

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					int      =						BlenderbotSmallTokenizer
 _lowercase     :					Union[str, Any]      =						False

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      super().setUp()

      _lowerCAmelCase   :					List[Any]        =    ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
      _lowerCAmelCase   :					Optional[Any]        =    dict(zip(A_     ,						range(len(A_       )       )       )       )

      _lowerCAmelCase   :					Any        =    ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
      _lowerCAmelCase   :					Dict        =    {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

      _lowerCAmelCase   :					int        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					Dict        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["merges_file"]       )
      with open(self.vocab_file     ,						"w"     ,						encoding="utf-8"       ) as fp:
           fp.write(json.dumps(A_       ) + "\n"       )
      with open(self.merges_file     ,						"w"     ,						encoding="utf-8"       ) as fp:
           fp.write("\n".join(A_       )       )

 def    __magic_name__       (		self :	Any     ,						**A_ :	Tuple       ):






      '''simple docstring'''




      kwargs.update(self.special_tokens_map       )
      return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname     ,						**A_       )

 def    __magic_name__       (		self :	str     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    "adapt act apte"
      _lowerCAmelCase   :					Tuple        =    "adapt act apte"
      return input_text, output_text

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    BlenderbotSmallTokenizer(self.vocab_file     ,						self.merges_file     ,						**self.special_tokens_map       )
      _lowerCAmelCase   :					Tuple        =    "adapt act apte"
      _lowerCAmelCase   :					Any        =    ["adapt", "act", "ap@@", "te"]
      _lowerCAmelCase   :					int        =    tokenizer.tokenize(A_       )
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Any        =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

      _lowerCAmelCase   :					Optional[Any]        =    [0, 1, 2, 3, 4, 5]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )     ,						A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"       )
      assert tok("sam"       ).input_ids == [1_3_8_4]
      _lowerCAmelCase   :					Tuple        =    "I am a small frog."
      _lowerCAmelCase   :					Tuple        =    tok([src_text]     ,						padding=A_     ,						truncation=A_       )["input_ids"]
      _lowerCAmelCase   :					Tuple        =    tok.batch_decode(A_     ,						skip_special_tokens=A_     ,						clean_up_tokenization_spaces=A_       )[0]
      assert src_text != decoded  # I wish it did!
      assert decoded == "i am a small frog ."


 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"       )
      _lowerCAmelCase   :					Optional[int]        =    "I am a small frog ."
      _lowerCAmelCase   :					str        =    "."
      _lowerCAmelCase   :					Optional[Any]        =    tok(A_       )["input_ids"]
      _lowerCAmelCase   :					Tuple        =    tok(A_       )["input_ids"]

      assert encoded[-1] == encoded_dot[0]









def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      "list[int]":

     """simple docstring"""







     if upper_limit < 0:
          raise ValueError("Limit for the Catalan sequence must be ≥ 0"				)

     _lowerCAmelCase   :					Optional[Any]        =    [0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     _lowerCAmelCase   :					Optional[int]        =    1
     if upper_limit > 0:
          _lowerCAmelCase   :					Any        =    1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2		,							upper_limit + 1				):
          for j in range(SCREAMING_SNAKE_CASE				):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
  print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
  print('\n*** Enter -1 at any time to quit ***')
  print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
  try:
    while True:
      __UpperCAmelCase    				=     int(input().strip())
      if N < 0:
        print('\n********* Goodbye!! ************')
        break
      else:
        print(f'''The Catalan numbers from 0 through {N} are:''')
        print(catalan_numbers(N))
        print('Try another upper limit for the sequence: ', end='')
  except (NameError, ValueError):
    print('\n********* Invalid input, goodbye! ************\n')

  import doctest

  doctest.testmod()






