


'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[Any]  					=							["""image_processor""", """tokenizer"""]
      lowerCAmelCase__					:     Optional[int]  					=							"""ViTImageProcessor"""
      lowerCAmelCase__					:     Optional[Any]  					=							("""CLIPTokenizer""", """CLIPTokenizerFast""")



      def __init__(self    :    List[Any]   ,						UpperCamelCase    :    List[str]=None   ,						UpperCamelCase    :    List[Any]=None   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                        ''' instead.'''   ,						UpperCamelCase   ,						)
                    lowercase__      			= kwargs.pop('''feature_extractor''' )

             lowercase__      			= image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('''You need to specify an `image_processor`.''' )
             if tokenizer is None:
                    raise ValueError('''You need to specify a `tokenizer`.''' )

             super().__init__(UpperCamelCase   ,						UpperCamelCase )



      def __call__(self    :    Optional[Any]   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    Dict=None   ,						UpperCamelCase    :    Union[str, Any]=None   ,						UpperCamelCase    :    List[str]=None   ,						**UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







             if text is None and visual_prompt is None and images is None:
                    raise ValueError('''You have to specify either text, visual prompt or images.''' )

             if text is not None and visual_prompt is not None:
                    raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''' )

             if text is not None:
                    lowercase__      			= self.tokenizer(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if visual_prompt is not None:
                    lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if images is not None:
                    lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if visual_prompt is not None and images is not None:
                    lowercase__      			= {
                        '''pixel_values''': image_features.pixel_values,
                        '''conditional_pixel_values''': prompt_features.pixel_values,
                    }
                    return encoding
             elif text is not None and images is not None:
                    lowercase__      			= image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             elif visual_prompt is not None:
                    lowercase__      			= {
                        '''conditional_pixel_values''': prompt_features.pixel_values,
                    }
                    return encoding
             else:
                    return BatchEncoding(data=dict(**UpperCamelCase )   ,						tensor_type=UpperCamelCase )



      def       UpperCamelCase__		(self    :    Tuple   ,						*UpperCamelCase    :    List[str]   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             return self.tokenizer.batch_decode(*UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[str]   ,						*UpperCamelCase    :    Any   ,						**UpperCamelCase    :    int ):

             '''simple docstring'''







             return self.tokenizer.decode(*UpperCamelCase   ,						**UpperCamelCase )



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,						UpperCamelCase   ,						)
             return self.image_processor_class






      @property
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,						UpperCamelCase   ,						)
             return self.image_processor












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)


_a      	=  make_dataset()


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, ...]:





						"""simple docstring"""



						for triplet in permutations(__lowerCAmelCase ,		3	):
												if sum(__lowerCAmelCase	) == target:
																		return tuple(sorted(__lowerCAmelCase	)	)
						return (0, 0, 0)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, int, int]:





						"""simple docstring"""



						arr.sort()
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						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=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						return (min(__lowerCAmelCase	), min(__lowerCAmelCase	))


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

		testmod()
		_a      	=  solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')
