

'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			int     	= ["image_processor", "tokenizer"]
  __lowerCAmelCase       :			List[Any]     	= "FlavaImageProcessor"
  __lowerCAmelCase       :			Tuple     	= ("BertTokenizer", "BertTokenizerFast")
  def __init__(  self ,			a_=None ,			a_=None ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						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_ ,			)
      UpperCAmelCase    				=						kwargs.pop('feature_extractor'  )

    UpperCAmelCase    				=						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_  )
    UpperCAmelCase    				=						self.image_processor
  def __call__(  self ,			a_ = None ,			a_ = None ,			a_ = True ,			a_ = False ,			a_ = False ,			a_ = None ,			a_ = 0 ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = False ,			a_ = False ,			a_ = False ,			a_ = False ,			a_ = True ,			a_ = None ,			**a_ ,			)				->					Optional[int]:


    """simple docstring"""

    if text is None and images is None:
      raise ValueError('You have to specify either text or images. Both cannot be none.'  )

    if text is not None:
      UpperCAmelCase    				=						self.tokenizer(
          text=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_ ,			)
    if images is not None:
      UpperCAmelCase    				=						self.image_processor(
          a_ ,			return_image_mask=a_ ,			return_codebook_pixels=a_ ,			return_tensors=a_ ,			**a_ ,			)

    if text is not None and images is not None:
      encoding.update(a_  )
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**a_  ) ,			tensor_type=a_  )
  def     snake_case_      (  self ,			*a_ ,			**a_  )				->					Optional[Any]:


    """simple docstring"""
    return self.tokenizer.batch_decode(*a_ ,			**a_  )
  def     snake_case_      (  self ,			*a_ ,			**a_  )				->					Optional[int]:


    """simple docstring"""
    return self.tokenizer.decode(*a_ ,			**a_  )
  @property
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						self.tokenizer.model_input_names
    UpperCAmelCase    				=						self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
  @property
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    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     snake_case_      (  self  )				->					str:


    """simple docstring"""
    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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int | float | str							, SCREAMING_SNAKE_CASE   : int | float | str    ):
  if nth_term == "":
    return [""]
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						[]
  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()

  _a					:			List[Any]							=    int(input('Enter the last number (nth term) of the P-Series'))
  _a					:			Tuple							=    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))


