


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 			lowercase_				(lowercase__      ):
    snake_case  						=["image_processor", "tokenizer"]
    snake_case  						="LayoutLMv2ImageProcessor"
    snake_case  						=("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")





    def __init__(							self					,     lowercase_=None					,     lowercase_=None					,     **lowercase_)     ->   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.'					,     UpperCAmelCase_					,     )
                  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__(UpperCAmelCase_					,     UpperCAmelCase_)





    def __call__(							self					,     lowercase_					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = True					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = None					,     lowercase_ = 0					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = True					,     lowercase_ = None					,     **lowercase_					,     )     ->   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=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_):
                         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=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
           a__     						=features.pop('pixel_values')
           if return_overflowing_tokens is True:
                  a__     						=self.get_overflowing_images(UpperCAmelCase_					,     encoded_inputs['overflow_to_sample_mapping'])
           a__     						=images

           return encoded_inputs





    def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   Dict:
           # 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(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 	__UpperCamelCase						(							self					,     *lowercase_					,     **lowercase_)     ->   Optional[int]:
           return self.tokenizer.batch_decode(*UpperCAmelCase_					,     **UpperCAmelCase_)





    def 	__UpperCamelCase						(							self					,     *lowercase_					,     **lowercase_)     ->   Any:
           return self.tokenizer.decode(*UpperCAmelCase_					,     **UpperCAmelCase_)





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





    @property
    def 	__UpperCamelCase						(							self)     ->   int:
           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 	__UpperCamelCase						(							self)     ->   Optional[int]:
           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,     UpperCAmelCase_					,     )
           return self.image_processor





from __future__ import annotations






def      _lowercase(				__a							:	float | int						,    __a							:	float | int						,    __a							:	float | int						,    ):
							if not all((nth_term, start_term_a, common_ratio_r)    ):
														return []
							a__     						=[]
							a__     						=1
							a__     						=common_ratio_r
							for _ in range(int(__a    )    ):
														if not series:
																					series.append(__a    )
														else:
																					power += 1
																					series.append(float(start_term_a * multiple    )    )
																					a__     						=pow(float(__a    )						,    __a    )
							return series


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCAmelCase:	Tuple      							=					float(input('Enter the last number (n term) of the Geometric Series'))
	_lowerCAmelCase:	Optional[int]      							=					float(input('Enter the starting term (a) of the Geometric Series'))
	_lowerCAmelCase:	List[Any]      							=					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))

