
"""simple docstring"""


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     int
						_lowerCamelCase:     int







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   Tuple	,A_  :   int   )  -> Union[str, Any]:
							A     = [[] for _ in range(A_   )]
							A     = size


						def __getitem__(			self  :   str	,A_  :   int   )  -> Iterator[Edge]:
							return iter(self._graph[vertex]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							return self._size


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   int	,A_  :   int	,A_  :   int   )  -> Tuple:
							if weight not in (0, 1):
								raise ValueError('Edge weight must be either 0 or 1.'   )

							if to_vertex < 0 or to_vertex >= self.size:
								raise ValueError('Vertex indexes must be in [0; size).'   )

							self._graph[from_vertex].append(Edge(A_	,A_   )   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   int	,A_  :   int   )  -> int | None:
							A     = deque([start_vertex]   )
							A     = [None] * self.size
							A     = 0

							while queue:
								A     = queue.popleft()
								A     = distances[current_vertex]
								if current_distance is None:
									continue

								for edge in self[current_vertex]:
									A     = current_distance + edge.weight
									A     = distances[edge.destination_vertex]
									if (
									    isinstance(A_	,A_   )
									    and new_distance >= dest_vertex_distance
									):
										continue
									A     = new_distance
									if edge.weight == 0:
										queue.appendleft(edge.destination_vertex   )
									else:
										queue.append(edge.destination_vertex   )

							if distances[finish_vertex] is None:
								raise ValueError('No path from start_vertex to finish_vertex.'   )

							return distances[finish_vertex]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

"""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]