






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__  (						UpperCAmelCase_       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			BlenderbotSmallTokenizer
     _A       =			False







     def 						_a      (self    ):






           '''simple docstring'''



           super().setUp()

           lowerCamelCase								=		["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
           lowerCamelCase								=		dict(zip(__a				,			range(len(__a    )    )    )    )

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

           lowerCamelCase								=		os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["vocab_file"]    )
           lowerCamelCase								=		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 						_a      (self				,			**__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		"adapt act apte"
           lowerCamelCase								=		"adapt act apte"
           return input_text, output_text







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		BlenderbotSmallTokenizer(self.vocab_file				,			self.merges_file				,			**self.special_tokens_map    )
           lowerCamelCase								=		"adapt act apte"
           lowerCamelCase								=		["adapt", "act", "ap@@", "te"]
           lowerCamelCase								=		tokenizer.tokenize(__a    )
           self.assertListEqual(__a				,			__a    )

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

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"    )
           assert tok("sam"    ).input_ids == [13_84]
           lowerCamelCase								=		"I am a small frog."
           lowerCamelCase								=		tok([src_text]				,			padding=__a				,			truncation=__a    )["input_ids"]
           lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"    )
           lowerCamelCase								=		"I am a small frog ."
           lowerCamelCase								=		"."
           lowerCamelCase								=		tok(__a    )["input_ids"]
           lowerCamelCase								=		tok(__a    )["input_ids"]

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




from __future__ import annotations

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





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[[] for _ in range(__a    )]
           lowerCamelCase								=		size







     def __getitem__(self				,			__a    ):






           '''simple docstring'''



           return iter(self._graph[vertex]    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self._size







     def 						_a      (self				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           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 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		deque([start_vertex]    )
           lowerCamelCase								=		[None] * self.size
           lowerCamelCase								=		0

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

                 for edge in self[current_vertex]:
                       lowerCamelCase								=		current_distance + edge.weight
                       lowerCamelCase								=		distances[edge.destination_vertex]
                       if (
                           isinstance(__a				,			__a    )
                           and new_distance >= dest_vertex_distance
                       ):
                             continue
                       lowerCamelCase								=		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()