

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			):







				"""simple docstring"""





				lowerCamelCase		     =							['image_processor', 'tokenizer']
				lowerCamelCase		     =							'LayoutLMv3ImageProcessor'
				lowerCamelCase		     =							('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')


				def __init__(				self			:	List[str],lowercase_			:	Optional[Any]=None,lowercase_			:	int=None,**lowercase_			:	List[str]		)->   Tuple:





								'''simple docstring'''







								A__   				=       None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
												    ' instead.',lowercase_,)
												A__   				=       kwargs.pop('feature_extractor'		)

								A__   				=       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__(lowercase_,lowercase_		)


				def __call__(				self			:	str,lowercase_			:	Optional[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] = None,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_			:	Tuple,)->   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.'		)

								# first, apply the image processor
								A__   				=       self.image_processor(images=lowercase_,return_tensors=lowercase_		)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(lowercase_,lowercase_		):
																A__   				=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
												A__   				=       features['words']

								A__   				=       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=lowercase_,add_special_tokens=lowercase_,padding=lowercase_,truncation=lowercase_,max_length=lowercase_,stride=lowercase_,pad_to_multiple_of=lowercase_,return_token_type_ids=lowercase_,return_attention_mask=lowercase_,return_overflowing_tokens=lowercase_,return_special_tokens_mask=lowercase_,return_offsets_mapping=lowercase_,return_length=lowercase_,verbose=lowercase_,return_tensors=lowercase_,**lowercase_,)

								# add pixel values
								A__   				=       features.pop('pixel_values'		)
								if return_overflowing_tokens is True:
												A__   				=       self.get_overflowing_images(lowercase_,encoded_inputs['overflow_to_sample_mapping']		)
								A__   				=       images

								return encoded_inputs


				def 						snake_case__					(				self			:	Any,lowercase_			:	Optional[Any],lowercase_			:	List[str]		)->   Dict:





								'''simple docstring'''







								A__   				=       []
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]		)

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

								return images_with_overflow


				def 						snake_case__					(				self			:	Union[str, Any],*lowercase_			:	Optional[Any],**lowercase_			:	Union[str, Any]		)->   Tuple:





								'''simple docstring'''







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


				def 						snake_case__					(				self			:	Optional[int],*lowercase_			:	Tuple,**lowercase_			:	Tuple		)->   Dict:





								'''simple docstring'''







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


				@property
				def 						snake_case__					(				self			:	List[str]		)->   Union[str, Any]:





								'''simple docstring'''







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


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





								'''simple docstring'''







								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',lowercase_,)
								return self.image_processor_class


				@property
				def 						snake_case__					(				self			:	List[Any]		)->   Dict:





								'''simple docstring'''







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




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

