


"""simple docstring"""




class    __lowerCAmelCase   :


		'''simple docstring'''



		def __init__(    self ):
				__a	 =     ''''''
				__a	 =     ''''''
				__a	 =     []

		def      __UpperCAmelCase			(    self				,					_a				,					_a ):
				if m == -1:
						return n + 1
				elif n == -1:
						return m + 1
				elif self.dp[m][n] > -1:
						return self.dp[m][n]
				else:
						if self.worda[m] == self.worda[n]:
								__a	 =     self.__min_dist_top_down_dp(m - 1				,					n - 1 )
						else:
								__a	 =     self.__min_dist_top_down_dp(A_				,					n - 1 )
								__a	 =     self.__min_dist_top_down_dp(m - 1				,					A_ )
								__a	 =     self.__min_dist_top_down_dp(m - 1				,					n - 1 )
								__a	 =     1 + min(A_				,					A_				,					A_ )

						return self.dp[m][n]

		def      __UpperCAmelCase			(    self				,					_a				,					_a ):
				__a	 =     worda
				__a	 =     worda
				__a	 =     [[-1 for _ in range(len(A_ ) )] for _ in range(len(A_ ) )]

				return self.__min_dist_top_down_dp(len(A_ ) - 1				,					len(A_ ) - 1 )





		def      __UpperCAmelCase			(    self				,					_a				,					_a ):
				__a	 =     worda
				__a	 =     worda
				__a	 =     len(A_ )
				__a	 =     len(A_ )
				__a	 =     [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]

				for i in range(m + 1 ):
						for j in range(n + 1 ):
								if i == 0:  # first string is empty
										__a	 =     j
								elif j == 0:  # second string is empty
										__a	 =     i
								elif worda[i - 1] == worda[j - 1]:  # last characters are equal
										__a	 =     self.dp[i - 1][j - 1]
								else:
										__a	 =     self.dp[i][j - 1]
										__a	 =     self.dp[i - 1][j]
										__a	 =     self.dp[i - 1][j - 1]
										__a	 =     1 + min(A_				,					A_				,					A_ )
				return self.dp[m][n]


if __name__ == "__main__":
				lowercase_     	=    EditDistance()

				print("****************** Testing Edit Distance DP Algorithm ******************")
				print()

				lowercase_     	=    input("Enter the first string: ").strip()
				lowercase_     	=    input("Enter the second string: ").strip()

				print()
				print(F'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
				print(F'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
				print()
				print("*************** End of Testing Edit Distance DP Algorithm ***************")








"""simple docstring"""


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 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     BlenderbotSmallTokenizer
						_lowerCamelCase:     List[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							super().setUp()

							A     = ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
							A     = dict(zip(A_	,range(len(A_   )   )   )   )

							A     = ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
							A     = {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,**A_  :   Union[str, Any]   )  -> Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = 'adapt act apte'
							A     = 'adapt act apte'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							A     = BlenderbotSmallTokenizer(self.vocab_file	,self.merges_file	,**self.special_tokens_map   )
							A     = 'adapt act apte'
							A     = ['adapt', 'act', 'ap@@', 'te']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

							A     = [0, 1, 2, 3, 4, 5]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							assert tok('sam'   ).input_ids == [1384]
							A     = 'I am a small frog.'
							A     = tok([src_text]	,padding=A_	,truncation=A_   )['input_ids']
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							A     = 'I am a small frog .'
							A     = '.'
							A     = tok(A_   )['input_ids']
							A     = tok(A_   )['input_ids']

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