from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowerCAmelCase__					=				TypeVar('T', bound='Enum')
@unique
class        a_				(		lowerCAmelCase_		):






	'''simple docstring'''






	UpperCAmelCase_							=      80
	UpperCAmelCase_							=      70
	UpperCAmelCase_							=      60
	UpperCAmelCase_							=      50
	UpperCAmelCase_							=      40
	UpperCAmelCase_							=      30
	UpperCAmelCase_							=      20
	UpperCAmelCase_							=      10






@unique
class        a_				(		lowerCAmelCase_		):






	'''simple docstring'''






	UpperCAmelCase_							=      24
	UpperCAmelCase_							=      21
	UpperCAmelCase_							=      18
	UpperCAmelCase_							=      15
	UpperCAmelCase_							=      12
	UpperCAmelCase_							=      9
	UpperCAmelCase_							=      6
	UpperCAmelCase_							=      3
	UpperCAmelCase_							=      2
	UpperCAmelCase_							=      1
	UpperCAmelCase_							=      -1
	UpperCAmelCase_							=      -2
	UpperCAmelCase_							=      -3
	UpperCAmelCase_							=      -6
	UpperCAmelCase_							=      -9
	UpperCAmelCase_							=      -12
	UpperCAmelCase_							=      -15
	UpperCAmelCase_							=      -18
	UpperCAmelCase_							=      -21
	UpperCAmelCase_							=      -24




	@classmethod
	def 	__snake_case		(    cls      :						Any):







								'''simple docstring'''



								return {unit.name: unit.value for unit in cls if unit.value > 0}




	@classmethod
	def 	__snake_case		(    cls      :						List[str]):







								'''simple docstring'''



								return {unit.name: unit.value for unit in cls if unit.value < 0}






def    __lowerCamelCase	(		lowerCAmelCase__			):
							lowerCAmelCase__  			=      SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
							for name_prefix, value_prefix in prefixes.items():
														lowerCAmelCase__  			=      value / (1_0**value_prefix)
														if numerical_part > 1:
																					return F"""{numerical_part!s} {name_prefix}"""
							return str(_SCREAMING_SNAKE_CASE			)






def    __lowerCamelCase	(		lowerCAmelCase__			):
							for prefix in BinaryUnit:
														lowerCAmelCase__  			=      value / (2**prefix.value)
														if numerical_part > 1:
																					return F"""{numerical_part!s} {prefix.name}"""
							return str(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

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_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




 def __init__(    self      :						List[Any]    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						int=None    ,					**lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      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__    ,					)
               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__(lowercase__    ,					lowercase__)




 def __call__(    self      :						Any    ,					lowercase__      :						Union[str, Any]    ,					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__      :						List[str]    ,					):







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

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        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      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						str    ,					**lowercase__      :						List[Any]):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''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]):







        '''simple docstring'''



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