





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    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					["""image_processor""", """tokenizer"""]
       lowerCAmelCase__						=					"""LayoutLMv3ImageProcessor"""
       lowerCAmelCase__						=					("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")




       def __init__(  self			:   Union[str, Any]							,    A			:   str=None							,    A			:   Union[str, Any]=None							,    **A			:   Any		):
         __snake_case:						Any			  =						None
         if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""							,    A							,    )
           __snake_case:						Tuple			  =						kwargs.pop("""feature_extractor"""		)

         __snake_case:						List[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__(A							,    A		)




       def __call__(  self			:   Tuple							,    A			:   int							,    A			:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,    A			:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None							,    A			:   Union[List[List[int]], List[List[List[int]]]] = None							,    A			:   Optional[Union[List[int], List[List[int]]]] = None							,    A			:   bool = True							,    A			:   Union[bool, str, PaddingStrategy] = False							,    A			:   Union[bool, str, TruncationStrategy] = None							,    A			:   Optional[int] = None							,    A			:   int = 0							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    A			:   Optional[bool] = None							,    A			:   bool = False							,    A			:   bool = False							,    A			:   bool = False							,    A			:   bool = False							,    A			:   bool = True							,    A			:   Optional[Union[str, TensorType]] = None							,    **A			:   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
         __snake_case:						Union[str, Any]			  =						self.image_processor(images=A							,    return_tensors=A		)

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

         __snake_case:						List[Any]			  =						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=A							,    add_special_tokens=A							,    padding=A							,    truncation=A							,    max_length=A							,    stride=A							,    pad_to_multiple_of=A							,    return_token_type_ids=A							,    return_attention_mask=A							,    return_overflowing_tokens=A							,    return_special_tokens_mask=A							,    return_offsets_mapping=A							,    return_length=A							,    verbose=A							,    return_tensors=A							,    **A							,    )

         # add pixel values
         __snake_case:						Optional[Any]			  =						features.pop("""pixel_values"""		)
         if return_overflowing_tokens is True:
           __snake_case:						Dict			  =						self.get_overflowing_images(A							,    encoded_inputs["""overflow_to_sample_mapping"""]		)
         __snake_case:						Optional[Any]			  =						images

         return encoded_inputs




       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   Union[str, Any]							,    A			:   Tuple		):
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         __snake_case:						int			  =						[]
         for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]		)

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

         return images_with_overflow




       def 							UpperCAmelCase__			(  self			:   int							,    *A			:   Optional[int]							,    **A			:   int		):
         return self.tokenizer.batch_decode(*A							,    **A		)




       def 							UpperCAmelCase__			(  self			:   List[str]							,    *A			:   str							,    **A			:   Union[str, Any]		):
         return self.tokenizer.decode(*A							,    **A		)




       @property
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         return ["input_ids", "bbox", "attention_mask", "pixel_values"]




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






       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,    A							,    )
         return self.image_processor













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline






