


'''simple docstring'''

from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					'''MarkupLMFeatureExtractor'''
     UpperCAmelCase			       =					('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
     UpperCAmelCase			       =					True






     def __call__(      self  ,				__UpperCamelCase=None  ,				__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  ,				)							->     BatchEncoding:
          # first, create nodes and xpaths
          if self.parse_html:
               if html_strings is None:
                    raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"		)

               if nodes is not None or xpaths is not None or node_labels is not None:
                    raise ValueError(
                        "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"		)

               _a            =		self.feature_extractor(__UpperCamelCase		)
               _a            =		features["nodes"]
               _a            =		features["xpaths"]
          else:
               if html_strings is not None:
                    raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."		)
               if nodes is None or xpaths is None:
                    raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"		)

        # # second, apply the tokenizer
          if questions is not None and self.parse_html:
               if isinstance(__UpperCamelCase  ,				__UpperCamelCase		):
                    _a            =		[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

          _a            =		self.tokenizer(
              text=questions if questions is not None else nodes  ,				text_pair=nodes if questions is not None else None  ,				xpaths=__UpperCamelCase  ,				node_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  ,				)

          return encoded_inputs






     def 			a_       (      self  ,				*__UpperCamelCase  ,				**__UpperCamelCase		)							->     Optional[int]:
          return self.tokenizer.batch_decode(*__UpperCamelCase  ,				**__UpperCamelCase		)






     def 			a_       (      self  ,				*__UpperCamelCase  ,				**__UpperCamelCase		)							->     Tuple:
          return self.tokenizer.decode(*__UpperCamelCase  ,				**__UpperCamelCase		)






     @property
     def 			a_       (      self		)							->     Optional[int]:
          _a            =		self.tokenizer.model_input_names
          return tokenizer_input_names





'''simple docstring'''

from __future__ import annotations






def __UpperCamelCase (      __lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	)   ->  list[float | int]:







     '''simple docstring'''






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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowercase__					  =		float(input("Enter the last number (n term) of the Geometric Series"))
   lowercase__					  =		float(input("Enter the starting term (a) of the Geometric Series"))
   lowercase__					  =		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))

