
import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__lowerCamelCase							= """stas/tiny-wmt19-en-ru"""






class 						UpperCAmelCase	(		A_ ,unittest.TestCase				):
   A__      :	Dict		  =  FSMTTokenizer
   A__      :	Optional[Any]		  =  False



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Any:


          '''simple docstring'''


          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          snake_case :     List[str]           =   [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "w</w>",
              "r</w>",
              "t</w>",
              "lo",
              "low",
              "er</w>",
              "low</w>",
              "lowest</w>",
              "newer</w>",
              "wider</w>",
              "<unk>",
          ]
          snake_case :     Union[str, Any]           =   dict(zip(snake_case__					,		range(len(snake_case__				)				)				)				)
          snake_case :     Union[str, Any]           =   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

          snake_case :     List[str]           =   ["en", "ru"]
          snake_case :     List[str]           =   {
              "langs": self.langs,
              "src_vocab_size": 10,
              "tgt_vocab_size": 20,
          }

          snake_case :     List[str]           =   os.path.join(self.tmpdirname					,		VOCAB_FILES_NAMES["src_vocab_file"]				)
          snake_case :     Tuple           =   os.path.join(self.tmpdirname					,		VOCAB_FILES_NAMES["tgt_vocab_file"]				)
          snake_case :     Optional[Any]           =   os.path.join(self.tmpdirname					,		"tokenizer_config.json"				)
          snake_case :     Optional[int]           =   os.path.join(self.tmpdirname					,		VOCAB_FILES_NAMES["merges_file"]				)
          with open(self.src_vocab_file					,		"w"				) as fp:
                 fp.write(json.dumps(snake_case__				)				)
          with open(self.tgt_vocab_file					,		"w"				) as fp:
                 fp.write(json.dumps(snake_case__				)				)
          with open(self.merges_file					,		"w"				) as fp:
                 fp.write("\n".join(snake_case__				)				)
          with open(snake_case__					,		"w"				) as fp:
                 fp.write(json.dumps(snake_case__				)				)



   @cached_property
   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			List[Any]:


          '''simple docstring'''


          return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"				)



   @cached_property
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			List[str]:


          '''simple docstring'''


          return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"				)



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Optional[int]           =   FSMTTokenizer.from_pretrained(snake_case__				)
          self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]					,		["en", "ru"]				)
          self.assertEqual(tokenizer.src_vocab_size					,		21				)
          self.assertEqual(tokenizer.tgt_vocab_size					,		21				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Dict           =   FSMTTokenizer(self.langs					,		self.src_vocab_file					,		self.tgt_vocab_file					,		self.merges_file				)

          snake_case :     List[str]           =   "lower"
          snake_case :     int           =   ["low", "er</w>"]
          snake_case :     List[Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     List[str]           =   tokens + ["<unk>"]
          snake_case :     Union[str, Any]           =   [14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			int:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   self.tokenizer_ru_en

          snake_case :     int           =   tokenizer.encode("sequence builders"					,		add_special_tokens=snake_case__				)
          snake_case :     Any           =   tokenizer.encode("multi-sequence build"					,		add_special_tokens=snake_case__				)

          snake_case :     Optional[int]           =   tokenizer.build_inputs_with_special_tokens(snake_case__				)
          snake_case :     Any           =   tokenizer.build_inputs_with_special_tokens(snake_case__					,		snake_case__				)

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



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Any:


          '''simple docstring'''


          snake_case :     Dict           =   self.tokenizer_en_ru
          snake_case :     Optional[Any]           =   self.tokenizer_ru_en

          snake_case :     int           =   [
              [
                  "Here's a little song I wrote. Don't worry, be happy.",
                  [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
              ],
              ["This is it. No more. I'm done!", [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
          ]

          # if data needs to be recreated or added, run:
          # import torch
          # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
          # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

          for src_text, tgt_input_ids in targets:
                 snake_case :     str           =   tokenizer_enc.encode(snake_case__					,		return_tensors=snake_case__				)
                 self.assertListEqual(snake_case__					,		snake_case__				)

                 # and decode backward, using the reversed languages model
                 snake_case :     Optional[Any]           =   tokenizer_dec.decode(snake_case__					,		skip_special_tokens=snake_case__				)
                 self.assertEqual(snake_case__					,		snake_case__				)



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Tuple           =   FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"					,		do_lower_case=snake_case__				)
          snake_case :     Dict           =   tokenizer.tokenize("USA is United States of America"				)
          snake_case :     Union[str, Any]           =   ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
          self.assertListEqual(snake_case__					,		snake_case__				)



   @unittest.skip("FSMTConfig.__init__  requires non-optional args"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Any:


          '''simple docstring'''


          pass



   @unittest.skip("FSMTConfig.__init__  requires non-optional args"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Tuple:


          '''simple docstring'''


          pass







from __future__ import annotations






class 						UpperCAmelCase	:



   def __init__(self	:      List[str]					,		snake_case__	:      int				)	->			None:


          '''simple docstring'''


          snake_case :     Any           =   vertices
          snake_case :     str           =   [[0] * vertices for _ in range(snake_case__				)]



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      list[int]				)	->			None:


          '''simple docstring'''


          print("Vertex \t Distance from Source"				)
          for vertex in range(self.vertices				):
                 print(snake_case__					,		"\t\t"					,		distances_from_source[vertex]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      list[int]					,		snake_case__	:      list[bool]				)	->			int:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   1e7
          snake_case :     Any           =   0

          # Search not nearest vertex not in the shortest path tree
          for vertex in range(self.vertices				):
                 if distances_from_source[vertex] < minimum and visited[vertex] is False:
                        snake_case :     Union[str, Any]           =   distances_from_source[vertex]
                        snake_case :     str           =   vertex
          return min_index



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      int				)	->			None:


          '''simple docstring'''


          snake_case :     Optional[int]           =   [int(1e7				)] * self.vertices  # distances from the source
          snake_case :     Dict           =   0
          snake_case :     Union[str, Any]           =   [False] * self.vertices

          for _ in range(self.vertices				):
                 snake_case :     Dict           =   self.minimum_distance(snake_case__					,		snake_case__				)
                 snake_case :     List[str]           =   True

                 # Update dist value of the adjacent vertices
                 # of the picked vertex only if the current
                 # distance is greater than new distance and
                 # the vertex in not in the shortest path tree
                 for v in range(self.vertices				):
                        if (
                            self.graph[u][v] > 0
                            and visited[v] is False
                            and distances[v] > distances[u] + self.graph[u][v]
                        ):
                               snake_case :     Union[str, Any]           =   distances[u] + self.graph[u][v]

          self.print_solution(snake_case__				)


if __name__ == "__main__":
   __lowerCamelCase							= Graph(9)
   __lowerCamelCase							= [
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 11, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 14, 0, 0, 0],
       [0, 0, 0, 9, 0, 10, 0, 0, 0],
       [0, 0, 4, 14, 10, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 11, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)





