code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_a : List[str] = logging.get_logger(__name__)
class __A (__magic_name__ ):
snake_case ... | 10 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : int = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
_a : Tuple = 1.60_21e-19 # units = C
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> tuple[str, float]:
"""simple docstring"""
if (c... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 1 |
'''simple docstring'''
import requests
_a : Optional[Any] = "YOUR API KEY"
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ = giphy_api_key ) -> list:
"""simple docstring"""
__UpperCAmelCase : Tuple = "+".join(query.... | 10 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
__UpperCAmelCase : Optional[Any] =... | 10 | '''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 import (
... | 10 | 1 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 10 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 1 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest... | 10 | '''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
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configurat... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 1 |
'''simple docstring'''
_a : List[str] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _lowercase ( ) -> tuple[list[int], int]:
"""simple docstring"""
__UpperCAmelCase : str = [ran... | 10 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_a : List[Any] = logging.get_logger(__name__)
def _lowercase ( lowerCamelCase__ ) -> List[int]:
"""simple docstri... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : str = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available():... | 10 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __A (__magic_name__ ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , Up... | 10 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils imp... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 1 |
'''simple docstring'''
import random
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False ) -> dict:
"""simple docstring"""
__UpperCAmelCase : dict = {i: [] for i in range(lowerCamelCase__ )}
... | 10 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
impo... | 10 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
_a : Optional[Any] = [True] * 1000001
_a : Optional[Any] = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
_a : Dict = False
i += 1
def _lowercase ( lowerCa... | 10 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 1 |
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_a : Tuple = "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("... | 10 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 1 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...im... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_lo... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 1 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __A (ctypes.Structure ):
# _fields is a specific attr expected by ctypes
snake_case ... | 10 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a : Any = logging.get_logg... | 10 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipel... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = [0] * len(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : str = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"... | 10 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(lowerCamelCase__ ):
... | 10 | '''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 import (
... | 10 | 1 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_a : Dict = datasets.utils.logging.get_logger(__name__)
class __A (folder_based_builder.FolderBasedBu... | 10 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 10 | '''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
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokenize... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_confi... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _lowercase ( ... | 10 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
"""simple docstring"""
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 1 |
'''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 1 |
'''simple docstring'''
import os
def _lowercase ( lowerCamelCase__ = "input.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) as input_file:
__UpperCA... | 10 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class __A (__magic_name__ ):
def __init__( self ):
# test for the above condition
self.test()
def _snake_case ... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 1 |
'''simple docstring'''
def _lowercase ( ) -> int:
"""simple docstring"""
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowerCamelCase__ , 999 )
if (a * a + b * b ==... | 10 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 1 |
'''simple docstring'''
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 ... | 10 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_a : str = (720, 1280) # Height, Width
_a : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
_a : List[st... | 10 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 1 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..co... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 1 |
'''simple docstring'''
import os
from math import logaa
def _lowercase ( lowerCamelCase__ = "base_exp.txt" ) -> int:
"""simple docstring"""
__UpperCAmelCase : float = 0
__UpperCAmelCase : Dict = 0
for i, line in... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 1 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOu... | 10 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import log... | 10 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 1 |
'''simple docstring'''
_a : Tuple = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBA... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
_a : List[Any] = "2020.9.26"
_a : int = "xcodz-dot, cclaus, dhruvmanila"
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lo... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : int = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/c... | 10 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType... | 10 | '''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 import (
... | 10 | 1 |
'''simple docstring'''
def _lowercase ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_a : int = generate_large_matrix()
_a : Tuple = ... | 10 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 1 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def _lowercase ( lowerCamelCase__ ) -> str:
"""simple docstring"""
if not sentence:
return ""
__UpperCAmelCase : List[str] = dict(zip(lowerCamelC... | 10 | '''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
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 1 |
'''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 ... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __A (__magic_name__ ):
snake_case :Dict = (PNDMScheduler,)
snake_case :List[Any] = (("... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_a : List[Any] = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__UpperCAmelCase : Dict ... | 10 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 1 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax... | 10 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> None:
"""simple docstring"""
__UpperCAmelCase : str = len(lowerCamelCase__ )
print("The following activities are selected:" )
# ... | 10 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __A (unittest.TestCase , __magic_name__ ):
def _snake_case ( self ):
__UpperCAmelCase : str ... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 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 import nn
imp... | 10 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
_a : Dict = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable... | 10 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from .... | 10 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transform... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class __A (__magic_name__ ):
snake_case :str
snake_case :int
def _lowercase ( lowerCamelCase__ ) -> list[str]:
"""simple ... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A :
snake_case :int
snake_case :Node | None = ... | 10 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 1 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_a : Optional[int] = False
clas... | 10 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : Optional[Any] = {
"configuration_efficientformer": [
"EFFICIENT... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 1 |
'''simple docstring'''
from functools import lru_cache
def _lowercase ( lowerCamelCase__ ) -> set:
"""simple docstring"""
__UpperCAmelCase : int = 2
__UpperCAmelCase : Optional[Any] = set()
while i * i <= n:
... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ ) -> bool:
"""simple docstring"""
return str(lowerCamelCase__ ) == str(lowerCamelCase__ )[::-1]
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple doc... | 10 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 1 |
'''simple docstring'''
_a : str = "Input must be a string of 8 numbers plus letter"
_a : List[str] = "TRWAGMYFPDXBNJZSQVHLCKE"
def _lowercase ( lowerCamelCase__ ) -> bool:
"""simple docstring"""
if not isinstance(lowerCamelCase__ ... | 10 | '''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 import (
... | 10 | 1 |
'''simple docstring'''
import os
def _lowercase ( lowerCamelCase__ = "matrix.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) as in_file:
__UpperCAme... | 10 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 1 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
_a : int = TypeVar("_T")
class __A (Generic[_T] ):
def __init__( self , UpperCamelCase_ = None ):
__UpperCAmelCase : list... | 10 | '''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
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 1 |
'''simple docstring'''
import sys
from collections import defaultdict
class __A :
def __init__( self ):
__UpperCAmelCase : Optional[Any] = []
def _snake_case ( self , UpperCamelCase_ )... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 1 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
for param in module.parameters():
__UpperCAmelCase : List[Any] ... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ ) -> list[int]:
"""simple docstring"""
if len(lowerCamelCase__ ) == 0:
return array
__UpperCAmelCase , __UpperCAmelCase : str ... | 10 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNe... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wa... | 10 | '''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> list[float]:
... | 10 | 1 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def _lowercase ( lowerCamelCase__ ) -> str:
"""simple docstring"""... | 10 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = prime_factors(lowerCamelCase__ ... | 10 | 1 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __A (__magic_name__ ):
snake_case :Union[str, Any] = (UnCLIPScheduler,)
def _snake_case ( self ... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Dict = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG... | 10 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Dict = {
"vocab_file": "vocab.json... | 10 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Any = {
"kssteven/ibe... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Optional[int] = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_availabl... | 10 | '''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = HfArgumentParser(lowerCamelCase__ )
__Upper... | 10 | 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,
... | 10 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 10 | 1 |
'''simple docstring'''
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 BasicTransformer... | 10 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_a : Option... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
if index == number_of_items:
return 0
... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Optional[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
... | 10 | '''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str = datasets.load_iris()
_a : List[Any] = np.array(data["data"])
_a : Optional[Any] = np.array(data["ta... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = len(lowerCamelCase__ )
__UpperCAmelCase : List[str] = sum(lowerCamelCase__ )... | 10 | '''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = set_counts
__UpperCAmelCase : int = max(UpperCamelCase_ )
__UpperCAmelCase : List[... | 10 | 1 |
'''simple docstring'''
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
... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Dict = (boundary[1] - boundary[0]) / steps
__UpperCAmelCase : Tuple = boundary[0... | 10 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_a : Optional[Any] = logging.get_logger("transformers.models.speecht5")
def _lowercase ( lowerCamelCase__ ... | 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 1 |
'''simple docstring'''
from collections.abc import Callable
class __A :
def __init__( self , UpperCamelCase_ = None ):
# Stores actual heap items.
__UpperCAmelCase : list = []
# Stores indexes ... | 10 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 1 |
'''simple docstring'''
def _lowercase ( ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase : str = 0
for i in range(1 , 1001 ):
total += i**i
return str(lowerCamelCase__ )[-10:]
if __name__ == "_... | 10 | '''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 import (
... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = int(lowerCamelCase__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowerCamelCase_... | 10 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
_a : int = []
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
for i in range(len(lowerCamelCase__ ) ... | 10 | '''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
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ = None ) -> list[list[str]]:
"""simple docstring"""
__UpperCAmelCase : str = word_bank or []
# create a table
... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
__UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) // 6
retu... | 10 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_a : Dict = logging.get_logger(__name__)
# TODO: upload to AWS
_a : Optional[Any] = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/ret... | 10 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
rai... | 10 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ ) -> list[int]:
"""simple docstring"""
return [ord(lowerCamelCase__ ) - 96 for elem in plain]
def _lowercase ( lowerCamelCase__ ) -> str:
... | 10 | '''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_a : Union[str, Any] = HfApi()
_a : int = {}
# fmt: off
_a : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347,... | 10 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a : Any = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt model... | 10 | 1 |
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