

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     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				['image_processor', 'tokenizer']
			__lowerCamelCase       				=				'Pix2StructImageProcessor'
			__lowerCamelCase       				=				('T5Tokenizer', 'T5TokenizerFast')


			def __init__(			self					,					lowercase					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	False
							super().__init__(lowercase					,					lowercase				)


			def __call__(			self					,					lowercase=None					,					lowercase = None					,					lowercase = True					,					lowercase = False					,					lowercase = None					,					lowercase = None					,					lowercase = 2048					,					lowercase = 0					,					lowercase = None					,					lowercase = None					,					lowercase = False					,					lowercase = False					,					lowercase = False					,					lowercase = False					,					lowercase = False					,					lowercase = True					,					lowercase = None					,					**lowercase					,					)     ->  BatchEncoding:
							'''simple docstring'''






							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:
											A__						=	self.tokenizer
											A__						=	self.tokenizer(
											    text=lowercase					,					add_special_tokens=lowercase					,					padding=lowercase					,					truncation=lowercase					,					max_length=lowercase					,					stride=lowercase					,					pad_to_multiple_of=lowercase					,					return_attention_mask=lowercase					,					return_overflowing_tokens=lowercase					,					return_special_tokens_mask=lowercase					,					return_offsets_mapping=lowercase					,					return_token_type_ids=lowercase					,					return_length=lowercase					,					verbose=lowercase					,					return_tensors=lowercase					,					**lowercase					,					)
											return text_encoding

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

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

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

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

							return encoding_image_processor


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Tuple:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






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


			@property
			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.tokenizer.model_input_names
							A__						=	self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)



from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int | None, int | None, float]:
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			_snake_case		=			(low + high) // 2
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					mid + 1       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_cross_sum(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int, int, float]:
			_snake_case	,					_snake_case		=			float('-inf' ), -1
			_snake_case	,					_snake_case		=			float('-inf' ), -1

			_snake_case		=			0
			for i in range(__lowercase       ,					low - 1       ,					-1 ):
						summ += arr[i]
						if summ > left_sum:
									_snake_case		=			summ
									_snake_case		=			i

			_snake_case		=			0
			for i in range(mid + 1       ,					high + 1 ):
						summ += arr[i]
						if summ > right_sum:
									_snake_case		=			summ
									_snake_case		=			i

			return max_left, max_right, (left_sum + right_sum)






def        a_		(       __lowercase   :	int ) ->  float:
			_snake_case		=			[randint(1       ,					__lowercase ) for _ in range(__lowercase )]
			_snake_case		=			time.time()
			max_subarray(__lowercase       ,					0       ,					input_size - 1 )
			_snake_case		=			time.time()
			return end - start






def        a_		(       ) ->  None:
			_snake_case		=			[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
			_snake_case		=			[time_max_subarray(__lowercase ) for input_size in input_sizes]
			print('No of Inputs\t\tTime Taken' )
			for input_size, runtime in zip(__lowercase       ,					__lowercase ):
						print(__lowercase       ,					'\t\t'       ,					__lowercase )
			plt.plot(__lowercase       ,					__lowercase )
			plt.xlabel('Number of Inputs' )
			plt.ylabel('Time taken in seconds' )
			plt.show()


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

			testmod()