'''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__      (   UpperCAmelCase__ ):
     lowerCamelCase  :     Optional[int]							=["image_processor", "tokenizer"]
     lowerCamelCase  :     Union[str, Any]							="LayoutLMv2ImageProcessor"
     lowerCamelCase  :     int							=("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")






     def __init__( self		:		Optional[int]  ,				a		:		Any=None  ,				a		:		Any=None  ,				**a		:		Union[str, Any]    ):





         """simple docstring"""



         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''  ,				a  ,				)
             __lowerCamelCase		=  kwargs.pop('''feature_extractor'''    )

         __lowerCamelCase		=  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		:		Optional[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		:		Tuple  ,				):





         """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.'''    )

         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		=  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    ):
                 __lowerCamelCase		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
             __lowerCamelCase		=  features['''words''']

         __lowerCamelCase		=  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
         __lowerCamelCase		=  features.pop('''pixel_values'''    )
         if return_overflowing_tokens is True:
             __lowerCamelCase		=  self.get_overflowing_images(a  ,				encoded_inputs['''overflow_to_sample_mapping''']    )
         __lowerCamelCase		=  images

         return encoded_inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		Optional[Any]  ,				a		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  []
         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    SCREAMING_SNAKE_CASE__   ( self		:		List[str]  ,				*a		:		Optional[Any]  ,				**a		:		Union[str, Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]  ,				*a		:		Union[str, Any]  ,				**a		:		Tuple    ):





         """simple docstring"""



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






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



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






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         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    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



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







"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result