




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


a_      						=   logging.get_logger(__name__)

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

a_      						=   {
    "vocab_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json",
    },
    "merges_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "openai/clip-vit-base-patch32": (
            "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"
        ),
    },
}

a_      						=   {
    "openai/clip-vit-base-patch32": 77,
}

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Tuple			=    VOCAB_FILES_NAMES
      lowerCAmelCase__	:       Optional[int]			=    PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__	:       Optional[Any]			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__	:       Optional[Any]			=    ["input_ids", "attention_mask"]
      lowerCAmelCase__	:       Union[str, Any]			=    CLIPTokenizer







      def __init__(   self    :       Optional[Any]       ,				snake_case    :       List[Any]=None       ,				snake_case    :       int=None       ,				snake_case    :       Any=None       ,				snake_case    :       Optional[Any]="<|endoftext|>"       ,				snake_case    :       List[str]="<|startoftext|>"       ,				snake_case    :       Optional[Any]="<|endoftext|>"       ,				snake_case    :       int="<|endoftext|>"       ,				**snake_case    :       Any       ,				):
          super().__init__(
              snake_case       ,				snake_case       ,				tokenizer_file=snake_case       ,				unk_token=snake_case       ,				bos_token=snake_case       ,				eos_token=snake_case       ,				pad_token=snake_case       ,				**snake_case       ,				)

          if not isinstance(self.backend_tokenizer.pre_tokenizer       ,				pre_tokenizers.Sequence ):
              raise ValueError(
                  '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
                  ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
                  ''' to be compatible with this version.The easiest way to do so is'''
                  ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
                  ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
                  ''' transformers.''' )

          self._wrap_decode_method_backend_tokenizer()







      def 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  self.backend_tokenizer.decode

          def new_decode_method(*snake_case    :       str       ,				**snake_case    :       Union[str, Any] ):
              __UpperCamelCase     						=  orig_decode_method(*snake_case       ,				**snake_case )
              __UpperCamelCase     						=  text.replace(self.backend_tokenizer.model.end_of_word_suffix       ,				''' ''' ).strip()
              return text

          __UpperCamelCase     						=  new_decode_method







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       List[int]       ,				snake_case    :       Optional[List[int]] = None ):
          __UpperCamelCase     						=  [self.bos_token_id]
          __UpperCamelCase     						=  [self.eos_token_id]

          if token_ids_a is None:
              return bos_token + token_ids_a + eos_token
          return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token







      def 					snake_case		(   self    :       Tuple       ,				snake_case    :       List[int]       ,				snake_case    :       Optional[List[int]] = None ):
          __UpperCamelCase     						=  [self.bos_token_id]
          __UpperCamelCase     						=  [self.eos_token_id]

          if token_ids_a is None:
              return len(bos_token + token_ids_a + eos_token ) * [0]
          return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token ) * [0]







      def 					snake_case		(   self    :       Tuple       ,				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)}""")






