'''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        __magic_name__		(       unittest.TestCase):
			UpperCamelCase__							=			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			UpperCamelCase__							=			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    Optional[Any]							,     lowercase_							:    int							,     lowercase_							:    List[str]       ):
										if isinstance(model.config							,     lowercase_       ):
																	lowercase_		,		lowercase_   :     Dict									=			list(tokenizer.lang_code_to_id.keys()       )[:2]
																	lowercase_   :     int									=			TranslationPipeline(model=lowercase_							,     tokenizer=lowercase_							,     src_lang=lowercase_							,     tgt_lang=lowercase_       )
										else:
																	lowercase_   :     List[Any]									=			TranslationPipeline(model=lowercase_							,     tokenizer=lowercase_       )
										return translator, ["Some string", "Some other text"]







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    Dict							,     lowercase_							:    List[str]       ):
										lowercase_   :     Any									=			translator("""Some string"""       )
										self.assertEqual(lowercase_							,     [{"""translation_text""": ANY(lowercase_       )}]       )

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

										lowercase_   :     Optional[int]									=			translator(["""Some string""", """other string"""]       )
										self.assertEqual(lowercase_							,     [{"""translation_text""": ANY(lowercase_       )}, {"""translation_text""": ANY(lowercase_       )}]       )







			@require_torch
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										lowercase_   :     Dict									=			pipeline("""translation_en_to_ro"""							,     model="""patrickvonplaten/t5-tiny-random"""							,     framework="""pt"""       )
										lowercase_   :     Any									=			translator("""This is a test string"""							,     max_length=20       )
										self.assertEqual(
										    lowercase_							,     [
										        {
										            """translation_text""": (
										                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
										                """ Beide Beide"""
										            )
										        }
										    ]							,     )







			@require_tf
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     Any									=			pipeline("""translation_en_to_ro"""							,     model="""patrickvonplaten/t5-tiny-random"""							,     framework="""tf"""       )
										lowercase_   :     Tuple									=			translator("""This is a test string"""							,     max_length=20       )
										self.assertEqual(
										    lowercase_							,     [
										        {
										            """translation_text""": (
										                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
										                """ Beide Beide"""
										            )
										        }
										    ]							,     )







			@require_torch
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_   :     Optional[int]									=			pipeline("""translation_en_to_de"""							,     model="""patrickvonplaten/t5-tiny-random"""							,     framework="""pt"""       )
										lowercase_   :     Optional[int]									=			translator("""This is a test string"""							,     max_length=20       )
										self.assertEqual(
										    lowercase_							,     [
										        {
										            """translation_text""": (
										                """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
										                """ urine urine urine urine urine urine urine"""
										            )
										        }
										    ]							,     )







			@require_tf
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     List[Any]									=			pipeline("""translation_en_to_de"""							,     model="""patrickvonplaten/t5-tiny-random"""							,     framework="""tf"""       )
										lowercase_   :     Union[str, Any]									=			translator("""This is a test string"""							,     max_length=20       )
										self.assertEqual(
										    lowercase_							,     [
										        {
										            """translation_text""": (
										                """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
										                """ urine urine urine urine urine urine urine"""
										            )
										        }
										    ]							,     )





class        __magic_name__		(       unittest.TestCase):







			@require_torch
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										# We don't provide a default for this pair
										with self.assertRaises(lowercase_       ):
																	pipeline(task="""translation_cn_to_ar"""       )

										# but we do for this one
										lowercase_   :     Dict									=			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       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										lowercase_   :     Dict									=			MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""       )
										lowercase_   :     int									=			MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""       )

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

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

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

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







			@require_torch
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										lowercase_   :     Union[str, Any]									=			"""patrickvonplaten/t5-tiny-random"""
										lowercase_   :     Dict									=			pipeline(task="""translation_cn_to_ar"""							,     model=lowercase_       )
										self.assertEqual(translator._preprocess_params["""src_lang"""]							,     """cn"""       )
										self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,     """ar"""       )







			@require_torch
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     List[str]									=			"""patrickvonplaten/t5-tiny-random"""
										with pytest.warns(lowercase_							,     match=r""".*translation_en_to_de.*"""       ):
																	lowercase_   :     int									=			pipeline(task="""translation"""							,     model=lowercase_       )
										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       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										with self.assertRaises(lowercase_       ):
																	pipeline(task="""translation"""       )






'''simple docstring'''




from __future__ import annotations





class        __magic_name__		:







			def __init__(      self							:    List[str]							,     lowercase_							:    int       ):
										lowercase_   :     List[str]									=			vertices
										lowercase_   :     Optional[Any]									=			[[0] * vertices for _ in range(lowercase_       )]







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    list[int]       ):
										print("""Vertex \t Distance from Source"""       )
										for vertex in range(self.vertices       ):
																	print(lowercase_							,     """\t\t"""							,     distances_from_source[vertex]       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    list[int]							,     lowercase_							:    list[bool]       ):
										lowercase_   :     Dict									=			1E7
										lowercase_   :     int									=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices       ):
																	if distances_from_source[vertex] < minimum and visited[vertex] is False:
																								lowercase_   :     Tuple									=			distances_from_source[vertex]
																								lowercase_   :     str									=			vertex
										return min_index







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    int       ):
										lowercase_   :     Optional[Any]									=			[int(1E7       )] * self.vertices  # distances from the source
										lowercase_   :     List[Any]									=			0
										lowercase_   :     Any									=			[False] * self.vertices

										for _ in range(self.vertices       ):
																	lowercase_   :     Tuple									=			self.minimum_distance(lowercase_							,     lowercase_       )
																	lowercase_   :     Union[str, Any]									=			True

																	# Update dist value of the adjacent vertices
																	# of the picked vertex only if the current
																	# distance is greater than new distance and
																	# the vertex in not in the shortest path tree
																	for v in range(self.vertices       ):
																								if (
																								    self.graph[u][v] > 0
																								    and visited[v] is False
																								    and distances[v] > distances[u] + self.graph[u][v]
																								):
																															lowercase_   :     Any									=			distances[u] + self.graph[u][v]

										self.print_solution(lowercase_       )


if __name__ == "__main__":
						_lowercase		:					Any													=		Graph(9)
						_lowercase		:					Any													=		[
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)





