparse
import os
# New Code #
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 import Acce... | 705 |
import itertools
import string
from collections.abc import Generator, Iterable
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = iter(snake_case )
while True:
__SCREAMING_SNAKE_CASE : int = tuple(itertools.isl... | 131 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/r... | 521 |
from __future__ import annotations
def __UpperCamelCase ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict ): # noqa: E741
while r - l > 1:
__a : Tuple = (l + r) // 2
if v... | 521 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _UpperCAmelCase ( ... | 421 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def a_ ( lowerCAmelCase_ : List[Any] ):
__lowerCAmelCase = [
'decoder.version',
'decoder.output_projection.weight',
'_floa... | 421 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try... | 372 |
'''simple docstring'''
lowerCAmelCase : List[str] = 2_5_6
# Modulus to hash a string
lowerCAmelCase : Tuple = 1_0_0_0_0_0_3
def _A ( A ,A ) -> bool:
lowercase : List[Any] = len(A )
lowercase : List[Any] = len(A )
... | 372 | 1 |
"""simple docstring"""
from string import ascii_lowercase, ascii_uppercase
def lowercase ( _snake_case : Tuple ) ->Optional[int]:
"""simple docstring"""
if not sentence:
return ""
__snake_case : List[str] = dict(zip(lowerCAmelCase_ , lowerCAmelCase_ ) )
return lowe... | 703 |
"""simple docstring"""
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 ... | 229 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> Optional[int]:
"""simp... | 381 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
... | 381 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a : Any = [8, 5, 9, 7]
a : Optional[int] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a : Union[str, Any] = [
... | 422 |
"""simple docstring"""
import math
a : str = 10
a : List[Any] = 7
a : Tuple = BALLS_PER_COLOUR * NUM_COLOURS
def snake_case__ ( _SCREAMING_SNAKE_CASE = 2_0 ) ->str:
UpperCAmelCase__ = math.comb(_SCREAMING_SNAKE_CASE , _SCR... | 422 | 1 |
from collections import defaultdict
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =1
UpperCAmelCase_ =True
for v in tree[start]:
if v not in visited:
ret += dfs(lowercase__ )
if ret % 2 =... | 54 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S ... | 54 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_visio... | 691 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small... | 691 | 1 |
def _UpperCAmelCase ( UpperCAmelCase : int ):
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be posit... | 519 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
__UpperCamelCase : Optional[Any] = '__DUMMY_TRANSFORMERS_USER__'
__UpperCamelCase : Optional[Any] = 'Dummy User'
__UpperCa... | 519 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 718 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
A = datasets.logging.get_logger(__name__)
A = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam and Dipanjan Da... | 97 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[str] = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
... | 15 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
"""simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCAmelCase__ ( unittest.TestCase ):
def lowercase_ ( self ):
'''simple docstring'''
A__ = [
"safety_checker/pytorch_model... | 706 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
__UpperCAmelCase ="""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
__UpperCAmelCase ... | 261 | 0 |
import baseaa
def A_ ( _lowerCAmelCase ) -> bytes:
return baseaa.baaencode(string.encode("utf-8" ) )
def A_ ( _lowerCAmelCase ) -> str:
return baseaa.baadecode(_lowerCAmelCase ).decode("utf-8" )
if __name__ == "__main__":
__lowerCamelCase : int = """Hello ... | 629 |
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 ...test_configuration_common import Confi... | 629 | 1 |
"""simple docstring"""
import math
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if ang... | 507 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_A = """src/transformers"""
# This is to ma... | 507 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _A ( A__ , A__ , A__ , A__=5 ):
"""simple docstring"""
assert masked_input.count('''<mask>''' ) == 1
__lowercase = torch.tensor(tokenizer.encode(A__ ,... | 41 |
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 | 0 |
from __future__ import annotations
def A ( UpperCAmelCase , UpperCAmelCase ):
_snake_case : list[list[int]] = []
_snake_case : list[int] = []
_snake_case : int = 0
_snake_case : Tuple ... | 717 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__lowerCAmelCase :str = logging.get_logger(__name__)
class _a( __A ):
def __init__( self , *__snake_case , **__snake_case ) ... | 278 | 0 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class UpperCam... | 379 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformer... | 419 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-mediu... | 706 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def snake_case_ (__A : np.ndarray ) -> np.ndarray:
return input_array.reshape((input_array.size, 1) )
def snake_case_ (... | 218 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 612 |
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 impo... | 612 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( UpperCamelCase ,UpperCamelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_... | 718 |
'''simple docstring'''
from __future__ import annotations
import requests
lowerCAmelCase__ = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked c... | 471 | 0 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils... | 120 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__... | 15 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__a = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCamelCase__( lowerCAmelCase__ ):
"... | 689 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCamelCase_ ( a_ ) ->Tuple:
A =FileLock(str(tmpdir / "foo.lock" ) )
A =FileLock(str(tmpdir / "foo.lock" ) )
A =0.01
with locka.acquire():
with pytest.raises(a_ ):
A =time.tim... | 689 | 1 |
from __future__ import annotations
def __UpperCamelCase ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int ):
__a : list[list[int]] = []
__a : list[int] = []
__a : List[str] = 0
__a : Any ... | 521 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase , UpperCamelCase :List[Any] = position
UpperCamelCase :Any = [
(y + 1, x + 2),
(y - 1, x + 2)... | 658 | 0 |
def lowerCamelCase_ ( A : Optional[Any] = 50 ):
"""simple docstring"""
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - bl... | 702 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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_... | 413 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__ : Optional[Any] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
... | 171 |
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_warmup, set_seed
from accelerate import Accelera... | 171 | 1 |
def lowerCamelCase_(lowerCamelCase_ ) -> Union[str, Any]:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCAmelCase = 1
UpperCAmelCase = 1
while repunit:
UpperCAmelCase = (10 * repunit + 1) % divisor
... | 719 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
__lo... | 457 | 0 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( lowerCamelCase_ , unittest.Test... | 105 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 662 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_UpperCamelCase : str = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 713 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_UpperCamelCase : int = logging... | 134 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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 import (
TEXT_GUIDED_IMAGE_INPAINT... | 271 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...... | 100 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowercase__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
def __init__(self , *_lowerca... | 716 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT M... | 63 | 0 |
"""simple docstring"""
import warnings
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 ( ... | 530 | """simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 530 | 1 |
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 AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN... | 212 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProc... | 212 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __SCREAMING_SNAKE_CASE ( lowerCAmelC... | 315 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _a ( ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__ : Any = {
'''repo_name''': ['''test_repo1''', '''test_re... | 315 | 1 |
import numpy as np
def a ( A__ : Optional[Any] , A__ : Tuple , A__ : Union[str, Any] = 1e-12 , A__ : Tuple = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(_lowercase )[0] == np.shape(_lowercase... | 704 |
def a ( A__ : Optional[int] ) -> Tuple:
"""simple docstring"""
_lowercase =[0] * len(A__ )
_lowercase =[]
_lowercase =[]
_lowercase =0
for values in graph.values():
for i in values:
indegree[... | 380 | 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.Te... | 347 |
def UpperCamelCase( __UpperCamelCase : str ,__UpperCamelCase : str ):
lowerCAmelCase_ : Any = len(__UpperCamelCase )
lowerCAmelCase_ : Optional[int] = []
for i in range(len(__UpperCamelCase ) - pat_len + 1 ):
lowerCAmelCase_ : s... | 171 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase_ = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase_ = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"... | 586 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenization_m2m_100": ["M2M1... | 586 | 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 PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {''... | 565 |
'''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 = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_... | 565 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import ... | 718 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler... | 324 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitesp... | 49 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ ( _UpperCamelCase ):
@staticmethod
@abstractmethod
def __lowercase ( _UpperCAmelCase : ArgumentParser ):
raise NotImplementedEr... | 358 | 0 |
'''simple docstring'''
import cmath
import math
def UpperCamelCase_ ( A__ : float , A__ : float , A__ : float , A__ : float ):
'''simple docstring'''
lowerCAmelCase_ : List[str] ... | 398 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int ):
'''simple docstring'''
assert isinstance(A__ , A__ ), f'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
lowerCAmelCase_ ... | 398 | 1 |
def UpperCAmelCase_ ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'{soluti... | 509 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 509 | 1 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def _snake_case ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('U... | 22 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats... | 22 | 1 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowercase__ )
__lowercase = len(lowercase__ )
__lowercase = (
first_str_length if first_str_length > second_str_length else second_str_length
)
... | 80 |
def __UpperCamelCase ( lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
while b:
lowerCAmelCase_ , lowerCAmelCase_ : int = b, a % b
return a
def __UpperCamelCase ( lowercase__ : ... | 600 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowercase ( unittest.TestCase ):
... | 719 |
def snake_case (UpperCamelCase : int ):
'''simple docstring'''
lowerCamelCase__ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 235 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCamelCase__ : Union[str, Any] = {
"""configuration_trocr""": ["""TROCR_PRE... | 387 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[Any] = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """Bloo... | 387 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Any = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ ="""encoder-decoder"""
SCREAMING_SNAKE_CASE__ ... | 720 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 214 | 0 |
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__ = {
'''facebook/xlm-roberta-xl''': '''https://huggi... | 14 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: Optional[int] = logging.get_logger(__name__)
A: Dict = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/m... | 160 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCamelCase__ ( yaml.SafeLoader):
"""simple docstring"""
def _a (self , __a ):
'''simple docstring'''
... | 484 |
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 lowerCamelCase__ ( unittest... | 484 | 1 |
"""simple docstring"""
import math
class __lowercase :
'''simple docstring'''
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase ):
__a : List[str] = 0.0
__a : ... | 52 | 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 = {'''vocab_file''': '''sen... | 167 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> float:
if digit_amount > 0:
return round(number - int(_lowerCAmelCase ) , _lowerCAmelCase )
return number - int(_lowerCAmelCase )
if __name__ == "__main__":
print(decimal_isolat... | 700 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] =... | 301 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePro... | 116 |
lowerCAmelCase_ = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
1_0: "a",
1_1: "b",
1_2: "c",
1_3: "d",
1_4: "e",
1_5: "f",
}
def A_ ( lowercase_ ) -> str:
assert type(lowercase_ ) in... | 326 | 0 |
'''simple docstring'''
from math import pow, sqrt
def SCREAMING_SNAKE_CASE ( *a_ : float ):
__a = len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def SCREAMING_SNAKE_CASE ( a_ : float , a_ :... | 706 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
UpperCAmelCase_ = "path-to-your-trained-model"
UpperCAmelCase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
UpperCAmelCase_ = "A photo of sks dog... | 490 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Dict = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json"""... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...u... | 45 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 100 , ) -> float:
lowercase__ = x_start
lowercase__ ... | 45 | 1 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import requir... | 75 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE: Any = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_a... | 360 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , _UpperCAmelCase=2 , _UpperCAmelCase=3 , _UpperCAmelCa... | 709 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 679 | 0 |
from __future__ import annotations
def a__ ( snake_case , snake_case , snake_case , snake_case ): # noqa: E741
"""simple docstring"""
while r - l > 1:
__SCREAMING_SNAKE_CASE : Optional[int] = (l + r) // 2
if v[m] >= key:
__SCREAMING_SNA... | 74 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"... | 451 | 0 |
from __future__ import annotations
def UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if not nums:
return 0
__A = nums[0]
__A = 0
for num in nums[1:]:
__A , __A = (
max_excluding + num,
... | 205 |
from __future__ import annotations
from typing import Any
def UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
create_state_space_tree(lowerCAmelCase__ , [] , 0 )
def UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerC... | 205 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYa... | 431 |
def __UpperCamelCase ( _A ):
if not isinstance(_A , _A ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
lowerCAmelCase_ = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
... | 431 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ : Optional[Any] = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_... | 713 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def A_( A , A , **A ):
UpperCAmelCase_ = AutoConfig.from_pretrained(A , **A )
UpperCAmelCase_ = AutoModelForSeqaSeqLM.from_config(A )
model.save_pr... | 486 | 0 |
'''simple docstring'''
import requests
A = '''''' # <-- Put your OpenWeatherMap appid here!
A = '''https://api.openweathermap.org/data/2.5/'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : str = "Chicago" , lowerCAmelCase__ : str = APPID) -> Tupl... | 125 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepen... | 580 | 0 |
from maths.prime_check import is_prime
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
_lowerCAmelCase : Dict = F"Input value of [number={number}] must be an integer"
... | 196 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFea... | 196 | 1 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase_ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase_ = "\\n@in... | 326 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A_ ( ) -> int:
_snake_case : Optional[int] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3''']... | 326 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _a :
'''simple docstring'''
UpperCamelCa... | 713 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
tra... | 120 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 663 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobert... | 663 | 1 |
'''simple docstring'''
def a_ ( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class a_ ( snake_cas... | 347 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_snake_case : List[str] = logging.get_logger(__name__)
def lowerCAmelCase_ ( __lowerCamelCase ):
if isinstance(__lowerCamelCase , np.ndarray )... | 81 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowercase__ ( __UpperCamelCase = "AAPL" )-> str:
UpperCamelCase = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
UpperCamelCase = BeautifulSoup(... | 301 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import req... | 704 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 601 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class ... | 24 |
def _lowerCAmelCase ( _lowerCAmelCase ) -> int:
'''simple docstring'''
assert column_title.isupper()
__snake_case = 0
__snake_case = len(_lowerCAmelCase ) - 1
__snake_case = 0
while index >= 0:
__snake... | 371 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from... | 109 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffuser... | 109 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 114 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase__ = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Do... | 276 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"YituTech/conv-bert-base": "https... | 276 | 1 |
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... | 611 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
... | 611 | 1 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock im... | 718 | """simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
... | 558 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCam... | 28 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ):
"""simple docstring"""
wit... | 616 | 0 |
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,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPooli... | 236 | import heapq
def a__ ( a ) -> set[int]:
A_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, s... | 236 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import ca... | 468 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_au... | 468 | 1 |
'''simple docstring'''
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def SCREAMING_SNAKE_CASE ( *lowercase_ : Any ):
with open(UpperCamelCase__ , """r""" ) as fh:
fcntl.flock(UpperCamelCase__ , fcntl.LOCK_EX )
... | 720 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
lowercase = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) ... | 653 | 0 |
"""simple docstring"""
from typing import Any
class lowercase_ :
def __init__( self : Optional[Any] , _lowercase : Any ):
lowerCAmelCase__ : Tuple = data
lowerCAmelCase__ : List[str] = None
... | 308 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_pro... | 308 | 1 |
import re
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str ) -> str:
if len(re.findall("[ATCG]" , lowerCAmelCase ) ) != len(lowerCAmelCase ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
if __name__ == "__main__":
... | 708 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> int:
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("String lengths must match!" )
_UpperCAmelCase : List[Any] = 0
for chara, chara in zip(lowerCAmelCase , ... | 467 | 0 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
class lowerCAmelCase__ ( __lowercase )... | 501 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 0 |
import unittest
from transformers import XLMConfig, 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 ModelTesterMixin, ... | 144 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCamelCase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
UpperCamelCase = None
def _a ( ) -> Tuple:
lowerCamelCase_ : Optional[int] = ar... | 144 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 333 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase =logging.get_logger(__name__)
__lowerCAmelCase ={
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-mon... | 333 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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/licen... | 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
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
... | 24 | 1 |
"""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.s... | 617 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[str] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 286 | 0 |
import os
def lowercase_ ( ):
"""simple docstring"""
with open(os.path.dirname(_A ) + "/p022_names.txt" ) as file:
lowerCamelCase__ : List[Any] = str(file.readlines()[0] )
lowerCamelCase__ : List[Any] = names.replace("\"" ... | 5 |
from __future__ import annotations
def lowercase_ ( _A : str , _A : list[str] | None = None , _A : dict[str, float] | None = None , _A : bool = False , ):
"""simple docstring"""
lowerCamelCase__ : Tuple = ... | 5 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _lowerCAmelCase ( __snake_case : List[str] ) -> Any:
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
... | 8 |
"""simple docstring"""
import random
from typing import Any
def lowercase__ ( lowercase_ ) -> list[Any]:
"""simple docstring"""
for _ in range(len(lowercase_ ) ):
_UpperCamelCase : Dict = random.randint(0 ,len(lowercase_ ... | 624 | 0 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = ["image_proces... | 715 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=13_37 , num_examples=42 , dataset_n... | 157 | 0 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase_ (A : Union[str, Any] , A : Optional[int] , A : str ):
snake_case__ : Union[str, Any] = {
'en': 'Machine learning is great, isn\'t it?',
... | 478 |
def lowercase_ (A : int , A : int ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
snake_case__ : List[str] = str(bin(A ) )[2:] # remove the leading "0b"
snake_case__ : int = ... | 478 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is_torch_available(... | 548 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, t... | 548 | 1 |
import math
def __snake_case ( __magic_name__ ):
'''simple docstring'''
return math.sqrt(__magic_name__ ) * math.sqrt(__magic_name__ ) == num
def __snake_case ( __magic_name__ ):
'''simple docstring'''
lowercase = ... | 441 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ):
'''simple docstring'''
lowercase = ArgumentParser(
description=(
... | 441 | 1 |
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 B... | 719 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase__ : Any = logging.get_logger(__name__)
lowerCamelCase__ : Union[str, Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https... | 208 | 0 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __lowercase ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a : List[str] = CustomTokenizer
pass
... | 502 |
"""simple docstring"""
def _A ( __lowercase = 200_0000 ):
"""simple docstring"""
lowerCamelCase__ = [0 for i in range(n + 1 )]
lowerCamelCase__ = 1
lowerCamelCase__ = 1
for i in range(2 , int(n**0.5 ) ... | 129 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.... | 16 |
"""simple docstring"""
import baseaa
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaaencode(string.encode('utf-8' ) )
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaadecod... | 16 | 1 |
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