



'''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 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Union[str, Any]			    =		['''image_processor''', '''tokenizer''']
			_A      :		Dict			    =		'''Pix2StructImageProcessor'''
			_A      :		Optional[Any]			    =		('''T5Tokenizer''', '''T5TokenizerFast''')

			def __init__(							self     : Optional[Any]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		False
					super().__init__(lowerCAmelCase__							,     lowerCAmelCase__						)

			def __call__(							self     : Dict							,     lowerCAmelCase__     : Optional[int]=None							,     lowerCAmelCase__     : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Union[bool, str, PaddingStrategy] = False							,     lowerCAmelCase__     : Union[bool, str, TruncationStrategy] = None							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : Optional[int] = 2_0_4_8							,     lowerCAmelCase__     : int = 0							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Optional[Union[str, TensorType]] = None							,     **lowerCAmelCase__     : Optional[int]							,     ):





					"""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:
							__SCREAMING_SNAKE_CASE       :		Dict           =		self.tokenizer
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.tokenizer(
							    text=lowerCAmelCase__							,     add_special_tokens=lowerCAmelCase__							,     padding=lowerCAmelCase__							,     truncation=lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     stride=lowerCAmelCase__							,     pad_to_multiple_of=lowerCAmelCase__							,     return_attention_mask=lowerCAmelCase__							,     return_overflowing_tokens=lowerCAmelCase__							,     return_special_tokens_mask=lowerCAmelCase__							,     return_offsets_mapping=lowerCAmelCase__							,     return_token_type_ids=lowerCAmelCase__							,     return_length=lowerCAmelCase__							,     verbose=lowerCAmelCase__							,     return_tensors=lowerCAmelCase__							,     **lowerCAmelCase__							,     )
							return text_encoding

					if not self.image_processor.is_vqa:
							# add pixel_values
							__SCREAMING_SNAKE_CASE       :		int           =		self.image_processor(
							    lowerCAmelCase__							,     return_tensors=lowerCAmelCase__							,     max_patches=lowerCAmelCase__							,     **lowerCAmelCase__						)
					else:
							# add pixel_values and bbox
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.image_processor(
							    lowerCAmelCase__							,     return_tensors=lowerCAmelCase__							,     max_patches=lowerCAmelCase__							,     header_text=lowerCAmelCase__							,     **lowerCAmelCase__						)

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

							if "attention_mask" in text_encoding:
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		text_encoding.pop("""attention_mask"""						)
							if "input_ids" in text_encoding:
									__SCREAMING_SNAKE_CASE       :		List[str]           =		text_encoding.pop("""input_ids"""						)
					else:
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		None

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

					return encoding_image_processor

			def UpperCamelCase__					(							self     : Optional[int]							,     *lowerCAmelCase__     : int							,     **lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : int							,     *lowerCAmelCase__     : Optional[int]							,     **lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






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



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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		self.tokenizer.model_input_names
					__SCREAMING_SNAKE_CASE       :		int           =		self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list					):
		if any(not isinstance(_lowerCamelCase       ,      _lowerCamelCase					) or x < 0 for x in sequence					):
				raise TypeError("""Sequence must be list of non-negative integers"""					)
		for _ in range(len(_lowerCamelCase					)					):
				for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase       ,      sequence[1:]					)					):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]