
"""simple docstring"""







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 							__magic_name__     (		__SCREAMING_SNAKE_CASE   ):
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   ['image_processor', 'tokenizer']
					_SCREAMING_SNAKE_CASE       :					int     							=   'Pix2StructImageProcessor'
					_SCREAMING_SNAKE_CASE       :					str     							=   ('T5Tokenizer', 'T5TokenizerFast')





					def __init__(				self   :							Union[str, Any]							,						snake_case_   :							List[str]							,						snake_case_   :							int    ):
												__snake_case     			=       False
												super().__init__(_a							,						_a    )





					def __call__(				self   :							Tuple							,						snake_case_   :							Tuple=None							,						snake_case_   :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,						snake_case_   :							bool = True							,						snake_case_   :							Union[bool, str, PaddingStrategy] = False							,						snake_case_   :							Union[bool, str, TruncationStrategy] = None							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							Optional[int] = 2048							,						snake_case_   :							int = 0							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							Optional[bool] = None							,						snake_case_   :							bool = False							,						snake_case_   :							bool = False							,						snake_case_   :							bool = False							,						snake_case_   :							bool = False							,						snake_case_   :							bool = False							,						snake_case_   :							bool = True							,						snake_case_   :							Optional[Union[str, TensorType]] = None							,						**snake_case_   :							Dict							,						):




												if images is None and text is None:
																			raise ValueError("You have to specify either images or text."    )

												# Get only text
												if images is None and not self.image_processor.is_vqa:
																			__snake_case     			=       self.tokenizer
																			__snake_case     			=       self.tokenizer(
																			    text=_a							,						add_special_tokens=_a							,						padding=_a							,						truncation=_a							,						max_length=_a							,						stride=_a							,						pad_to_multiple_of=_a							,						return_attention_mask=_a							,						return_overflowing_tokens=_a							,						return_special_tokens_mask=_a							,						return_offsets_mapping=_a							,						return_token_type_ids=_a							,						return_length=_a							,						verbose=_a							,						return_tensors=_a							,						**_a							,						)
																			return text_encoding

												if not self.image_processor.is_vqa:
																			# add pixel_values
																			__snake_case     			=       self.image_processor(
																			    _a							,						return_tensors=_a							,						max_patches=_a							,						**_a    )
												else:
																			# add pixel_values and bbox
																			__snake_case     			=       self.image_processor(
																			    _a							,						return_tensors=_a							,						max_patches=_a							,						header_text=_a							,						**_a    )

												if text is not None and not self.image_processor.is_vqa:
																			__snake_case     			=       self.tokenizer(
																			    text=_a							,						add_special_tokens=_a							,						padding=_a							,						truncation=_a							,						max_length=_a							,						stride=_a							,						pad_to_multiple_of=_a							,						return_attention_mask=_a							,						return_overflowing_tokens=_a							,						return_special_tokens_mask=_a							,						return_offsets_mapping=_a							,						return_token_type_ids=_a							,						return_length=_a							,						verbose=_a							,						return_tensors=_a							,						**_a							,						)

																			if "attention_mask" in text_encoding:
																										__snake_case     			=       text_encoding.pop("attention_mask"    )
																			if "input_ids" in text_encoding:
																										__snake_case     			=       text_encoding.pop("input_ids"    )
												else:
																			__snake_case     			=       None

												if text_encoding is not None:
																			encoding_image_processor.update(_a    )

												return encoding_image_processor





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						*snake_case_   :							List[str]							,						**snake_case_   :							str    ):
												return self.tokenizer.batch_decode(*_a							,						**_a    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						*snake_case_   :							Optional[int]							,						**snake_case_   :							Union[str, Any]    ):
												return self.tokenizer.decode(*_a							,						**_a    )





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       self.tokenizer.model_input_names
												__snake_case     			=       self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )


"""simple docstring"""







from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def   __UpperCamelCase   (					)  ->     tuple[list[int], int]:







							"""simple docstring"""


							__snake_case     			=       [randint(-10_00							,    10_00 ) for i in range(10 )]
							__snake_case     			=       randint(-50_00							,    50_00 )
							return (arr, r)


_SCREAMING_SNAKE_CASE	       =      make_dataset()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, ...]:







							"""simple docstring"""


							for triplet in permutations(SCREAMING_SNAKE_CASE							,    3 ):
														if sum(SCREAMING_SNAKE_CASE ) == target:
																					return tuple(sorted(SCREAMING_SNAKE_CASE ) )
							return (0, 0, 0)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, int, int]:







							"""simple docstring"""


							arr.sort()
							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(n - 1 ):
														__snake_case			,   __snake_case     			=       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   __UpperCamelCase   (					)  ->     tuple[float, float]:







							"""simple docstring"""


							__snake_case     			=       "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
							__snake_case     			=       "\ntriplet_sum1(*dataset)\n"
							__snake_case     			=       "\ntriplet_sum2(*dataset)\n"
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							return (min(SCREAMING_SNAKE_CASE ), min(SCREAMING_SNAKE_CASE ))


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

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