





'''simple docstring'''







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        _UpperCAmelCase				(		lowerCAmelCase_  ):
		a  :  Union[str, Any]      =["image_processor", "tokenizer"]
		a  :  Dict      ="LayoutLMv3ImageProcessor"
		a  :  List[Any]      =("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
		def __init__(    self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE     ):



					'''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,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = 0,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):



					'''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
					__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,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					__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,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					return self.tokenizer.batch_decode(*UpperCAmelCase_,**UpperCAmelCase_     )
		def     lowerCamelCase__     (    self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE     ):



					'''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 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    if len(_snake_case							) == 1:
        return True
    lowercase__									=    series[1] - series[0]
    for index in range(len(_snake_case							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True




def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    lowercase__									=    0
    for val in series:
        answer += val
    return answer / len(_snake_case							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




