




"""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 lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""image_processor""", """tokenizer"""]
			lowerCamelCase__   =    """Pix2StructImageProcessor"""
			lowerCamelCase__   =    ("""T5Tokenizer""", """T5TokenizerFast""")
			def __init__(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Optional[int]					=					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						,   ):
							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:
											_lowerCamelCase  :    Optional[Any]					=					self.tokenizer
											_lowerCamelCase  :    Tuple					=					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
											_lowerCamelCase  :    Union[str, Any]					=					self.image_processor(
											    lowercase						,   return_tensors=lowercase						,   max_patches=lowercase						,   **lowercase						)
							else:
											# add pixel_values and bbox
											_lowerCamelCase  :    Optional[Any]					=					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:
											_lowerCamelCase  :    Optional[Any]					=					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:
															_lowerCamelCase  :    List[Any]					=					text_encoding.pop('attention_mask'						)
											if "input_ids" in text_encoding:
															_lowerCamelCase  :    str					=					text_encoding.pop('input_ids'						)
							else:
											_lowerCamelCase  :    Optional[Any]					=					None

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

							return encoding_image_processor
			def       A_					(						self						,   *lowercase						,   **lowercase						):
							return self.tokenizer.batch_decode(*lowercase						,   **lowercase						)
			def       A_					(						self						,   *lowercase						,   **lowercase						):
							return self.tokenizer.decode(*lowercase						,   **lowercase						)






			@property
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					self.tokenizer.model_input_names
							_lowerCamelCase  :    Optional[int]					=					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					abs(lowercase__	)
				_lowerCamelCase  :    Optional[int]					=					0
				while n > 0:
								res += n % 10
								n //= 10
				return res







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					abs(lowercase__	)
				return n if n < 10 else n % 10 + sum_of_digits(n // 10	)







def   _snake_case    (       lowercase__	):
				return sum(int(lowercase__	) for c in str(abs(lowercase__	)	)	)







def   _snake_case    (       ):
				from collections.abc import Callable
				from timeit import timeit

				def benchmark_a_function(lowercase__							,					lowercase__	) -> None:
								_lowerCamelCase  :    int					=					f'''{func.__name__}({value})'''
								_lowerCamelCase  :    Optional[Any]					=					timeit(f'''__main__.{call}'''							,					setup='import __main__'	)
								print(f'''{call:56} = {func(lowercase__	)} -- {timing:.4f} seconds'''	)

				for value in (262144, 1125899906842624, 1267650600228229401496703205376):
								for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
												benchmark_a_function(lowercase__							,					lowercase__	)
								print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()