"""simple docstring"""






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCAmelCase__  :  int											=     get_tests_dir('fixtures/test_sentencepiece.model')
@require_torch
@require_torchaudio
@require_sentencepiece
class     snake_case	(			unittest.TestCase    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							tempfile.mkdtemp()

									UpperCAmelCase__					=							["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
									UpperCAmelCase__					=							dict(zip(__lowerCamelCase   ,range(len(__lowerCamelCase      )      )      )      )
									UpperCAmelCase__					=							Path(self.tmpdirname      )
									save_json(__lowerCamelCase   ,save_dir / VOCAB_FILES_NAMES['vocab_file']      )
									if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
													copyfile(__lowerCamelCase   ,save_dir / VOCAB_FILES_NAMES['spm_file']      )

									UpperCAmelCase__					=							SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
									tokenizer.save_pretrained(self.tmpdirname      )

									UpperCAmelCase__					=							{
									    "feature_size": 24,
									    "num_mel_bins": 24,
									    "padding_value": 0.0,
									    "sampling_rate": 16_000,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}
									save_json(__lowerCamelCase   ,save_dir / FEATURE_EXTRACTOR_NAME      )







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,**lowerCamelCase__       :     int      ):
									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,**__lowerCamelCase      )







					def 		__lowerCAmelCase						(    self       :     Dict   ,**lowerCamelCase__       :     int      ):
									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,**__lowerCamelCase      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									shutil.rmtree(self.tmpdirname      )







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							self.get_tokenizer()
									UpperCAmelCase__					=							self.get_feature_extractor()

									UpperCAmelCase__					=							SpeechaTextProcessor(tokenizer=__lowerCamelCase   ,feature_extractor=__lowerCamelCase      )

									processor.save_pretrained(self.tmpdirname      )
									UpperCAmelCase__					=							SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

									self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer.get_vocab()      )
									self.assertIsInstance(processor.tokenizer   ,__lowerCamelCase      )

									self.assertEqual(processor.feature_extractor.to_json_string()   ,feature_extractor.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor   ,__lowerCamelCase      )







					def 		__lowerCAmelCase						(    self       :     str      ):
									UpperCAmelCase__					=							SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()   ,feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									UpperCAmelCase__					=							self.get_tokenizer(bos_token='(BOS)'   ,eos_token='(EOS)'      )
									UpperCAmelCase__					=							self.get_feature_extractor(do_normalize=__lowerCamelCase   ,padding_value=1.0      )

									UpperCAmelCase__					=							SpeechaTextProcessor.from_pretrained(
									    self.tmpdirname   ,bos_token='(BOS)'   ,eos_token='(EOS)'   ,do_normalize=__lowerCamelCase   ,padding_value=1.0      )

									self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()      )
									self.assertIsInstance(processor.tokenizer   ,__lowerCamelCase      )

									self.assertEqual(processor.feature_extractor.to_json_string()   ,feature_extractor_add_kwargs.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor   ,__lowerCamelCase      )







					def 		__lowerCAmelCase						(    self       :     Optional[Any]      ):
									UpperCAmelCase__					=							self.get_feature_extractor()
									UpperCAmelCase__					=							self.get_tokenizer()

									UpperCAmelCase__					=							SpeechaTextProcessor(tokenizer=__lowerCamelCase   ,feature_extractor=__lowerCamelCase      )

									UpperCAmelCase__					=							floats_list((3, 1_000)      )

									UpperCAmelCase__					=							feature_extractor(__lowerCamelCase   ,return_tensors='np'      )
									UpperCAmelCase__					=							processor(__lowerCamelCase   ,return_tensors='np'      )

									for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1e-2      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.get_feature_extractor()
									UpperCAmelCase__					=							self.get_tokenizer()

									UpperCAmelCase__					=							SpeechaTextProcessor(tokenizer=__lowerCamelCase   ,feature_extractor=__lowerCamelCase      )

									UpperCAmelCase__					=							"This is a test string"

									UpperCAmelCase__					=							processor(text=__lowerCamelCase      )

									UpperCAmelCase__					=							tokenizer(__lowerCamelCase      )

									for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]      )







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							self.get_feature_extractor()
									UpperCAmelCase__					=							self.get_tokenizer()

									UpperCAmelCase__					=							SpeechaTextProcessor(tokenizer=__lowerCamelCase   ,feature_extractor=__lowerCamelCase      )

									UpperCAmelCase__					=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									UpperCAmelCase__					=							processor.batch_decode(__lowerCamelCase      )
									UpperCAmelCase__					=							tokenizer.batch_decode(__lowerCamelCase      )

									self.assertListEqual(__lowerCamelCase   ,__lowerCamelCase      )







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							self.get_feature_extractor()
									UpperCAmelCase__					=							self.get_tokenizer()

									UpperCAmelCase__					=							SpeechaTextProcessor(tokenizer=__lowerCamelCase   ,feature_extractor=__lowerCamelCase      )

									self.assertListEqual(
									    processor.model_input_names   ,feature_extractor.model_input_names   ,msg='`processor` and `feature_extractor` model input names do not match'   ,)




"""simple docstring"""






from __future__ import annotations
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Tuple   ,lowerCamelCase__       :     int      ):
									UpperCAmelCase__					=							vertices
									UpperCAmelCase__					=							[[0] * vertices for _ in range(lowerCamelCase__      )]







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     list[int]      ):
									print('Vertex \t Distance from Source'      )
									for vertex in range(self.vertices      ):
													print(lowerCamelCase__   ,'\t\t'   ,distances_from_source[vertex]      )







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     list[int]   ,lowerCamelCase__       :     list[bool]      ):
									UpperCAmelCase__					=							1e7
									UpperCAmelCase__					=							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:
																	UpperCAmelCase__					=							distances_from_source[vertex]
																	UpperCAmelCase__					=							vertex
									return min_index







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     int      ):
									UpperCAmelCase__					=							[int(1e7      )] * self.vertices  # distances from the source
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							[False] * self.vertices

									for _ in range(self.vertices      ):
													UpperCAmelCase__					=							self.minimum_distance(lowerCamelCase__   ,lowerCamelCase__      )
													UpperCAmelCase__					=							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]
																	):
																					UpperCAmelCase__					=							distances[u] + self.graph[u][v]

									self.print_solution(lowerCamelCase__      )


if __name__ == "__main__":
						lowerCAmelCase__  :  List[str]											=     Graph(9)
						lowerCAmelCase__  :  Tuple											=     [
						    [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)



