
'''simple docstring'''
import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 				UpperCAmelCase_			(		snake_case__      ,  unittest.TestCase   ):
 UpperCAmelCase_						      =  MPNetTokenizer
 UpperCAmelCase_						      =  MPNetTokenizerFast
 UpperCAmelCase_						      =  True
 UpperCAmelCase_						      =  True





 def     snake_case__						(      self):
     super().setUp()

     snake_case_	:      int            =  [
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     snake_case_	:      List[Any]            =  os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["vocab_file"])
     with open(self.vocab_file	,    "w"	,    encoding="utf-8") as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      List[str]            =  "UNwant\u00E9d,running"
     snake_case_	:      Any            =  "unwanted, running"
     return input_text, output_text





 def     snake_case__						(      self):
     snake_case_	:      Dict            =  self.tokenizer_class(self.vocab_file)

     snake_case_	:      int            =  tokenizer.tokenize("UNwant\u00E9d,running")
     self.assertListEqual(lowercase_	,    ["un", "##want", "##ed", ",", "runn", "##ing"])
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_)	,    [9, 6, 7, 12, 10, 11])





 @slow
 def     snake_case__						(      self):
     snake_case_	:      Union[str, Any]            =  self.tokenizer_class.from_pretrained("microsoft/mpnet-base")

     snake_case_	:      Dict            =  tokenizer.encode("sequence builders"	,    add_special_tokens=lowercase_)
     snake_case_	:      int            =  tokenizer.encode("multi-sequence build"	,    add_special_tokens=lowercase_)

     snake_case_	:      Optional[Any]            =  tokenizer.build_inputs_with_special_tokens(lowercase_)
     snake_case_	:      Dict            =  tokenizer.build_inputs_with_special_tokens(lowercase_	,    lowercase_)

     assert encoded_sentence == [0] + text + [2]
     assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE = 1_0_0,						):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  x_start
    snake_case_	:      str            =  fnc(__SCREAMING_SNAKE_CASE    )
    snake_case_	:      List[str]            =  0.0

    for _ in range(__SCREAMING_SNAKE_CASE    ):
        # Approximates small segments of curve as linear and solve
        # for trapezoidal area
        snake_case_	:      List[str]            =  (x_end - x_start) / steps + xa
        snake_case_	:      Optional[int]            =  fnc(__SCREAMING_SNAKE_CASE    )
        area += abs(fxa + fxa    ) * (xa - xa) / 2

        # Increment step
        snake_case_	:      List[Any]            =  xa
        snake_case_	:      Optional[Any]            =  fxa
    return area


if __name__ == "__main__":

  def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
      """simple docstring"""






      return x**3

  print("f(x) = x^3")
  print("The area between the curve, x = -10, x = 10 and the x axis is:")
  a_     =  10
  while i <= 10_0000:
    a_     =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 10




