





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   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_   ):
       _UpperCAmelCase :    Union[str, Any]					       =       ["image_processor", "tokenizer"]
       _UpperCAmelCase :    str					       =       "LayoutLMv3ImageProcessor"
       _UpperCAmelCase :    Tuple					       =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")







       def __init__( self	:      Any						,   A	:      Tuple=None						,   A	:      int=None						,   **A	:      List[Any]     )  ->Union[str, Any]:
          lowerCamelCase__  :		List[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.'''						,   __lowerCAmelCase						,   )
             lowerCamelCase__  :		Union[str, Any]     =				kwargs.pop('''feature_extractor'''     )

          lowerCamelCase__  :		List[str]     =				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__(__lowerCAmelCase						,   __lowerCAmelCase     )







       def __call__( self	:      Optional[Any]						,   A	:      Any						,   A	:      int = None						,   A	:      Optional[int] = None						,   A	:      Optional[int] = None						,   A	:      Dict = None						,   A	:      List[str] = True						,   A	:      Tuple = False						,   A	:      Any = None						,   A	:      Optional[Any] = None						,   A	:      Any = 0						,   A	:      Optional[Any] = None						,   A	:      List[str] = None						,   A	:      Optional[int] = None						,   A	:      int = False						,   A	:      Optional[Any] = False						,   A	:      Dict = False						,   A	:      Union[str, Any] = False						,   A	:      Tuple = True						,   A	:      Optional[Any] = None						,   **A	:      List[Any]						,   )  ->List[str]:
          # 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.'''     )

          # first, apply the image processor
          lowerCamelCase__  :		Union[str, Any]     =				self.image_processor(images=__lowerCAmelCase						,   return_tensors=__lowerCAmelCase     )

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

          lowerCamelCase__  :		Dict     =				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=__lowerCAmelCase						,   add_special_tokens=__lowerCAmelCase						,   padding=__lowerCAmelCase						,   truncation=__lowerCAmelCase						,   max_length=__lowerCAmelCase						,   stride=__lowerCAmelCase						,   pad_to_multiple_of=__lowerCAmelCase						,   return_token_type_ids=__lowerCAmelCase						,   return_attention_mask=__lowerCAmelCase						,   return_overflowing_tokens=__lowerCAmelCase						,   return_special_tokens_mask=__lowerCAmelCase						,   return_offsets_mapping=__lowerCAmelCase						,   return_length=__lowerCAmelCase						,   verbose=__lowerCAmelCase						,   return_tensors=__lowerCAmelCase						,   **__lowerCAmelCase						,   )

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

          return encoded_inputs







       def 		__lowerCamelCase       ( self	:      int						,   A	:      str						,   A	:      Any     )  ->Optional[Any]:
          lowerCamelCase__  :		Optional[int]     =				[]
          for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]     )

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

          return images_with_overflow







       def 		__lowerCamelCase       ( self	:      Union[str, Any]						,   *A	:      Any						,   **A	:      Any     )  ->Union[str, Any]:
          return self.tokenizer.batch_decode(*__lowerCAmelCase						,   **__lowerCAmelCase     )







       def 		__lowerCamelCase       ( self	:      Dict						,   *A	:      Optional[int]						,   **A	:      Any     )  ->str:
          return self.tokenizer.decode(*__lowerCAmelCase						,   **__lowerCAmelCase     )







       @property
       def 		__lowerCamelCase       ( self	:      Tuple     )  ->str:
          return ["input_ids", "bbox", "attention_mask", "pixel_values"]







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







       @property
       def 		__lowerCamelCase       ( self	:      str     )  ->Tuple:
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,   __lowerCAmelCase						,   )
          return self.image_processor











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,       __lowerCAmelCase							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   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__(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""

								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''  )

								if text is not None:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class






