




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 					snake_case__							(__UpperCAmelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Tuple											=      ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_ :							Tuple											=      """Pix2StructImageProcessor"""
				SCREAMING_SNAKE_CASE_ :							Tuple											=      ("""T5Tokenizer""", """T5TokenizerFast""")





				def __init__(   self       :  List[str]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  int							)      ->  List[str]:
							a					=    False
							super().__init__(__lowerCamelCase      ,   __lowerCamelCase							)





				def __call__(   self       :  Any      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  Union[str, Any] = None      ,   __lowerCamelCase       :  Optional[Any] = True      ,   __lowerCamelCase       :  Dict = False      ,   __lowerCamelCase       :  Dict = None      ,   __lowerCamelCase       :  int = None      ,   __lowerCamelCase       :  List[str] = 20_48      ,   __lowerCamelCase       :  Optional[int] = 0      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  List[str] = None      ,   __lowerCamelCase       :  Union[str, Any] = False      ,   __lowerCamelCase       :  Optional[Any] = False      ,   __lowerCamelCase       :  Any = False      ,   __lowerCamelCase       :  Dict = False      ,   __lowerCamelCase       :  List[str] = False      ,   __lowerCamelCase       :  Dict = True      ,   __lowerCamelCase       :  Tuple = None      ,   **__lowerCamelCase       :  Union[str, Any]      ,   )      ->  Optional[int]:

							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=__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
										a					=    self.image_processor(
										    __lowerCamelCase      ,   return_tensors=__lowerCamelCase      ,   max_patches=__lowerCamelCase      ,   **__lowerCamelCase							)
							else:
										# add pixel_values and bbox
										a					=    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:
										a					=    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:
													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(__lowerCamelCase							)

							return encoding_image_processor





				def 							__UpperCAmelCase							(   self       :  Dict      ,   *__lowerCamelCase       :  str      ,   **__lowerCamelCase       :  Tuple							)      ->  Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   *__lowerCamelCase       :  List[Any]      ,   **__lowerCamelCase       :  Tuple							)      ->  Tuple:
							return self.tokenizer.decode(*__lowerCamelCase      ,   **__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(   self       :  int							)      ->  int:
							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 typing import TYPE_CHECKING

from ....utils import _LazyModule


__lowerCAmelCase	:       int     =	{'tokenization_tapex': ['TapexTokenizer']}


if TYPE_CHECKING:
		from .tokenization_tapex import TapexTokenizer


else:
		import sys

		__lowerCAmelCase	:       Tuple     =	_LazyModule(__name__, globals()['__file__'], _import_structure)






