code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTeste... | 293 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__A = Lock()
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _S... | 293 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCamelCase__ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCAmelC... | 257 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def A__ ( ):
SCREAMING_SNAKE_CASE_ = 9
SCREAMING_SNAKE_CASE_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
... | 257 | 1 |
'''simple docstring'''
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, prepa... | 200 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
def ... | 200 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective... | 365 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
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_comm... | 4 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ... | 152 |
'''simple docstring'''
import socket
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE__ : str =socket.gethostname()
... | 152 | 1 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers imp... | 368 |
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils imp... | 132 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'microsoft/focalnet-tiny': 'https://hugging... | 205 |
import argparse
import json
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 accelerat... | 205 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtrac... | 79 |
"""simple docstring"""
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->bytes:
'''simple docstring'''
a : Optional[Any] = "https://downloadgram.net/wp-json/wppress/video-downloader... | 79 | 1 |
from __future__ import annotations
from cmath import sqrt
def __lowercase ( a__ , a__ , a__ ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
__SCREAMING_SNAKE_CASE = b * b - 4 * a * c
_... | 257 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, ... | 257 | 1 |
'''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 OptionalDepende... | 0 |
'''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... | 0 | 1 |
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,
transpose,
)
if is_flax_availabl... | 82 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosi... | 4 | 0 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 202 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 202 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCamelCase__ : List[Any] = "▁"
... | 225 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_... | 132 | 0 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def __a ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 350 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCa... | 337 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''Ju... | 79 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def __lowerCamelCase ( __a :Optional[int] , __a :str ) -> Optional[Any]:
"... | 365 |
def __lowerCamelCase ( __a :float , __a :list[float] ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be emp... | 276 | 0 |
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 OptionalDependencyN... | 0 |
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 requi... | 0 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
f... | 369 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mod... | 289 | 0 |
"""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/licenses/LICE... | 202 |
"""simple docstring"""
def __magic_name__ ( __snake_case : int , __snake_case : int , __snake_case : int ) -> float:
lowercase : List[Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for... | 202 | 1 |
'''simple docstring'''
import os
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_pegasus import PegasusTokenizer
else:... | 356 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in ra... | 332 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
class UpperCamel... | 14 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],
}
try:
if not ... | 337 | 0 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
snake_case__ : Tuple = '''.'''
if __name__ == "__main__":
snake_case__ : Dict = os.path.join(REPO_P... | 274 | '''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''':... | 274 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class A__ ( __snake_case ):
def __init__( self , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = params
UpperC... | 52 |
'''simple docstring'''
A__: Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: int = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''T... | 276 | 0 |
import socket
def lowerCamelCase__ ( ) -> Optional[int]:
__snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__snake_case = socket.gethostname()
__snake_case = 1_2312
sock.connect((host, port) )
sock.se... | 352 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 238 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_C... | 46 | """simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 289 | 0 |
import argparse
import os
import re
import packaging.version
a__ = '''examples/'''
a__ = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULT... | 15 |
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__a ,__a ):
return 0
elif n == 2:
return 1
else:
_a : Any = [0, 1]
for i in range(2 ,n + 1 ... | 15 | 1 |
'''simple docstring'''
import re
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
if len(re.findall('[ATCG]' , __A ) ) != len(__A ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doct... | 42 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowercase : int = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
... | 332 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 355 |
from __future__ import annotations
snake_case__ : Dict = [True] * 1000001
snake_case__ : int = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
snake_case__ : str = Fa... | 250 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Optional[int] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',... | 274 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
... | 274 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation... | 246 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common ... | 246 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowerCamelCase : List[str] = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
__lowerCamelCase : Dict = None
def _snake_case ( ):
"""simple docstring"""
SCREAMIN... | 18 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_... | 238 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 356 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SqueezeBertConfig''',
... | 335 | 0 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE :int = 'examples/'
SCREAMING_SNAKE_CASE :Dict = {
'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(R'^__version... | 15 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE... | 15 | 1 |
"""simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> set:
snake_case_ = set()
# edges = list of graph's edges
snake_case_ = get_edges(UpperCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to... | 312 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = ["keras_nlp"]
def __init__( self, *lowerCAmelCase__, **lowerCAmelCase__) -> int:
requires... | 312 | 1 |
'''simple docstring'''
def A_ ( snake_case , snake_case , snake_case ):
def update_area_of_max_square(snake_case , snake_case ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
SCREAMING_SNAKE_CASE:Union[str, Any] = ... | 139 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( snake_case ) -> dict:
_lowercase : Dict = F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(snake_case ).json()
def _A ( snake_... | 250 | 0 |
"""simple docstring"""
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_sp... | 359 |
"""simple docstring"""
def _a ( _snake_case = 6008_5147_5143 ):
"""simple docstring"""
try:
UpperCAmelCase = int(_snake_case )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" ... | 234 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_t... | 246 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_UpperCAmelCase : Optional[Any] = 1
_UpperCAmelCase : List[str] = 1
while repunit:
_UpperCAmelCase : Tuple = ... | 246 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import ... | 358 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wa... | 159 | 0 |
"""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 Optiona... | 86 |
"""simple docstring"""
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 ImageProcessingSavingT... | 335 | 0 |
"""simple docstring"""
import math
def snake_case( __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
lowercase : Optional[int] = F"""Input value of [numb... | 360 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. an... | 116 | 0 |
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=1337 ,num_examples=42 ,dataset_name="my_dataset... | 312 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __snake_case ( ):
"""simple docstring"""
A_ = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"... | 312 | 1 |
"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _A ( UpperCamelCase... | 355 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_a = 5_00_00
_a = 50_00
_a , _a = os.path.split(__file__)
_a = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENA... | 144 | 0 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( _a = "AAPL"):
SCREAMING_SNAKE_CASE : List[str] = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
SCREAMING_SNAKE_CASE : Optional[int] = BeautifulSoup(requests.get(__lowerCAmelCase).text ... | 76 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class lowerCAmelCase__ :
def __init__( self : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
... | 234 | 0 |
'''simple docstring'''
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 _U... | 353 |
'''simple docstring'''
from math import factorial
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase =n // 2
return int(facto... | 48 | 0 |
'''simple docstring'''
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, prepar... | 272 |
from __future__ import annotations
from collections import namedtuple
def _lowerCAmelCase ( lowerCAmelCase_ :float , lowerCAmelCase_ :float , lowerCAmelCase_ :float )->tuple:
'''simple docstring'''
snake_case_ = namedtuple("result"... | 159 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( _lowercase ):
a = (IPNDMScheduler,)
a = (("""num_inference_steps""", 50),)
... | 361 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_A : str ={
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels'... | 129 | 0 |
lowercase_ = "Alexander Joslin"
import operator as op
from .stack import Stack
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> int:
'''simple docstring'''
A__ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
A_... | 7 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_:str = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_tr... | 116 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCAmelCase : List[str] = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowerCAmelCase : Union[str, Any] ... | 340 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modu... | 340 | 1 |
"""simple docstring"""
from collections import defaultdict
def __A ( a_ :str , a_ :str) -> bool:
__a : Tuple = first_str.lower().strip()
__a : Dict = second_str.lower().strip()
# Remove whitespace
__a : Lis... | 160 |
"""simple docstring"""
import os
def _snake_case ( ) -> Dict:
with open(os.path.dirname(lowerCamelCase__ ) + "/p022_names.txt" ) as file:
lowerCamelCase_ : str =str(file.readlines()[0] )
lowerCamelCase_ : Union[str, Any] ... | 144 | 0 |
from math import pi
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 367 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeli... | 195 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
requi... | 96 |
from math import sqrt
def A ( _SCREAMING_SNAKE_CASE = 100_0000 ) -> int:
lowerCamelCase : int = 0
lowerCamelCase : int = 0
lowerCamelCase : int
while num_cuboids <= limit:
max_cuboid_size += 1
... | 48 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _snake_case : list[int] , _snake_case : int ) ->bool:
"""simple docstring"""
if len(_snake_case ) == 0:
return False
__snake_case : int = len(_snake_case ) ... | 24 |
"""simple docstring"""
def lowercase ( _snake_case : int = 100 ) ->int:
"""simple docstring"""
__snake_case : str = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Dict = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __... | 24 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_im... | 74 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__snake_case : Dict =logging.get_logger(__name__)
... | 129 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 369 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
import t... | 160 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def _a ( UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Any=1_000 ) -> str:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# ... | 340 |
import collections
import importlib.util
import os
import re
from pathlib import Path
a_ = '''src/transformers'''
# Matches is_xxx_available()
a_ = re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
a_ = re.compile(r'''^_import_structure\s+=\s+\{(... | 340 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 192 | 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.co... | 192 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCAmelCase__ ():
"""simple docstring"""
with offline(O... | 64 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 195 | 0 |
'''simple docstring'''
import os
snake_case__ = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
def snake_case__ ( lowerCamelCase__ : str ) -> int:
A_ : Optional[Any] = 0
A_ : An... | 4 |
'''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 i... | 4 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils im... | 24 |
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 (
is_pipelin... | 24 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
lowerCAmelCase_ : Tuple ... | 248 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {
'''microsoft/unispeech-large-1500h-cv''': (... | 248 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
"""simple docstring"""
def __A ( a_ :int = 60_08_51_47_51_43) -> int:
try:
__a : List[Any] = int(a_)
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''')
if n <= 0:
raise V... | 160 | 0 |
"""simple docstring"""
from manim import *
class _UpperCAmelCase ( __snake_case ):
def a ( self : List[str] ):
__UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
__UpperCAmelCase = Rectangle(height=0.46 , width=0.46 ).set... | 365 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import... | 86 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_ve... | 192 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 192 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : ... | 233 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> int:
assert column_title.isupper()
snake_case_ = 0
snake_case_ = len(_SCREAMING_SNAKE_CASE ) - 1
snake_case_ = 0
while index >= 0:
snake_case_ ... | 233 | 1 |
'''simple docstring'''
import os
__snake_case ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def a_ ( lowerCamelCase : str ):
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(lowerCamelCas... | 4 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class UpperCAmelCase_ ( unittest.TestCase ):
def __Upp... | 4 | 1 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
_lowercase : List[str] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
_lowercase : Any ... | 363 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = 'SpeechT5FeatureExtractor'
_a = 'SpeechT5Tokenizer'
def __init__( self : D... | 272 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( a__ , a__ , a__):
'''simple docstring'''
a_ : Tuple =... | 248 |
from typing import Any
class A__:
"""simple docstring"""
def __init__( self , _lowercase ) -> List[str]:
a_ : List[str] = data
a_ : Optional[int] = None
def __repr__( self ) -> str:
return F'''Node({self... | 248 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm mu... | 365 |
"""simple docstring"""
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=1337 , num_examples... | 309 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 0.00
__lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
__lowerCAmelCase = f"Resistor at ind... | 57 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@requ... | 86 | 0 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowercase = logging.get_logger(__name__)
lowercase = R'\n Args:\n input_i... | 356 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class A ( __snake_case ):
def __init__( self , SCREAMING_SNAKE_CAS... | 311 | 0 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils im... | 233 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ... | 233 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCamelCase__ = {
# 1536-bit
5: {
'''prime''': int(
'''FFFFFFFF... | 63 | import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( a__ = "https://www.worldometers.info/coronavirus" ) ->dict:
'''simple docstring'''
_UpperCamelCase = BeautifulSoup(requests.get(a__ ).text , "html.parser" )
_UpperCamelCase = soup.findAll(... | 63 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToken... | 50 | '''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_vision
f... | 272 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowercase : List[str] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : ... | 151 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowercase : Any = '\\n@misc{chen2021evaluating,\n title={Evaluating Large... | 151 | 1 |
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
while a != 0:
a__ , a__ : Optional[Any] =b % a, a
return b
def _A ( SCREAMING_SNAKE_CASE : int... | 95 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Config... | 309 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 133 |
"""simple docstring"""
import baseaa
def a__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8" ) )
def a__ ( SCREAMING_SNAKE_CASE : bytes ):
'''simple docstring'''
return baseaa.baadecode(SCREAMI... | 133 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google... | 214 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : str = "docs/source/en/_toctree.yml"
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Dict = defaultdict(__magic_name__ ... | 311 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
a_ = logging.get... | 50 | import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
... | 50 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 63 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, t... | 289 |
"""simple docstring"""
import baseaa
def UpperCamelCase_ ( lowerCAmelCase__ : str ) -> bytes:
"""simple docstring"""
return baseaa.aaaencode(string.encode('utf-8' ) )
def UpperCamelCase_ ( lowerCAmelCase__ : bytes ) ... | 289 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
lowercase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokeni... | 151 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
... | 151 | 1 |
from __future__ import annotations
def UpperCamelCase__( UpperCamelCase__ : tuple[int, int] , UpperCamelCase__ : int )->list[tuple[int, int]]:
A__ , A__ = position
A__ = [
(y + 1, x + 2),
(y - 1, x + 2),
... | 39 |
# 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
#
# Unl... | 39 | 1 |
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = len(snake_case_ )
for i in range(snake_case_ ):
for j in range(i + 1 , snake_case_ ):
if numbers[j] < numbers[i]:
... | 133 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch... | 133 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipeline... | 53 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: str = logging.get_logger(__name__)
_UpperCamelCase: Union[str, Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba... | 53 | 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 lowerCAmelCase ( __UpperCamelCase,... | 50 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( _UpperCAmelC... | 50 | 1 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowerCamelCase : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowerCamelCase : ... | 114 |
'''simple docstring'''
import os
def _lowerCAmelCase ( ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =os.path.dirname(os.path.realpath(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =os.path.join(_UpperCamelCase , 'triangle.txt' )
w... | 114 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = [
"""encoder.version""",
"""decoder.version"""... | 289 | """simple docstring"""
UpperCAmelCase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre"""... | 289 | 1 |
'''simple docstring'''
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,
AutoModelForSequenceC... | 352 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a ( __a = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def a ( _... | 219 | 0 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbon... | 39 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, an... | 265 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A : Tuple = logging.get_logger(__name__)
_A : List[str] = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.... | 265 | 1 |
'''simple docstring'''
def lowercase__ ( __lowercase : int ) -> list[int]:
"""simple docstring"""
if num <= 0:
raise ValueError('Input must be a positive integer' )
__UpperCamelCase = [True] * (num + 1)
__UpperCamelCase =... | 53 |
'''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 import pa... | 53 | 1 |
'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 337 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Optional[Any] = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_ava... | 337 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = None
a :... | 114 |
# 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
#
# Unless... | 114 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 328 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0... | 328 | 1 |
'''simple docstring'''
class _lowercase :
def __init__( self: Optional[Any] ):
lowerCamelCase__ : dict[str, TrieNode] = {} # Mapping from char to TrieNode
lowerCamelCase__ : List[str] = False
... | 41 | import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
f... | 219 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> List[str]:
"""simple docstring"""... | 355 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 125 | 0 |
'''simple docstring'''
a : Tuple = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
1_3_3,
1_1_1,
8_8,
6_6,
... | 265 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase , int(b / 2 ) ) * actual_power(_lowercase , int(b / 2 ) )
else:
retur... | 265 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase__ ( _A , _A , _A , _A , _A ):
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if not scores:
raise ValueError('Scores cannot be empty' )
if de... | 350 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. 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/LICENS... | 96 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
for param in module.parameters():
lowercase : Optional[int] = False
def __lowercase ( ) ->Tuple:
... | 337 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 337 | 1 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowerCAmelCase ( UpperCamelCase_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all eve... | 362 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()[0]
__SCREAMIN... | 255 | 0 |
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