



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__       =	{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPTokenizerFast"]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPFeatureExtractor"]
							UpperCAmelCase__       =	["CLIPImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CLIPModel",
							    "CLIPPreTrainedModel",
							    "CLIPTextModel",
							    "CLIPTextModelWithProjection",
							    "CLIPVisionModel",
							    "CLIPVisionModelWithProjection",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCLIPModel",
							    "TFCLIPPreTrainedModel",
							    "TFCLIPTextModel",
							    "TFCLIPVisionModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "FlaxCLIPModel",
							    "FlaxCLIPPreTrainedModel",
							    "FlaxCLIPTextModel",
							    "FlaxCLIPTextPreTrainedModel",
							    "FlaxCLIPVisionModel",
							    "FlaxCLIPVisionPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_clip import (
							    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    CLIPConfig,
							    CLIPOnnxConfig,
							    CLIPTextConfig,
							    CLIPVisionConfig,
							)
							from .processing_clip import CLIPProcessor
							from .tokenization_clip import CLIPTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_clip_fast import CLIPTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clip import CLIPFeatureExtractor
														from .image_processing_clip import CLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_clip import (
														    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CLIPModel,
														    CLIPPreTrainedModel,
														    CLIPTextModel,
														    CLIPTextModelWithProjection,
														    CLIPVisionModel,
														    CLIPVisionModelWithProjection,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_clip import (
														    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCLIPModel,
														    TFCLIPPreTrainedModel,
														    TFCLIPTextModel,
														    TFCLIPVisionModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_clip import (
														    FlaxCLIPModel,
														    FlaxCLIPPreTrainedModel,
														    FlaxCLIPTextModel,
														    FlaxCLIPTextPreTrainedModel,
														    FlaxCLIPVisionModel,
														    FlaxCLIPVisionPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat





def 							A     (					)    ->			tuple[list[int], int]:







			'''simple docstring'''
			_UpperCAmelCase			=       [randint(-1_000							,       1_000   ) for i in range(10   )]
			_UpperCAmelCase			=       randint(-5_000							,       5_000   )
			return (arr, r)


UpperCAmelCase__       =	make_dataset()





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, ...]:







			'''simple docstring'''
			for triplet in permutations(_UpperCAmelCase							,       3   ):
						if sum(_UpperCAmelCase   ) == target:
									return tuple(sorted(_UpperCAmelCase   )   )
			return (0, 0, 0)





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, int, int]:







			'''simple docstring'''
			arr.sort()
			_UpperCAmelCase			=       len(_UpperCAmelCase   )
			for i in range(n - 1   ):
						_UpperCAmelCase	,					_UpperCAmelCase			=       i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)





def 							A     (					)    ->			tuple[float, float]:







			'''simple docstring'''
			_UpperCAmelCase			=       '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
			_UpperCAmelCase			=       '\ntriplet_sum1(*dataset)\n'
			_UpperCAmelCase			=       '\ntriplet_sum2(*dataset)\n'
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			return (min(_UpperCAmelCase   ), min(_UpperCAmelCase   ))


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							UpperCAmelCase__       =	solution_times()
							print(f"""The time for naive implementation is {times[0]}.""")
							print(f"""The time for optimized implementation is {times[1]}.""")
