
"""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"   )






"""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()




