


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_  ):
						UpperCamelCase__							:						Union[str, Any]   =["image_processor", "tokenizer"]
						UpperCamelCase__							:						Tuple   ="LayoutLMv2ImageProcessor"
						UpperCamelCase__							:						str   =("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






						def __init__(      self		:  Optional[int]    ,     lowercase_		:  Optional[int]=None    ,     lowercase_		:  Optional[int]=None    ,     **lowercase_		:  int				)      ->		Optional[Any]:
													"""simple docstring"""




													_lowerCamelCase :	Union[str, 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.'    ,     UpperCAmelCase_    ,     )
																				_lowerCamelCase :	int						=kwargs.pop('feature_extractor'				)

													_lowerCamelCase :	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__(UpperCAmelCase_    ,     UpperCAmelCase_				)






						def __call__(      self		:  Tuple    ,     lowercase_		:  int    ,     lowercase_		:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,     lowercase_		:  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,     lowercase_		:  Union[List[List[int]], List[List[List[int]]]] = None    ,     lowercase_		:  Optional[Union[List[int], List[List[int]]]] = None    ,     lowercase_		:  bool = True    ,     lowercase_		:  Union[bool, str, PaddingStrategy] = False    ,     lowercase_		:  Union[bool, str, TruncationStrategy] = False    ,     lowercase_		:  Optional[int] = None    ,     lowercase_		:  int = 0    ,     lowercase_		:  Optional[int] = None    ,     lowercase_		:  Optional[bool] = None    ,     lowercase_		:  Optional[bool] = None    ,     lowercase_		:  bool = False    ,     lowercase_		:  bool = False    ,     lowercase_		:  bool = False    ,     lowercase_		:  bool = False    ,     lowercase_		:  bool = True    ,     lowercase_		:  Optional[Union[str, TensorType]] = None    ,     **lowercase_		:  Union[str, Any]    ,     )      ->		Any:
													"""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 :	Optional[int]						=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_				):
																											_lowerCamelCase :	str						=[text]  # add batch dimension (as the image processor always adds a batch dimension)
																				_lowerCamelCase :	Union[str, Any]						=features['words']

													_lowerCamelCase :	Tuple						=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
													_lowerCamelCase :	Union[str, Any]						=features.pop('pixel_values'				)
													if return_overflowing_tokens is True:
																				_lowerCamelCase :	List[Any]						=self.get_overflowing_images(UpperCAmelCase_    ,     encoded_inputs['overflow_to_sample_mapping']				)
													_lowerCamelCase :	Tuple						=images

													return encoded_inputs






						def  lowerCamelCase (      self		:  Optional[int]    ,     lowercase_		:  Dict    ,     lowercase_		:  Union[str, Any]				)      ->		int:
													"""simple docstring"""




													_lowerCamelCase :	Union[str, 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		:  Optional[int]    ,     *lowercase_		:  Optional[int]    ,     **lowercase_		:  List[str]				)      ->		Union[str, Any]:
													"""simple docstring"""




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






						def  lowerCamelCase (      self		:  str    ,     *lowercase_		:  List[Any]    ,     **lowercase_		:  Optional[Any]				)      ->		Optional[int]:
													"""simple docstring"""




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






						@property
						def  lowerCamelCase (      self		:  Tuple				)      ->		List[Any]:
													"""simple docstring"""




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






						@property
						def  lowerCamelCase (      self		:  Any				)      ->		Dict:
													"""simple docstring"""




													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		:  str				)      ->		Tuple:
													"""simple docstring"""




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






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

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline


