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_					(				_a     ):
    UpperCAmelCase_      :							Union[str, Any]    			=				['image_processor', 'tokenizer']
    UpperCAmelCase_      :							Any    			=				'LayoutLMv2ImageProcessor'
    UpperCAmelCase_      :							Dict    			=				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




    def __init__(       self	: Tuple   ,   lowerCAmelCase	: int=None   ,   lowerCAmelCase	: Union[str, Any]=None   ,   **lowerCAmelCase	: Dict )						->			Tuple:
      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."   ,   _A   ,   )
        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__(_A   ,   _A )




    def __call__(       self	: Union[str, Any]   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,   lowerCAmelCase	: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,   lowerCAmelCase	: Union[List[List[int]], List[List[List[int]]]] = None   ,   lowerCAmelCase	: Optional[Union[List[int], List[List[int]]]] = None   ,   lowerCAmelCase	: bool = True   ,   lowerCAmelCase	: Union[bool, str, PaddingStrategy] = False   ,   lowerCAmelCase	: Union[bool, str, TruncationStrategy] = False   ,   lowerCAmelCase	: Optional[int] = None   ,   lowerCAmelCase	: int = 0   ,   lowerCAmelCase	: Optional[int] = None   ,   lowerCAmelCase	: Optional[bool] = None   ,   lowerCAmelCase	: Optional[bool] = None   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: bool = True   ,   lowerCAmelCase	: Optional[Union[str, TensorType]] = None   ,   **lowerCAmelCase	: Union[str, Any]   ,   )						->			Union[str, Any]:
      # 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." )

      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
      a__  		=  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 ):
          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=_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
      a__  		=  features.pop("pixel_values" )
      if return_overflowing_tokens is True:
        a__  		=  self.get_overflowing_images(_A   ,   encoded_inputs["overflow_to_sample_mapping"] )
      a__  		=  images

      return encoded_inputs




    def     __lowerCamelCase			(       self	: Tuple   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: Optional[int] )						->			int:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      a__  		=  []
      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     __lowerCamelCase			(       self	: Optional[Any]   ,   *lowerCAmelCase	: Tuple   ,   **lowerCAmelCase	: Any )						->			List[Any]:
      return self.tokenizer.batch_decode(*_A   ,   **_A )




    def     __lowerCamelCase			(       self	: Optional[int]   ,   *lowerCAmelCase	: str   ,   **lowerCAmelCase	: Union[str, Any] )						->			Tuple:
      return self.tokenizer.decode(*_A   ,   **_A )




    @property
    def     __lowerCamelCase			(       self	: Tuple )						->			Tuple:
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




    @property
    def     __lowerCamelCase			(       self	: str )						->			List[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     __lowerCamelCase			(       self	: List[str] )						->			Union[str, Any]:
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,   _A   ,   )
      return self.image_processor






from __future__ import annotations
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  )		->		list[float | int]:







						"""simple docstring"""

						if not all((nth_term, start_term_a, common_ratio_r)							):
												return []
						A__						:				list[float | int]         =			[]
						A__						:				Optional[Any]         =			1
						A__						:				List[Any]         =			common_ratio_r
						for _ in range(int(__lowercase							)							):
												if not series:
																		series.append(__lowercase							)
												else:
																		power += 1
																		series.append(float(start_term_a * multiple							)							)
																		A__						:				Optional[Any]         =			pow(float(__lowercase							)				,  __lowercase							)
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		snake_case     :    Optional[int]						=    float(input('Enter the last number (n term) of the Geometric Series'))
		snake_case     :    str						=    float(input('Enter the starting term (a) of the Geometric Series'))
		snake_case     :    int						=    float(
		    input('Enter the common ratio between two terms (r) of the Geometric Series')
		)
		print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))