






"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Any             =	"""laion/clap-htsat-unfused"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						Any							)					->       Dict:
   return RobertaTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						List[str]							)					->       int:
   return ClapFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_tokenizer()
   _a  :       Any             =	self.get_feature_extractor()

   _a  :       Optional[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       List[str]             =	ClapProcessor.from_pretrained(self.tmpdirname							)

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

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


 def _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Tuple             =	ClapProcessor(tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)
   _a  :       Union[str, Any]             =	self.get_feature_extractor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	ClapProcessor.from_pretrained(
       self.tmpdirname							,						bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

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

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


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	self.get_feature_extractor()
   _a  :       Tuple             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	floats_list((3, 1000)							)

   _a  :       List[Any]             =	feature_extractor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(audios=UpperCAmelCase__							,						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 _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_feature_extractor()
   _a  :       Any             =	self.get_tokenizer()

   _a  :       Any             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Optional[int]             =	"""This is a test string"""

   _a  :       Tuple             =	processor(text=UpperCAmelCase__							)

   _a  :       int             =	tokenizer(UpperCAmelCase__							)

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


 def _lowercase	(     self		:						List[Any]							)					->       Any:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       List[str]             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   _a  :       Dict             =	processor.batch_decode(UpperCAmelCase__							)
   _a  :       Any             =	tokenizer.batch_decode(UpperCAmelCase__							)

   self.assertListEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       Optional[Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   self.assertListEqual(
       processor.model_input_names[2:]							,						feature_extractor.model_input_names							,						msg="""`processor` and `feature_extractor` model input names do not match"""							,						)









"""simple docstring"""




from __future__ import annotations

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  print(F"""Vertex\tShortest Distance from vertex {src}"""				)
  for i, d in enumerate(UpperCamelCase__				):
    print(F"""{i}\t\t{d}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for j in range(UpperCamelCase__				):
    _a      ,  _a      ,  _a  :       List[str]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])
    if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
      return True
  return False

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	[float("""inf"""				)] * vertex_count
  _a  :       Any             =	0.0

  for _ in range(vertex_count - 1				):
    for j in range(UpperCamelCase__				):
      _a      ,  _a      ,  _a  :       List[Any]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

      if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
        _a  :       Any             =	distance[u] + w

  _a  :       Union[str, Any]             =	check_negative_cycle(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""				)

  return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _snake_case          =   int(input('Enter number of vertices: ').strip())
 _snake_case          =   int(input('Enter number of edges: ').strip())

 _snake_case          =   [{} for _ in range(E)]

 for i in range(E):
  print('Edge ', i + 1)
  _snake_case      , _snake_case      , _snake_case          =   (
      int(x)
      for x in input('Enter source, destination, weight: ').strip().split(' ')
  )
  _snake_case          =   {'src': src, 'dst': dest, 'weight': weight}

 _snake_case          =   int(input('\nEnter shortest path source:').strip())
 _snake_case          =   bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)

