



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 			__lowerCAmelCase  (						_UpperCamelCase ):
 lowerCAmelCase__							       =	['image_processor', 'tokenizer']
 lowerCAmelCase__							       =	'LayoutLMv3ImageProcessor'
 lowerCAmelCase__							       =	('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')




 def __init__(    self						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					**__UpperCAmelCase					):



    '''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.'''						,					__UpperCAmelCase						,					)
       __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__(__UpperCAmelCase						,					__UpperCAmelCase					)




 def __call__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = True						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = 0						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = True						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    # verify input
    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
    __lowerCamelCase    				=     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    				=     [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=__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    				=     features.pop('''pixel_values'''					)
    if return_overflowing_tokens is True:
       __lowerCamelCase    				=     self.get_overflowing_images(__UpperCAmelCase						,					encoded_inputs['''overflow_to_sample_mapping''']					)
    __lowerCamelCase    				=     images

    return encoded_inputs




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
    __lowerCamelCase    				=     []
    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						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    return self.tokenizer.batch_decode(*__UpperCAmelCase						,					**__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    return self.tokenizer.decode(*__UpperCAmelCase						,					**__UpperCAmelCase					)




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return ["input_ids", "bbox", "attention_mask", "pixel_values"]




 @property
 def 							lowerCamelCase   (    self					):



    '''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					):



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











"""simple docstring"""




def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  bool:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       0
     for ch in input_str:
          _UpperCAmelCase     :      Optional[int]														=       ord(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      int														=       pow(2		,    SCREAMING_SNAKE_CASE__							)

          # If we already turned on bit for current character's unicode
          if bitmap >> ch_unicode & 1 == 1:
               return False
          bitmap |= ch_bit_index_on
     return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
