


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 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							str	   =       'LayoutLMv3ImageProcessor'
     __lowercase				:							Tuple	   =       ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')

     def __init__(					self:Union[str, Any]     ,		_a:Dict=None     ,		_a:Optional[Any]=None     ,		**_a:Tuple   ):
       snake_case__						= None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''     ,		_a     ,		)
         snake_case__						= kwargs.pop('''feature_extractor'''   )

       snake_case__						= 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__(_a     ,		_a   )

     def __call__(					self:List[Any]     ,		_a:int     ,		_a:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,		_a:Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,		_a:Union[List[List[int]], List[List[List[int]]]] = None     ,		_a:Optional[Union[List[int], List[List[int]]]] = None     ,		_a:bool = True     ,		_a:Union[bool, str, PaddingStrategy] = False     ,		_a:Union[bool, str, TruncationStrategy] = None     ,		_a:Optional[int] = None     ,		_a:int = 0     ,		_a:Optional[int] = None     ,		_a:Optional[bool] = None     ,		_a:Optional[bool] = None     ,		_a:bool = False     ,		_a:bool = False     ,		_a:bool = False     ,		_a:bool = False     ,		_a:bool = True     ,		_a:Optional[Union[str, TensorType]] = None     ,		**_a:List[str]     ,		):
       # 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
       snake_case__						= self.image_processor(images=_a     ,		return_tensors=_a   )

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

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

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

       return encoded_inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int     ,		_a:int   ):
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       snake_case__						= []
       for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]   )

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

       return images_with_overflow

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		*_a:Optional[int]     ,		**_a:Tuple   ):
       return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		*_a:str     ,		**_a:Dict   ):
       return self.tokenizer.decode(*_a     ,		**_a   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,		_a     ,		)
       return self.image_processor_class







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		_a     ,		)
       return self.image_processor








'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


