
'''simple docstring'''

from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase												=   {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__UpperCAmelCase												=   {
    'openai-gpt': 512,
}


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= VOCAB_FILES_NAMES
   A		= PRETRAINED_VOCAB_FILES_MAP
   A		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A		= ['input_ids', 'attention_mask']
   A		= OpenAIGPTTokenizer






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE="<unk>"    ,**__SCREAMING_SNAKE_CASE      ):
    super().__init__(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,tokenizer_file=__SCREAMING_SNAKE_CASE    ,unk_token=__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )






   @property
   def 					__a			(       self      ):
    return True






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = None      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		self._tokenizer.model.save(__SCREAMING_SNAKE_CASE    ,name=__SCREAMING_SNAKE_CASE      )
    return tuple(__SCREAMING_SNAKE_CASE      )









'''simple docstring'''


from __future__ import annotations

from collections.abc import Callable




def       A							(      _UpperCAmelCase   :				Callable[[int | float], int | float]						,_UpperCAmelCase   :				int | float						,_UpperCAmelCase   :				int | float						,_UpperCAmelCase   :				int = 1_0_0						,)  ->				float:


  '''simple docstring'''




  __lowerCAmelCase		:    Dict			     =  x_start
  __lowerCAmelCase		:    Tuple			     =  fnc(_UpperCAmelCase					)
  __lowerCAmelCase		:    Optional[int]			     =  0.0

  for _ in range(_UpperCAmelCase					):
    # Approximates small segments of curve as linear and solve
    # for trapezoidal area
    __lowerCAmelCase		:    Optional[int]			     =  (x_end - x_start) / steps + xa
    __lowerCAmelCase		:    Optional[Any]			     =  fnc(_UpperCAmelCase					)
    area += abs(fxa + fxa					) * (xa - xa) / 2

    # Increment step
    __lowerCAmelCase		:    Optional[Any]			     =  xa
    __lowerCAmelCase		:    List[Any]			     =  fxa
  return area


if __name__ == "__main__":




    def       A							(      _UpperCAmelCase   :				List[Any]					)  ->				List[Any]:


      '''simple docstring'''




      return x**3

    print("f(x) = x^3")
    print("The area between the curve, x = -10, x = 10 and the x axis is:")
    A_				     =							10
    while i <= 10_00_00:
        A_				     =							trapezoidal_area(f, -5, 5, i)
        print(F'''with {i} steps: {area}''')
        i *= 10





