


import math
import sys


class 	a__						:

       def __init__( self   :  int     ):
            a_     :  Dict				      =     0
            a_     :  Optional[int]				      =     []
            a_     :  List[Any]				      =     {}  # To store the pos of node in array

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            return self.cur_size == 0

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  str     ):
            a_     :  str				      =     self.left(lowerCamelCase_     )
            a_     :  Optional[Any]				      =     self.right(lowerCamelCase_     )
            if lc < self.cur_size and self.array(lowerCamelCase_     )[0] < self.array(lowerCamelCase_     )[0]:
                 a_     :  str				      =     lc
            else:
                 a_     :  Union[str, Any]				      =     idx
            if rc < self.cur_size and self.array(lowerCamelCase_     )[0] < self.array(lowerCamelCase_     )[0]:
                 a_     :  int				      =     rc
            if smallest != idx:
                 self.swap(lowerCamelCase_   ,				lowerCamelCase_     )
                 self.min_heapify(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Union[str, Any]     ):
            # Inserts a node into the Priority Queue
            a_     :  Optional[int]				      =     self.cur_size
            self.cur_size += 1
            self.array.append((sys.maxsize, tup[1])     )
            self.decrease_key((sys.maxsize, tup[1])   ,				tup[0]     )

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            # Removes and returns the min element at top of priority queue
            a_     :  List[Any]				      =     self.array[0][1]
            a_     :  str				      =     self.array[self.cur_size - 1]
            self.cur_size -= 1
            self.min_heapify(1     )
            del self.pos[min_node]
            return min_node

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  int     ):
            # returns the index of left child
            return 2 * i + 1

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  Any     ):
            # returns the index of right child
            return 2 * i + 2

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  str     ):
            # returns the index of parent
            return math.floor(i / 2     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Optional[int]     ):
            # swaps array elements at indices i and j
            # update the pos{}
            a_     :  Optional[Any]				      =     j
            a_     :  Optional[int]				      =     i
            a_     :  Optional[int]				      =     self.array[i]
            a_     :  Dict				      =     self.array[j]
            a_     :  List[Any]				      =     temp

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Optional[Any]     ):
            a_     :  Tuple				      =     self.pos[tup[1]]
            # assuming the new_d is atmost old_d
            a_     :  List[str]				      =     (new_d, tup[1])
            while idx > 0 and self.array[self.par(lowerCamelCase_     )][0] > self.array[idx][0]:
                 self.swap(lowerCamelCase_   ,				self.par(lowerCamelCase_     )     )
                 a_     :  int				      =     self.par(lowerCamelCase_     )


class 	a__						:

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any]     ):
            a_     :  Dict				      =     {}  # To store graph: u -> (v,w)
            a_     :  List[str]				      =     num  # Number of nodes in graph
            # To store the distance from source vertex
            a_     :  Any				      =     [0] * self.num_nodes
            a_     :  Any				      =     [-1] * self.num_nodes  # To store the path

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  str     ):
            #  Edge going from node u to v and v to u with weight w
            # u (w)-> v, v (w) -> u
            # Check if u already in graph
            if u in self.adjList:
                 self.adjList[u].append((v, w)     )
            else:
                 a_     :  Any				      =     [(v, w)]

            # Assuming undirected graph
            if v in self.adjList:
                 self.adjList[v].append((u, w)     )
            else:
                 a_     :  List[Any]				      =     [(u, w)]

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            # u -> v(w)
            for u in self.adjList:
                 print(lowerCamelCase_   ,				"""->"""   ,				""" -> """.join(str(F'''{v}({w})'''     ) for v, w in self.adjList[u]     )     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  List[Any]     ):
            # Flush old junk values in par[]
            a_     :  int				      =     [-1] * self.num_nodes
            # src is the source node
            a_     :  List[str]				      =     0
            a_     :  int				      =     PriorityQueue()
            q.insert((0, src)     )  # (dist from src, node)
            for u in self.adjList:
                 if u != src:
                      a_     :  List[Any]				      =     sys.maxsize  # Infinity
                      a_     :  Optional[int]				      =     -1

            while not q.is_empty():
                 a_     :  Union[str, Any]				      =     q.extract_min()  # Returns node with the min dist from source
                 # Update the distance of all the neighbours of u and
                 # if their prev dist was INFINITY then push them in Q
                 for v, w in self.adjList[u]:
                      a_     :  Dict				      =     self.dist[u] + w
                      if self.dist[v] > new_dist:
                           if self.dist[v] == sys.maxsize:
                                q.insert((new_dist, v)     )
                           else:
                                q.decrease_key((self.dist[v], v)   ,				lowerCamelCase_     )
                           a_     :  Dict				      =     new_dist
                           a_     :  Dict				      =     u

        # Show the shortest distances from src
            self.show_distances(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  Any     ):
            print(F'''Distance from node: {src}'''     )
            for u in range(self.num_nodes     ):
                 print(F'''Node {u} has distance: {self.dist[u]}'''     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  int     ):
            # To show the shortest path from src to dest
            # WARNING: Use it *after* calling dijkstra
            a_     :  int				      =     []
            a_     :  Optional[int]				      =     0
            a_     :  Any				      =     dest
            # Backtracking from dest to src
            while self.par[temp] != -1:
                 path.append(lowerCamelCase_     )
                 if temp != src:
                      for v, w in self.adjList[temp]:
                           if v == self.par[temp]:
                                cost += w
                                break
                 a_     :  Dict				      =     self.par[temp]
            path.append(lowerCamelCase_     )
            path.reverse()

            print(F'''----Path to reach {dest} from {src}----'''     )
            for u in path:
                 print(F'''{u}'''   ,				end=""" """     )
                 if u != dest:
                      print("""-> """   ,				end=""""""     )

            print("""\nTotal cost of path: """   ,				lowerCamelCase_     )


if __name__ == "__main__":
       __lowerCamelCase   	=  Graph(9)
       graph.add_edge(0, 1, 4)
       graph.add_edge(0, 7, 8)
       graph.add_edge(1, 2, 8)
       graph.add_edge(1, 7, 11)
       graph.add_edge(2, 3, 7)
       graph.add_edge(2, 8, 2)
       graph.add_edge(2, 5, 4)
       graph.add_edge(3, 4, 9)
       graph.add_edge(3, 5, 14)
       graph.add_edge(4, 5, 10)
       graph.add_edge(5, 6, 2)
       graph.add_edge(6, 7, 1)
       graph.add_edge(6, 8, 6)
       graph.add_edge(7, 8, 7)
       graph.show_graph()
       graph.dijkstra(0)
       graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21









import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


a_ 		=  get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    tempfile.mkdtemp()

          __UpperCamelCase			=    SpeechTaTokenizer(snake_case					)
          tokenizer.save_pretrained(self.tmpdirname					)

          __UpperCamelCase			=    {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16000,
              '''do_normalize''': False,
              '''num_mel_bins''': 80,
              '''hop_length''': 16,
              '''win_length''': 64,
              '''win_function''': '''hann_window''',
              '''fmin''': 80,
              '''fmax''': 7600,
              '''mel_floor''': 1E-10,
              '''reduction_factor''': 2,
              '''return_attention_mask''': True,
          }

          __UpperCamelCase			=    os.path.join(self.tmpdirname			,							snake_case					)
          with open(self.feature_extraction_file			,							'''w'''			,							encoding='''utf-8'''					) as fp:
              fp.write(json.dumps(snake_case					) + '''\n'''					)







      def    snake_case						(  self       :			Union[str, Any]			,							**snake_case       :			List[Any]					):
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,							**snake_case					)







      def    snake_case						(  self       :			List[Any]			,							**snake_case       :			List[str]					):
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,							**snake_case					)







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







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_tokenizer()
          __UpperCamelCase			=    self.get_feature_extractor()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          processor.save_pretrained(self.tmpdirname					)
          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(self.tmpdirname					)

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

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







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    SpeechTaProcessor(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=snake_case			,							padding_value=1.0					)

          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(
              self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=snake_case			,							padding_value=1.0					)

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

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







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio=snake_case			,							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    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio_target=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio_target=snake_case			,							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    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

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







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text_target=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

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







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

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

          __UpperCamelCase			=    processor.batch_decode(snake_case					)
          __UpperCamelCase			=    tokenizer.batch_decode(snake_case					)

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

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






