




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  (							UpperCAmelCase__     ):







						'''simple docstring'''





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






						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	Any			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	False
							super().__init__(__lowercase			,      __lowercase		)






						def __call__(		self				:	Union[str, Any]			,      __lowercase				:	Any=None			,      __lowercase				:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,      __lowercase				:	bool = True			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Union[bool, str, TruncationStrategy] = None			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[int] = 20_48			,      __lowercase				:	int = 0			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	bool = False			,      __lowercase				:	bool = False			,      __lowercase				:	bool = False			,      __lowercase				:	bool = False			,      __lowercase				:	bool = False			,      __lowercase				:	bool = True			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""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:
								snake_case_						       =	self.tokenizer
								snake_case_						       =	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
								snake_case_						       =	self.image_processor(
								    __lowercase			,      return_tensors=__lowercase			,      max_patches=__lowercase			,      **__lowercase		)
							else:
								# add pixel_values and bbox
								snake_case_						       =	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:
								snake_case_						       =	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:
									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(__lowercase		)

							return encoding_image_processor






						def 			snake_case__		(		self				:	List[str]			,      *__lowercase				:	Optional[int]			,      **__lowercase				:	List[str]		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Any			,      *__lowercase				:	Optional[int]			,      **__lowercase				:	Tuple		):






							"""simple docstring"""






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






						@property
						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							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		)		)






from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@staticmethod
						@abstractmethod
						def 			snake_case__		(		__lowercase				:	ArgumentParser		):






							"""simple docstring"""






							raise NotImplementedError()






						@abstractmethod
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							raise NotImplementedError()
