


"""simple docstring"""
import warnings
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'''

							__lowercase:		int							      =    ["""image_processor""", """tokenizer"""]
							__lowercase:		Optional[Any]							      =    """LayoutLMv3ImageProcessor"""
							__lowercase:		Union[str, Any]							      =    ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")



							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]=None		,       UpperCAmelCase_							:      List[str]=None		,       **UpperCAmelCase_							:      Dict       )					->Any:



														"""simple docstring"""



														snake_case_           =		None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																					    """ instead."""		,       UpperCAmelCase_		,       )
																					snake_case_           =		kwargs.pop("""feature_extractor"""       )

														snake_case_           =		image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("""You need to specify an `image_processor`."""       )
														if tokenizer is None:
																					raise ValueError("""You need to specify a `tokenizer`."""       )

														super().__init__(UpperCAmelCase_		,       UpperCAmelCase_       )



							def __call__(				self							:      Dict		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,       UpperCAmelCase_							:      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,       UpperCAmelCase_							:      Union[List[List[int]], List[List[List[int]]]] = None		,       UpperCAmelCase_							:      Optional[Union[List[int], List[List[int]]]] = None		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      Union[bool, str, PaddingStrategy] = False		,       UpperCAmelCase_							:      Union[bool, str, TruncationStrategy] = None		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      int = 0		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      Optional[Union[str, TensorType]] = None		,       **UpperCAmelCase_							:      int		,       )					->BatchEncoding:



														"""simple docstring"""



														if self.image_processor.apply_ocr and (boxes is not None):
																					raise ValueError(
																					    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""       )

														if self.image_processor.apply_ocr and (word_labels is not None):
																					raise ValueError(
																					    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""       )

														# first, apply the image processor
														snake_case_           =		self.image_processor(images=UpperCAmelCase_		,       return_tensors=UpperCAmelCase_       )

														# second, apply the tokenizer
														if text is not None and self.image_processor.apply_ocr and text_pair is None:
																					if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																												snake_case_           =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
																					snake_case_           =		features["""words"""]

														snake_case_           =		self.tokenizer(
														    text=text if text is not None else features["""words"""]		,       text_pair=text_pair if text_pair is not None else None		,       boxes=boxes if boxes is not None else features["""boxes"""]		,       word_labels=UpperCAmelCase_		,       add_special_tokens=UpperCAmelCase_		,       padding=UpperCAmelCase_		,       truncation=UpperCAmelCase_		,       max_length=UpperCAmelCase_		,       stride=UpperCAmelCase_		,       pad_to_multiple_of=UpperCAmelCase_		,       return_token_type_ids=UpperCAmelCase_		,       return_attention_mask=UpperCAmelCase_		,       return_overflowing_tokens=UpperCAmelCase_		,       return_special_tokens_mask=UpperCAmelCase_		,       return_offsets_mapping=UpperCAmelCase_		,       return_length=UpperCAmelCase_		,       verbose=UpperCAmelCase_		,       return_tensors=UpperCAmelCase_		,       **UpperCAmelCase_		,       )

														# add pixel values
														snake_case_           =		features.pop("""pixel_values"""       )
														if return_overflowing_tokens is True:
																					snake_case_           =		self.get_overflowing_images(UpperCAmelCase_		,       encoded_inputs["""overflow_to_sample_mapping"""]       )
														snake_case_           =		images

														return encoded_inputs



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[str]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		[]
														for sample_idx in overflow_to_sample_mapping:
																					images_with_overflow.append(images[sample_idx]       )

														if len(UpperCAmelCase_       ) != len(UpperCAmelCase_       ):
																					raise ValueError(
																					    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
																					    F""" {len(UpperCAmelCase_       )} and {len(UpperCAmelCase_       )}"""       )

														return images_with_overflow



							def 		lowerCAmelCase       (				self							:      Dict		,       *UpperCAmelCase_							:      List[str]		,       **UpperCAmelCase_							:      int       )					->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       *UpperCAmelCase_							:      Tuple		,       **UpperCAmelCase_							:      int       )					->str:



														"""simple docstring"""



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



							@property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Dict:



														"""simple docstring"""



														return ["input_ids", "bbox", "attention_mask", "pixel_values"]



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



														warnings.warn(
														    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,       UpperCAmelCase_		,       )
														return self.image_processor_class



							@property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->str:



														"""simple docstring"""



														warnings.warn(
														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,       UpperCAmelCase_		,       )
														return self.image_processor







"""simple docstring"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



