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      ):
					__lowerCamelCase			   =  ["""image_processor""", """tokenizer"""]
					__lowerCamelCase			   =  """LayoutLMv2ImageProcessor"""
					__lowerCamelCase			   =  ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")




					def __init__(  self       ,			__UpperCamelCase=None       ,			__UpperCamelCase=None       ,			**__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      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.'       ,			__UpperCamelCase       ,			)
													snake_case__   :      Optional[int]   				= kwargs.pop('feature_extractor'						)

									snake_case__   :      Optional[int]   				= 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       ,			__UpperCamelCase       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = True       ,			__UpperCamelCase = False       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			__UpperCamelCase = 0       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = False       ,			__UpperCamelCase = False       ,			__UpperCamelCase = False       ,			__UpperCamelCase = False       ,			__UpperCamelCase = True       ,			__UpperCamelCase = None       ,			**__UpperCamelCase       ,			)	->    BatchEncoding:



									'''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
									snake_case__   :      Union[str, 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						):
																	snake_case__   :      Tuple   				= [text]  # add batch dimension (as the image processor always adds a batch dimension)
													snake_case__   :      Optional[int]   				= features['words']

									snake_case__   :      str   				= 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
									snake_case__   :      Dict   				= features.pop('pixel_values'						)
									if return_overflowing_tokens is True:
													snake_case__   :      Union[str, Any]   				= self.get_overflowing_images(__UpperCamelCase       ,			encoded_inputs['overflow_to_sample_mapping']						)
									snake_case__   :      str   				= images

									return encoded_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= []
									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      __a     (  self       ,			*__UpperCamelCase       ,			**__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







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




					def      __a     (  self       ,			*__UpperCamelCase       ,			**__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







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




					@property
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







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




					@property
					def      __a     (  self						)	->    int:



									'''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      __a     (  self						)	->    Dict:



									'''simple docstring'''







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



from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
				from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


