code: 
  """simple docstring"""
  import unittest
  
  import pytest
  
  from transformers import (
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      MBartaaTokenizerFast,
      MBartConfig,
      MBartForConditionalGeneration,
      TranslationPipeline,
      pipeline,
  )
  from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow
  
  from .test_pipelines_common import ANY
  
  
  @is_pipeline_test
  class    _lowerCAmelCase						(						unittest.TestCase						):
  							lowercase_     :     int       =							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  							lowercase_     :     Dict       =							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							Any:
  								if isinstance(model.config ,						a_   ):
  									_UpperCAmelCase ,      _UpperCAmelCase =						list(tokenizer.lang_code_to_id.keys()   )[:2]
  									_UpperCAmelCase =						TranslationPipeline(model=a_ ,						tokenizer=a_ ,						src_lang=a_ ,						tgt_lang=a_   )
  								else:
  									_UpperCAmelCase =						TranslationPipeline(model=a_ ,						tokenizer=a_   )
  								return translator, ["Some string", "Some other text"]
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Optional[int]:
  								_UpperCAmelCase =						translator("Some string"   )
  								self.assertEqual(a_ ,						[{"translation_text": ANY(a_   )}]   )
  
  								_UpperCAmelCase =						translator(["Some string"]   )
  								self.assertEqual(a_ ,						[{"translation_text": ANY(a_   )}]   )
  
  								_UpperCAmelCase =						translator(["Some string", "other string"]   )
  								self.assertEqual(a_ ,						[{"translation_text": ANY(a_   )}, {"translation_text": ANY(a_   )}]   )
  
  
  
  							@require_torch
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase =						pipeline("translation_en_to_ro" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="pt"   )
  								_UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
  								self.assertEqual(
  								    a_ ,						[
  								        {
  								            "translation_text": (
  								                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
  								                " Beide Beide"
  								            )
  								        }
  								    ] ,						)
  
  
  
  							@require_tf
  							def       _a   (      self   )							->							str:
  								_UpperCAmelCase =						pipeline("translation_en_to_ro" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="tf"   )
  								_UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
  								self.assertEqual(
  								    a_ ,						[
  								        {
  								            "translation_text": (
  								                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
  								                " Beide Beide"
  								            )
  								        }
  								    ] ,						)
  
  
  
  							@require_torch
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						pipeline("translation_en_to_de" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="pt"   )
  								_UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
  								self.assertEqual(
  								    a_ ,						[
  								        {
  								            "translation_text": (
  								                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
  								                " urine urine urine urine urine urine urine"
  								            )
  								        }
  								    ] ,						)
  
  
  
  							@require_tf
  							def       _a   (      self   )							->							Union[str, Any]:
  								_UpperCAmelCase =						pipeline("translation_en_to_de" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="tf"   )
  								_UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
  								self.assertEqual(
  								    a_ ,						[
  								        {
  								            "translation_text": (
  								                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
  								                " urine urine urine urine urine urine urine"
  								            )
  								        }
  								    ] ,						)
  
  
  
  
  
  class    _lowerCAmelCase						(						unittest.TestCase						):
  
  
  
  							@require_torch
  							@slow
  							def       _a   (      self   )							->							List[Any]:
  								# We don't provide a default for this pair
  								with self.assertRaises(a_   ):
  									pipeline(task="translation_cn_to_ar"   )
  
  								# but we do for this one
  								_UpperCAmelCase =						pipeline(task="translation_en_to_de"   )
  								self.assertEqual(translator._preprocess_params["src_lang"] ,						"en"   )
  								self.assertEqual(translator._preprocess_params["tgt_lang"] ,						"de"   )
  
  
  
  							@require_torch
  							@slow
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"   )
  								_UpperCAmelCase =						MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"   )
  
  								_UpperCAmelCase =						pipeline(task="translation" ,						model=a_ ,						tokenizer=a_   )
  								# Missing src_lang, tgt_lang
  								with self.assertRaises(a_   ):
  									translator("This is a test"   )
  
  								_UpperCAmelCase =						translator("This is a test" ,						src_lang="en_XX" ,						tgt_lang="ar_AR"   )
  								self.assertEqual(a_ ,						[{"translation_text": "هذا إختبار"}]   )
  
  								_UpperCAmelCase =						translator("This is a test" ,						src_lang="en_XX" ,						tgt_lang="hi_IN"   )
  								self.assertEqual(a_ ,						[{"translation_text": "यह एक परीक्षण है"}]   )
  
  								# src_lang, tgt_lang can be defined at pipeline call time
  								_UpperCAmelCase =						pipeline(task="translation" ,						model=a_ ,						tokenizer=a_ ,						src_lang="en_XX" ,						tgt_lang="ar_AR"   )
  								_UpperCAmelCase =						translator("This is a test"   )
  								self.assertEqual(a_ ,						[{"translation_text": "هذا إختبار"}]   )
  
  
  
  							@require_torch
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						"patrickvonplaten/t5-tiny-random"
  								_UpperCAmelCase =						pipeline(task="translation_cn_to_ar" ,						model=a_   )
  								self.assertEqual(translator._preprocess_params["src_lang"] ,						"cn"   )
  								self.assertEqual(translator._preprocess_params["tgt_lang"] ,						"ar"   )
  
  
  
  							@require_torch
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase =						"patrickvonplaten/t5-tiny-random"
  								with pytest.warns(a_ ,						match=r".*translation_en_to_de.*"   ):
  									_UpperCAmelCase =						pipeline(task="translation" ,						model=a_   )
  								self.assertEqual(translator.task ,						"translation_en_to_de"   )
  								self.assertEqual(translator._preprocess_params["src_lang"] ,						"en"   )
  								self.assertEqual(translator._preprocess_params["tgt_lang"] ,						"de"   )
  
  
  
  							@require_torch
  							def       _a   (      self   )							->							int:
  								with self.assertRaises(a_   ):
  									pipeline(task="translation"   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  import heapq
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						[(0, start)]  # cost from start node,end node
  	_UpperCAmelCase =						set()
  	while heap:
  		((_UpperCAmelCase) ,      (_UpperCAmelCase)) =						heapq.heappop(UpperCamelCase__      )
  		if u in visited:
  			continue
  		visited.add(UpperCamelCase__      )
  		if u == end:
  			return cost
  		for v, c in graph[u]:
  			if v in visited:
  				continue
  			_UpperCAmelCase =						cost + c
  			heapq.heappush(UpperCamelCase__				,     (next_item, v)      )
  	return -1
  
  
  __magic_name__		       =				{
      '''A''': [['''B''', 2], ['''C''', 5]],
      '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
      '''C''': [['''A''', 5], ['''F''', 3]],
      '''D''': [['''B''', 3]],
      '''E''': [['''B''', 4], ['''F''', 3]],
      '''F''': [['''C''', 3], ['''E''', 3]],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''F''', 3]],
      '''F''': [],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''G''', 2]],
      '''F''': [],
      '''G''': [['''F''', 1]],
  }
  
  __magic_name__		       =				dijkstra(G, '''E''', '''C''')
  print(short_distance)  # E -- 3 --> F -- 3 --> C == 6
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 3 --> F == 3
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 2 --> G -- 1 --> F == 3
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  