


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				(							_lowercase):
	_a    :    Dict        =				['''image_processor''', '''tokenizer''']
	_a    :    str        =				'''LayoutLMv2ImageProcessor'''
	_a    :    Optional[int]        =				('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')




	def __init__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						Optional[int]=None						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]=None						,   **_SCREAMING_SNAKE_CASE		:						Optional[Any]						)->			Any:
			lowerCAmelCase__  :     str       	=   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
					lowerCAmelCase__  :     List[Any]       	=   kwargs.pop('''feature_extractor'''						)

			lowerCAmelCase__  :     Union[str, Any]       	=   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__(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)




	def __call__(   self		:						Union[str, Any]						,   _SCREAMING_SNAKE_CASE		:						Dict						,   _SCREAMING_SNAKE_CASE		:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,   _SCREAMING_SNAKE_CASE		:						Union[List[List[int]], List[List[List[int]]]] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[List[int], List[List[int]]]] = None						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Union[bool, str, PaddingStrategy] = False						,   _SCREAMING_SNAKE_CASE		:						Union[bool, str, TruncationStrategy] = False						,   _SCREAMING_SNAKE_CASE		:						Optional[int] = None						,   _SCREAMING_SNAKE_CASE		:						int = 0						,   _SCREAMING_SNAKE_CASE		:						Optional[int] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[bool] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[bool] = None						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[str, TensorType]] = None						,   **_SCREAMING_SNAKE_CASE		:						int						,   )->			BatchEncoding:
			# verify input
			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.'''						)

			if return_overflowing_tokens is True and return_offsets_mapping is False:
					raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''						)

			# first, apply the image processor
			lowerCAmelCase__  :     Optional[Any]       	=   self.image_processor(images=_SCREAMING_SNAKE_CASE						,   return_tensors=_SCREAMING_SNAKE_CASE						)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						):
							lowerCAmelCase__  :     Optional[Any]       	=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCAmelCase__  :     Dict       	=   features['''words''']

			lowerCAmelCase__  :     List[str]       	=   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=_SCREAMING_SNAKE_CASE						,   add_special_tokens=_SCREAMING_SNAKE_CASE						,   padding=_SCREAMING_SNAKE_CASE						,   truncation=_SCREAMING_SNAKE_CASE						,   max_length=_SCREAMING_SNAKE_CASE						,   stride=_SCREAMING_SNAKE_CASE						,   pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,   return_token_type_ids=_SCREAMING_SNAKE_CASE						,   return_attention_mask=_SCREAMING_SNAKE_CASE						,   return_overflowing_tokens=_SCREAMING_SNAKE_CASE						,   return_special_tokens_mask=_SCREAMING_SNAKE_CASE						,   return_offsets_mapping=_SCREAMING_SNAKE_CASE						,   return_length=_SCREAMING_SNAKE_CASE						,   verbose=_SCREAMING_SNAKE_CASE						,   return_tensors=_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						,   )

			# add pixel values
			lowerCAmelCase__  :     Any       	=   features.pop('''pixel_values'''						)
			if return_overflowing_tokens is True:
					lowerCAmelCase__  :     List[Any]       	=   self.get_overflowing_images(_SCREAMING_SNAKE_CASE						,   encoded_inputs['''overflow_to_sample_mapping''']						)
			lowerCAmelCase__  :     Optional[Any]       	=   images

			return encoded_inputs




	def 				UpperCAmelCase__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						Any						,   _SCREAMING_SNAKE_CASE		:						Any						)->			List[Any]:
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCAmelCase__  :     Dict       	=   []
			for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

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

			return images_with_overflow




	def 				UpperCAmelCase__(   self		:						int						,   *_SCREAMING_SNAKE_CASE		:						Dict						,   **_SCREAMING_SNAKE_CASE		:						Union[str, Any]						)->			Union[str, Any]:
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						int						,   *_SCREAMING_SNAKE_CASE		:						str						,   **_SCREAMING_SNAKE_CASE		:						Optional[Any]						)->			int:
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	@property
	def 				UpperCAmelCase__(   self		:						Optional[int]						)->			int:
			return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




	@property
	def 				UpperCAmelCase__(   self		:						Any						)->			Union[str, Any]:
			warnings.warn(
			    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			return self.image_processor_class




	@property
	def 				UpperCAmelCase__(   self		:						List[Any]						)->			Dict:
			warnings.warn(
			    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			return self.image_processor








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase		=						{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




