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 |
|---|---|---|---|---|
from __future__ import annotations
import numpy as np
def _lowercase ( lowercase__ ):
__lowerCAmelCase, __lowerCAmelCase : Optional[int] = np.shape(lowercase__ )
if rows != columns:
__lowerCAmelCase : Tuple = (
'''... | 275 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowercase (unittest.TestCase ):
@property
def Upper... | 275 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 242 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://hu... | 242 | 1 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def snake_case_... | 34 |
"""simple docstring"""
import os
def snake_case ( ):
with open(os.path.dirname(A__ ) + "/grid.txt" ) as f:
UpperCAmelCase_ : Any = [] # noqa: E741
for _ in range(20 ):
l.append([int(A__ ) for x in f.readline().split()] )
UpperCAm... | 268 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = """MCTCTFeatureExtractor"""
SCREAMING_SNAKE_CASE__ : Dict = """AutoTokenizer"""
... | 368 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_a = object()
# For specifying empty leaf dict `{}`
_a = object()
def __a ( __lower... | 23 | 0 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class a_ ( logging.LoggerAdapter ):
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
Uppe... | 321 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
A_ : Any = namedtuple(
'_TestCommandArgs',
[
'dataset',
... | 192 | 0 |
# coding=utf-8
# Copyright 2023 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/LICENSE-2.0
#
# Unless... | 356 |
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 _lowerCAmelCase( unittest.... | 292 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 225 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ : Union[str, Any] = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_M... | 225 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowerCAmelCase :
def __init__( self: int , _lowerCAmelCase: Any ):
lowercase :Any = data
lowercase :Node | None = None
class __low... | 363 |
def UpperCAmelCase__ ( ):
lowercase :List[str] = 0
for i in range(1, 1001 ):
total += i**i
return str(lowerCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 158 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def a_ ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE__ : Any ):
_lowerCamelCase : Optional[Any] =getattr(SCREAMING_SNAKE_CAS... | 199 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaToken... | 199 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/huggingface/info... | 271 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 271 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
def __init__( self : Dict , __lowerCa... | 107 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__A = logging.get_logger(__name__)... | 370 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class snake_case ( pl.LightningModule ):
def __init__( self : str , UpperCamelC... | 108 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowercase : List[Any] = 0B10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_... | 99 |
import math
import random
def A_ ( A__ , A__ = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowercase : Optional[Any] = 0.02
def A_ ( A__ , A__ ) -> float:
a__ ... | 99 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice... | 342 | import numpy as np
import datasets
_snake_case = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mah... | 342 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowercase : int = logging.get_logger(__name__)
class lowerCAmelCase__ ( lowerCamelCase_ ):
def __init__( self ... | 93 |
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 OptionalDependencyNotAvailab... | 226 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a__ ( unittest.TestCase ):
... | 351 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != 2 or len(a[0]) != 2 or len(lowerCamelCase) != 2 or len(b[0]) != 2:
raise Exception('''Matrices are not 2x2''')
__lowerC... | 9 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 280 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 115 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Union[str, Any] , __magic_name__ : Tuple , __magic_name__ : int ) -> str:
"""simple docstring"""
Uppe... | 351 |
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase_ : Optional[Any] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 62 | 0 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 1 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__lowerCamelCase = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,... | 360 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase... | 101 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 29 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
... | 216 | 0 |
def UpperCamelCase (lowercase_: List[Any] , lowercase_: int , lowercase_: List[Any] ) -> int:
def count_of_possible_combinations(lowercase_: Any ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for ite... | 362 |
from __future__ import annotations
def UpperCamelCase (lowercase_: list[int] , lowercase_: list[int] , lowercase_: int ) -> tuple[float, list[float]]:
A__ : Tuple = list(range(len(lowercase_ ) ) )
A__ : Union[str, Any] = [v / w for v, w in zip(lo... | 141 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__snake_case = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorp... | 310 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD... | 147 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
fr... | 162 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ : List[Any] =logging.get_logger(__name__)
lowerCAmelCase__ : Tuple ={
'micros... | 162 | 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 require_vision
f... | 86 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impor... | 86 | 1 |
from ... import PretrainedConfig
lowerCAmelCase__ : Union[str, Any] ={
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ ... | 355 |
def __lowercase ( a__ ) -> int:
__SCREAMING_SNAKE_CASE = [[0 for _ in range(a__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__SCREAMING_SNAKE_CASE = 1
for n in range(m + 1 ):
for k in range(1 ... | 118 | 0 |
"""simple docstring"""
def UpperCamelCase__ ( lowercase__ : int ):
assert isinstance(lowercase__ , lowercase__ ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
snake_case : Optional[int] = F... | 148 |
"""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... | 148 | 1 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_bas... | 354 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base impor... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase_ = """#"""
class a_ :
def __init__( self ):
_lowerCAmelCase : dict = {}
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : ... | 309 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : Union[str, Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltC... | 371 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 6 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging... | 245 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
... | 245 | 1 |
from __future__ import annotations
import queue
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple ) -> Optional[Any]:
a_ : Any = data
a_ : str = None
a_ : List[Any... | 120 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Any = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_... | 120 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class ... | 14 |
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.utils import logging
logging.... | 103 | 0 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increase... | 362 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase : List[Any] ={
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 32 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 116 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impor... | 116 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _UpperCAmelCase ( ):
__UpperCamelCase =[randint(-10_00 , 10_00 ) for i in range(10 )]
__UpperCamelCase =randint(-50_00 ... | 357 |
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_dim... | 117 | 0 |
import argparse
import os
import re
__A ='''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A =re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A =re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key`... | 19 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( __a : Dict[str, torch.Tensor] ):
'''simple docstring'''
UpperCamelCase__ = []
UpperCamelCase__... | 244 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
cla... | 251 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPh... | 251 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 77 | import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""si... | 118 | 0 |
from __future__ import annotations
__a = 1.6021e-19 # units = C
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, ) ->tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobility).count(0 ) != 1:
ra... | 173 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models at https://huggingface.co/models?filter=p... | 173 | 1 |
"""simple docstring"""
import warnings
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 ... | 16 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 0 |
'''simple docstring'''
from math import isclose, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = point_y / 4 / point_x
snake_case_ = 2 * normal_gradi... | 350 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
a : int = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
for it... | 72 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepar... | 332 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase : Union[str, Any] = {
'configuration_resnet': ['RESNET_PRETRAI... | 332 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_... | 222 |
'''simple docstring'''
import math
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
return math.sqrt(UpperCamelCase__ ) * math.sqrt(UpperCamelCase__ ) == num
def _a( UpperCamelCase__ : int ):
... | 222 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
def __... | 112 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
U... | 112 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 258 | import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__UpperCamelCase : Dict = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
... | 258 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils i... | 100 |
"""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_modeling_common import Mode... | 100 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = False , ):
lowerCamelCase__ : List[str] = cipher_alphabet or [chr(_... | 316 |
"""simple docstring"""
import re
def lowerCamelCase_ ( _lowerCamelCase ):
if len(re.findall('[ATCG]' , _lowerCamelCase ) ) != len(_lowerCamelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name... | 316 | 1 |
from __future__ import annotations
a_ = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class __low... | 330 |
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 torch
a_ = logging.get... | 330 | 1 |
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... | 343 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowercase ( unittest.TestCase ):
def a__ ( self ) -> List[str]:
debug_launcher(test_script.main )
... | 343 | 1 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def UpperCamelCase ( _lowerCAmelCase : int ) -> Optional[int]:
retu... | 246 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int = 3, _lowerCAmelCase : int = 7, _lowerCAmelCase : int = 1000000 ) -> int:
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : int = 1
for current_denominator in range(1, limit... | 246 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase (metaclass=__lowercase ):
"""simple docstring"""
UpperCAmelCase_ = ["torch", "scipy"]
def __init__( self : int, *_UpperCAmelCase ... | 365 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : set , SCREAMING_SNAKE_CASE__ : set , SCREAMING_SNAKE_CASE__ : dict , ... | 191 | 0 |
"""simple docstring"""
import os
def UpperCAmelCase__ ( ) -> Union[str, Any]:
'''simple docstring'''
with open(os.path.dirname(lowerCAmelCase__ ) + """/p022_names.txt""" ) as file:
lowercase = str(file.readlines()[0] )
lowercas... | 197 |
def SCREAMING_SNAKE_CASE__ ( ) -> list[list[int]]:
return [list(range(1000 - i ,-1000 - i ,-1 ) ) for i in range(1000 )]
lowerCamelCase : List[Any] = generate_large_matrix()
lowerCamelCase : Optional[int] = (
[[4, 3, 2, -1],... | 124 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCamelCase__ = TypeVar("T")
UpperCamelCase__ = Union[List[T], Tuple[T, ...]]
UpperCamelCase__ = Union[T, List[T], Dict[str, T]]
UpperCamelCase__ = Union[str, bytes, os.PathLike]
| 366 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingface.co/models?fil... | 87 | 0 |
from maths.prime_check import is_prime
def lowerCAmelCase_ ( __A ) -> int:
'''simple docstring'''
if not isinstance(__A, __A ):
UpperCAmelCase__ = f"""Input value of [number={number}] must be an integer"""
raise TypeEr... | 65 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : str, lowercase__ : bool = False ):
'''simple docstring'''
if not isinstance(lowercase__, lowercase__ ):
__lowercase =F'''Expected string as input, found {type(lowercase__ )}'''
... | 141 | 0 |
def _UpperCamelCase ( UpperCamelCase_ : int , UpperCamelCase_ : int ) -> str:
"""simple docstring"""
return int(input_a == input_a == 0 )
def _UpperCamelCase ( ) -> str:
"""simple docstring"""
print('Truth Table ... | 367 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 122 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( lowerCAmelCase_ ):
SCRE... | 218 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class __magic_name__ ( lo... | 218 | 1 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase :Optional[int] = [
# tf -> hf
("/", "."),... | 240 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class a ( _a ):
"""simple docstring"""
def __init__( self : Optional[Any] ) -> int:
# test for the above condition
self.tes... | 240 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 103 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ ... | 86 | 0 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ = None ) -> list[list[str]]:
_lowercase : Optional[Any] = word_bank or []
# create a table
_lowercase : int = len(lowerCamelCase_ ) + 1
_lowercase : li... | 84 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : Union[str, Any] = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
_lowercase : Any = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] >... | 84 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase = {
... | 131 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_ava... | 247 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowerCAmelCase : Optional[int] =... | 168 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmel... | 168 | 1 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 71 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
... | 163 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE_:Any = {"vocab_file": "vocab.txt", "tokenizer_file": "t... | 351 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
A , A : Tuple = 9, 14 # noqa: F841
A : Optional[int] = [
[0, ... | 115 | 0 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 136 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list:
'''simple docstring'''
lowercase_ = len(__lowerCAmelCase )
lowercase_ = [[0] * n for i in range(__lowerCAme... | 136 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def UpperCamelCase_ ( A__ : np.ndarray ):
'''simple docstring'''
lowerCAmelCase_, lowerCAmelCase_ : Optional[int] = np.shape(A__ )
if rows != col... | 89 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
... | 89 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 150 |
def lowerCAmelCase_ ( _snake_case : list[list[int | float]] ) -> int:
'''simple docstring'''
__magic_name__ : Any = len(_snake_case )
__magic_name__ : Optional[Any] = len(matrix[0] )
__magic_name__ : Union[str, Any] = min(_snake_case , _snake_ca... | 281 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerC... | 3 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_SCREAMING_SNAKE_CASE = ["a", "b", "c", "d", "e"]
def __lowerCamelCase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __... | 3 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
... | 101 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase : Union[str, Any] = "src/diffusers"
# Matches is_xxx_available()
lowerCamelCase : Dict = ... | 204 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCamelCase :
snake_case_ = 42
snake_case_ = None
snake_case_ = None
lowerCAmelCase_ = namedtuple("CoinsD... | 332 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder,... | 332 | 1 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 98 |
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase_ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0... | 12 | 0 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_att... | 197 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase_ : Union[str, Any] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""... | 197 | 1 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fr... | 78 | """simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a)
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = field(default="language-modeling" , metad... | 77 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__SCREAMING_SNAKE_CASE :List[Any] = '''Usage of script: script_name <size_of_canvas:int>'''
__SCREAMING_SNAKE_CASE :Any = [0] * 100 ... | 156 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.... | 156 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/li... | 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Union[str, Any] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_M... | 93 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCamelCase_ = TypeVar("T")
UpperCamelCase_ = Union[List[T], Tuple[T, ...]]
UpperCamelCase_ = Union[T, List[T], Dict[str, T]]
UpperCamelCase_ = Union[str, bytes, os.PathLike]
| 352 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..ut... | 344 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ :Tuple = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
... | 71 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
fr... | 349 | 0 |
"""simple docstring"""
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _a ( _snake_case ):
"""simple docstring"""
return (torch... | 365 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase ... | 234 | 0 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
snake_case_ = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( ):
UpperCAmelCase = Github(os.environ['GITHUB... | 78 |
"""simple docstring"""
def snake_case_ ( A_ : list[int], A_ : str ):
'''simple docstring'''
_lowerCamelCase : Tuple = int(A_ )
# Initialize Result
_lowerCamelCase : Dict = []
# Traverse through all deno... | 72 | 0 |
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ )... | 358 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_C... | 345 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__snake_case )
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : str ... | 345 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HAS... | 347 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 1 |
from __future__ import annotations
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ ) -> None:
lowerCamelCase : List[Any] = order
# a_{0} ... a_{k}
lowerCamelCase : ... | 48 | """simple docstring"""
import requests
UpperCAmelCase__ = """""" # <-- Put your OpenWeatherMap appid here!
UpperCAmelCase__ = """https://api.openweathermap.org/data/2.5/"""
def __UpperCAmelCase ( lowercase = "Chicago" ,lowercase = APPID ):
"""simple docstring"""
... | 289 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
... | 362 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseT... | 65 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : str = {
'''nielsr/canine-s''': 20... | 332 |
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... | 314 | 0 |
import math
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial in... | 365 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 ConfigT... | 148 | 0 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as ... | 28 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in rang... | 337 | 0 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__):
_lowerCamelCase : str = (CMStochasticIterativeScheduler,)
_lowerCamelCase : Tuple ... | 31 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=SCREAMING_SNAKE_CASE__):
_lowerCamelCase : str = ['torch', 'scipy']
def __init__( self : List[str], *a_ : Optional[int], **a_ : int ):
"""simpl... | 31 | 1 |
'''simple docstring'''
import string
def __UpperCAmelCase ( A : str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
UpperCAmelCase_ : Dict = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCAmelCase... | 304 |
'''simple docstring'''
def __UpperCAmelCase ( A : int = 1_0_0_0 ) -> int:
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = 1, 1
UpperCAmelCase_ : Dict = []
for i in range(1 , n + 1 ):
UpperCAmelCase_ : Optional[int] = prev_numera... | 304 | 1 |
from __future__ import annotations
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> bool:
'''simple docstring'''
return len(set(SCREAMING_SNAKE_CASE__ ) ) == len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 |
'''simple docstring'''
import requests
lowercase__ = "" # <-- Put your OpenWeatherMap appid here!
lowercase__ = "https://api.openweathermap.org/data/2.5/"
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "Chicago" , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''sim... | 83 | 0 |
__lowerCamelCase : List[str] = {str(digit): digit**5 for digit in range(10)}
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase ) )
def _snake_case ( ):
"""simple docstring"""
... | 18 | 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_ba... | 18 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: Optional[int] = logging.get_logger(__name__)
_UpperCamelCase: List[str] = {
'funnel-transformer/small': 'https://huggingface.co/funn... | 53 |
"""simple docstring"""
_UpperCamelCase: Dict = 2_5_6
# Modulus to hash a string
_UpperCamelCase: Union[str, Any] = 1_0_0_0_0_0_3
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring''... | 53 | 1 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch im... | 294 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_snake_case = logging.get_logger(__name__)
class UpperCamelCase ( snake_case_ ):
def __init__( self : Any ... | 294 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Trainin... | 357 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : str , lowerCAmelCase : Any , lowerCAmelCase : Any=False ):
"""simple docstring"""
if isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : ... | 275 | 0 |
from collections.abc import Generator
from math import sin
def __A ( __lowerCAmelCase )-> bytes:
"""simple docstring"""
if len(__lowerCAmelCase ) != 32:
raise ValueError('Input must be of length 32' )
_UpperCAmelCase = b''
... | 39 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (_a ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.w... | 309 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
A_ : Dict... | 362 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : bool = False ) -> float:
if not arr:
return 0
A_ : Union[str, Any] = 0 if allow_empt... | 4 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 200 ):
snake_case_ = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case_ = [0] * (pence + 1)
snake_case_ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(SCREAMING_SNAKE_CASE__ , penc... | 8 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xf... | 161 | 0 |
'''simple docstring'''
# 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/lic... | 365 |
'''simple docstring'''
import itertools
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 190 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,__snake_case = None ,_... | 209 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.u... | 287 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase__ : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __ini... | 287 | 1 |
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