

from typing import Optional, Tuple

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


lowerCAmelCase						:   int										=    logging.get_logger(__name__)

lowerCAmelCase						:   Optional[int]										=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase						:   Union[str, Any]										=    {
    '''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'''},
}

lowerCAmelCase						:   Union[str, Any]										=    {
    '''openai-gpt''': 512,
}
class     SCREAMING_SNAKE_CASE__     (						a__		):





       '''simple docstring'''




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

       def __init__(						self       :						Dict    ,		lowerCAmelCase__       :						str=None    ,		lowerCAmelCase__       :						Union[str, Any]=None    ,		lowerCAmelCase__       :						List[Any]=None    ,		lowerCAmelCase__       :						Dict="<unk>"    ,		**lowerCAmelCase__       :						int							)			->    List[str]:
             super().__init__(SCREAMING_SNAKE_CASE_    ,		SCREAMING_SNAKE_CASE_    ,		tokenizer_file=SCREAMING_SNAKE_CASE_    ,		unk_token=SCREAMING_SNAKE_CASE_    ,		**SCREAMING_SNAKE_CASE_							)

       @property
       def UpperCAmelCase_						(						self       :						Any							)			->    Dict:
             return True

       def UpperCAmelCase_						(						self       :						str    ,		lowerCAmelCase__       :						Tuple    ,		lowerCAmelCase__       :						str = None							)			->    Tuple[str]:
             snake_case__					=   self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,		name=SCREAMING_SNAKE_CASE_							)
             return tuple(SCREAMING_SNAKE_CASE_							)









lowerCAmelCase						:   Optional[Any]										=    1000





def 			_lowercase    (      __UpperCamelCase     :		float ):
      return x * x





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		int = 4 ):
      assert callable(
          __UpperCamelCase ), F'''the function(object) passed should be callable your input : {function}'''
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''a should be float or integer your input : {a}'''
      assert isinstance(function(__UpperCamelCase )   ,       (float, int) ), (
          "the function should return integer or float return type of your function, "
          F'''{type(__UpperCamelCase )}'''
      )
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''b should be float or integer your input : {b}'''
      assert (
          isinstance(__UpperCamelCase   ,       __UpperCamelCase ) and precision > 0
      ), F'''precision should be positive integer your input : {precision}'''

      # just applying the formula of simpson for approximate integration written in
      # mentioned article in first comment of this file and above this function

      snake_case__					=   (b - a) / N_STEPS
      snake_case__					=   function(__UpperCamelCase ) + function(__UpperCamelCase )

      for i in range(1   ,       __UpperCamelCase ):
            snake_case__					=   a + h * i
            result += function(__UpperCamelCase ) * (4 if i % 2 else 2)

      result *= h / 3
      return round(__UpperCamelCase   ,       __UpperCamelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






