




from __future__ import annotations

from collections.abc import Callable







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


if __name__ == "__main__":







  def        lowerCAmelCase_			(  snake_case_  ):
        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:")
  _snake_case     :   Any            = 10
  while i <= 100000:
    print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
    i *= 10







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

