





'''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 							__SCREAMING_SNAKE_CASE			(       __SCREAMING_SNAKE_CASE      ):
      '''simple docstring'''



      lowerCAmelCase__       :      List[Any] 		=		["image_processor", "tokenizer"]
      lowerCAmelCase__       :      Optional[int] 		=		"LayoutLMv2ImageProcessor"
      lowerCAmelCase__       :      int 		=		("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")

      def __init__(							self    : Optional[int]   ,UpperCamelCase    : List[str]=None   ,UpperCamelCase    : Optional[int]=None   ,**UpperCamelCase    : Optional[Any]  )		->  Union[str, Any]:
           _lowercase							:      Dict  						=							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__   ,)
                _lowercase							:      Dict  						=							kwargs.pop('feature_extractor'  )

           _lowercase							:      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__(UpperCamelCase__   ,UpperCamelCase__  )

      def __call__(							self    : Any   ,UpperCamelCase    : Optional[Any]   ,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] = False   ,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    : Optional[int]   ,)		->  BatchEncoding:







           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
           _lowercase							:      List[Any]  						=							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__  ):
                     _lowercase							:      Optional[Any]  						=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
                _lowercase							:      Any  						=							features['''words''']

           _lowercase							:      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=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
           _lowercase							:      Optional[Any]  						=							features.pop('pixel_values'  )
           if return_overflowing_tokens is True:
                _lowercase							:      int  						=							self.get_overflowing_images(UpperCamelCase__   ,encoded_inputs['overflow_to_sample_mapping']  )
           _lowercase							:      List[str]  						=							images

           return encoded_inputs

      def   _lowerCamelCase		(							self    : List[Any]   ,UpperCamelCase    : Union[str, Any]   ,UpperCamelCase    : Union[str, Any]  )		->  str:
           _lowercase							:      List[Any]  						=							[]
           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    : Any   ,*UpperCamelCase    : str   ,**UpperCamelCase    : Any  )		->  Dict:
           return self.tokenizer.batch_decode(*UpperCamelCase__   ,**UpperCamelCase__  )

      def   _lowerCamelCase		(							self    : Dict   ,*UpperCamelCase    : Optional[Any]   ,**UpperCamelCase    : str  )		->  str:
           return self.tokenizer.decode(*UpperCamelCase__   ,**UpperCamelCase__  )

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

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









'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available



@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      Union[List[PIL.Image.Image], np.ndarray]
      lowerCAmelCase__       :      Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .modeling_roberta_series import RobertaSeriesModelWithTransformation
    from .pipeline_alt_diffusion import AltDiffusionPipeline
    from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline