



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			):







	'''simple docstring'''




	A_       :     Any									=		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	A_       :     List[Any]									=		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


				if isinstance(model.config   ,       lowerCamelCase       ):
							a__				,       a__					=  list(tokenizer.lang_code_to_id.keys()       )[:2]
							a__					=  TranslationPipeline(model=lowerCamelCase   ,       tokenizer=lowerCamelCase   ,       src_lang=lowerCamelCase   ,       tgt_lang=lowerCamelCase       )
				else:
							a__					=  TranslationPipeline(model=lowerCamelCase   ,       tokenizer=lowerCamelCase       )
				return translator, ["Some string", "Some other text"]

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  translator("""Some string"""       )
				self.assertEqual(lowerCamelCase   ,       [{"""translation_text""": ANY(lowerCamelCase       )}]       )

				a__					=  translator(["""Some string"""]       )
				self.assertEqual(lowerCamelCase   ,       [{"""translation_text""": ANY(lowerCamelCase       )}]       )

				a__					=  translator(["""Some string""", """other string"""]       )
				self.assertEqual(lowerCamelCase   ,       [{"""translation_text""": ANY(lowerCamelCase       )}, {"""translation_text""": ANY(lowerCamelCase       )}]       )

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  pipeline("""translation_en_to_ro"""   ,       model="""patrickvonplaten/t5-tiny-random"""   ,       framework="""pt"""       )
				a__					=  translator("""This is a test string"""   ,       max_length=20       )
				self.assertEqual(
				    lowerCamelCase   ,       [
				        {
				            """translation_text""": (
				                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
				                """ Beide Beide"""
				            )
				        }
				    ]   ,       )

	@require_tf
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  pipeline("""translation_en_to_ro"""   ,       model="""patrickvonplaten/t5-tiny-random"""   ,       framework="""tf"""       )
				a__					=  translator("""This is a test string"""   ,       max_length=20       )
				self.assertEqual(
				    lowerCamelCase   ,       [
				        {
				            """translation_text""": (
				                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
				                """ Beide Beide"""
				            )
				        }
				    ]   ,       )

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  pipeline("""translation_en_to_de"""   ,       model="""patrickvonplaten/t5-tiny-random"""   ,       framework="""pt"""       )
				a__					=  translator("""This is a test string"""   ,       max_length=20       )
				self.assertEqual(
				    lowerCamelCase   ,       [
				        {
				            """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       ):



				'''simple docstring'''


				a__					=  pipeline("""translation_en_to_de"""   ,       model="""patrickvonplaten/t5-tiny-random"""   ,       framework="""tf"""       )
				a__					=  translator("""This is a test string"""   ,       max_length=20       )
				self.assertEqual(
				    lowerCamelCase   ,       [
				        {
				            """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			):







	'''simple docstring'''




	@require_torch
	@slow
	def 				_A			(	self       ):



				'''simple docstring'''


				with self.assertRaises(lowerCamelCase       ):
							pipeline(task="""translation_cn_to_ar"""       )

				# but we do for this one
				a__					=  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       ):



				'''simple docstring'''


				a__					=  MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""       )
				a__					=  MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""       )

				a__					=  pipeline(task="""translation"""   ,       model=lowerCamelCase   ,       tokenizer=lowerCamelCase       )
				# Missing src_lang, tgt_lang
				with self.assertRaises(lowerCamelCase       ):
							translator("""This is a test"""       )

				a__					=  translator("""This is a test"""   ,       src_lang="""en_XX"""   ,       tgt_lang="""ar_AR"""       )
				self.assertEqual(lowerCamelCase   ,       [{"""translation_text""": """هذا إختبار"""}]       )

				a__					=  translator("""This is a test"""   ,       src_lang="""en_XX"""   ,       tgt_lang="""hi_IN"""       )
				self.assertEqual(lowerCamelCase   ,       [{"""translation_text""": """यह एक परीक्षण है"""}]       )

				# src_lang, tgt_lang can be defined at pipeline call time
				a__					=  pipeline(task="""translation"""   ,       model=lowerCamelCase   ,       tokenizer=lowerCamelCase   ,       src_lang="""en_XX"""   ,       tgt_lang="""ar_AR"""       )
				a__					=  translator("""This is a test"""       )
				self.assertEqual(lowerCamelCase   ,       [{"""translation_text""": """هذا إختبار"""}]       )

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """patrickvonplaten/t5-tiny-random"""
				a__					=  pipeline(task="""translation_cn_to_ar"""   ,       model=lowerCamelCase       )
				self.assertEqual(translator._preprocess_params["""src_lang"""]   ,       """cn"""       )
				self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,       """ar"""       )

	@require_torch
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """patrickvonplaten/t5-tiny-random"""
				with pytest.warns(lowerCamelCase   ,       match=r""".*translation_en_to_de.*"""       ):
							a__					=  pipeline(task="""translation"""   ,       model=lowerCamelCase       )
				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       ):



				'''simple docstring'''


				with self.assertRaises(lowerCamelCase       ):
							pipeline(task="""translation"""       )






def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			print("""\nVertex Distance"""	)
			for i in range(lowercase__	):
						if dist[i] != float("""inf"""	):
									print(lowercase__				,					"""\t"""				,					int(dist[i]	)				,					end="""\t"""	)
						else:
									print(lowercase__				,					"""\t"""				,					"""INF"""				,					end="""\t"""	)
						print()




def    UpperCAmelCase					(  lowercase__						:					Optional[int]				,					lowercase__						:					Any				,					lowercase__						:					Tuple	):



			'''simple docstring'''





			a__					=  float("""inf"""	)
			a__					=  -1
			for i in range(lowercase__	):
						if (not vset[i]) and mdist[i] < min_val:
									a__					=  i
									a__					=  mdist[i]
			return min_ind




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					Tuple				,					lowercase__						:					List[Any]	):



			'''simple docstring'''





			a__					=  [float("""inf"""	) for _ in range(lowercase__	)]
			a__					=  [False for _ in range(lowercase__	)]
			a__					=  0.0

			for _ in range(v - 1	):
						a__					=  min_dist(lowercase__				,					lowercase__				,					lowercase__	)
						a__					=  True

						for i in range(lowercase__	):
									if (
									    (not vset[i])
									    and graph[u][i] != float("""inf"""	)
									    and mdist[u] + graph[u][i] < mdist[i]
									):
												a__					=  mdist[u] + graph[u][i]

			print_dist(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	_lowercase				:					Any     					=int(input("""Enter number of vertices: """).strip())
	_lowercase				:					List[str]     					=int(input("""Enter number of edges: """).strip())

	_lowercase				:					List[str]     					=[[float("""inf""") for i in range(V)] for j in range(V)]

	for i in range(V):
		_lowercase				:					Dict     					=0.0

	for i in range(E):
		print("""\nEdge """, i + 1)
		_lowercase				:					Dict     					=int(input("""Enter source:""").strip())
		_lowercase				:					Optional[Any]     					=int(input("""Enter destination:""").strip())
		_lowercase				:					List[Any]     					=float(input("""Enter weight:""").strip())
		_lowercase				:					List[str]     					=weight

	_lowercase				:					List[Any]     					=int(input("""\nEnter shortest path source:""").strip())
	dijkstra(graph, V, gsrc)

