




from typing import Optional, Tuple

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


a_      						=   logging.get_logger(__name__)

a_      						=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_      						=   {
    "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"},
}

a_      						=   {
    "openai-gpt": 512,
}

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Union[str, Any]			=    VOCAB_FILES_NAMES
      lowerCAmelCase__	:       str			=    PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__	:       List[str]			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__	:       int			=    ["input_ids", "attention_mask"]
      lowerCAmelCase__	:       int			=    OpenAIGPTTokenizer







      def __init__(   self    :       List[Any]       ,				snake_case    :       List[str]=None       ,				snake_case    :       Optional[Any]=None       ,				snake_case    :       str=None       ,				snake_case    :       Union[str, Any]="<unk>"       ,				**snake_case    :       List[Any] ):
          super().__init__(snake_case       ,				snake_case       ,				tokenizer_file=snake_case       ,				unk_token=snake_case       ,				**snake_case )







      @property
      def 					snake_case		(   self    :       Optional[int] ):
          return True







      def 					snake_case		(   self    :       Any       ,				snake_case    :       str       ,				snake_case    :       Optional[str] = None ):
          __UpperCamelCase     						=  self._tokenizer.model.save(snake_case       ,				name=snake_case )
          return tuple(snake_case )












import random

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       float       ,				snake_case    :       float ):
          __UpperCamelCase     						=  x
          __UpperCamelCase     						=  y







      def 					snake_case		(   self    :       List[Any] ):
          return (self.x**2 + self.y**2) <= 1







      @classmethod
      def 					snake_case		(   cls    :       List[str] ):
          return cls(x=random.random()       ,				y=random.random() )







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		float:

    """simple docstring"""


    if number_of_simulations < 1:
        raise ValueError('''At least one simulation is necessary to estimate PI.'''					)

    __UpperCamelCase     						=  0
    for _ in range(lowercase_					):
        __UpperCamelCase     						=  Point.random_unit_square()

        if random_point.is_in_unit_circle():
            number_in_unit_circle += 1

    return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
 # import doctest

 # doctest.testmod()
 from math import pi

 a_      						=   "Please enter the desired number of Monte Carlo simulations: "
 a_      						=   estimate_pi(int(input(prompt).strip()))
 print(f"""An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}""")






