





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


lowercase				:	List[Any]      						=  logging.get_logger(__name__)

lowercase				:	Optional[Any]      						=  {"""tokenizer_file""": """tokenizer.json"""}

lowercase				:	Optional[int]      						=  {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  VOCAB_FILES_NAMES
   __A       :    List[str]	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    List[Any]	  =  ['''input_ids''', '''attention_mask''']
   __A       :    str	  =  None

   def __init__( self ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase="<unk>" ,  lowercase="<s>" ,  lowercase="</s>" ,  lowercase="<pad>" ,  lowercase=False ,  lowercase=False ,  **lowercase ,  )      ->						int:







          '''simple docstring'''

          super().__init__(
              lowercase ,  lowercase ,  tokenizer_file=lowercase ,  unk_token=lowercase ,  bos_token=lowercase ,  eos_token=lowercase ,  pad_token=lowercase ,  add_prefix_space=lowercase ,  clean_up_tokenization_spaces=lowercase ,  **lowercase ,  )
          a__						:     List[Any]			=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('add_prefix_space' ,  lowercase) != add_prefix_space:
                 a__						:     Tuple			=    getattr(lowercase ,  pre_tok_state.pop('type'))
                 a__						:     Dict			=    add_prefix_space
                 a__						:     Any			=    pre_tok_class(**lowercase)

          a__						:     List[Any]			=    add_prefix_space

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     Tuple			=    kwargs.get('is_split_into_words' ,  lowercase)
          if not (self.add_prefix_space or not is_split_into_words):
                 raise Exception(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'
                     ' pretokenized inputs.')

          return super()._batch_encode_plus(*lowercase ,  **lowercase)

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     List[Any]			=    kwargs.get('is_split_into_words' ,  lowercase)

          if not (self.add_prefix_space or not is_split_into_words):
                 raise Exception(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'
                     ' pretokenized inputs.')

          return super()._encode_plus(*lowercase ,  **lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self._tokenizer.model.save(lowercase ,  name=lowercase)
          return tuple(lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						List[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    []
          for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(lowercase ,  add_special_tokens=lowercase) + [self.eos_token_id])

          if len(lowercase) > self.model_max_length:
                 a__						:     List[str]			=    input_ids[-self.model_max_length :]
          return input_ids









from __future__ import annotations

from collections.abc import Callable



def A_     (		A__		,				A__		,				A__		,				A__ = 100		,				)  ->		float:
       a__						:     Dict			=    x_start
       a__						:     Any			=    fnc(A__    )
       a__						:     Optional[int]			=    0.0
       for _ in range(A__    ):
              # Approximates small segments of curve as linear and solve
              # for trapezoidal area
              a__						:     Union[str, Any]			=    (x_end - x_start) / steps + xa
              a__						:     str			=    fnc(A__    )
              area += abs(fxa + fxa    ) * (xa - xa) / 2
              # Increment step
              a__						:     Optional[Any]			=    xa
              a__						:     Optional[int]			=    fxa
       return area


if __name__ == "__main__":



    def A_     (		A__    )  ->		List[str]:
           return x**3 + x**2

    print("""f(x) = x^3 + x^2""")
    print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
    lowercase				:	Union[str, Any]      						=  1_0
    while i <= 1_0_0_0_0_0:
        print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
        i *= 1_0


