


'''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 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[str]         =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						UpperCAmelCase__	:       Dict         =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					Optional[int]  )							->					List[str]:
										if isinstance(model.config	,				_lowerCamelCase  ):
														__magic_name__  ,      __magic_name__	  = list(tokenizer.lang_code_to_id.keys()  )[:2]
														__magic_name__	  = TranslationPipeline(model=_lowerCamelCase	,				tokenizer=_lowerCamelCase	,				src_lang=_lowerCamelCase	,				tgt_lang=_lowerCamelCase  )
										else:
														__magic_name__	  = TranslationPipeline(model=_lowerCamelCase	,				tokenizer=_lowerCamelCase  )
										return translator, ["Some string", "Some other text"]
						def 					__A					( self				:					str	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					int  )							->					int:
										__magic_name__	  = translator("Some string"  )
										self.assertEqual(_lowerCamelCase	,				[{"translation_text": ANY(_lowerCamelCase  )}]  )

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

										__magic_name__	  = translator(["Some string", "other string"]  )
										self.assertEqual(_lowerCamelCase	,				[{"translation_text": ANY(_lowerCamelCase  )}, {"translation_text": ANY(_lowerCamelCase  )}]  )
						@require_torch
						def 					__A					( self				:					Optional[int]  )							->					Dict:
										__magic_name__	  = pipeline("translation_en_to_ro"	,				model="patrickvonplaten/t5-tiny-random"	,				framework="pt"  )
										__magic_name__	  = 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				:					str  )							->					List[str]:
										__magic_name__	  = pipeline("translation_en_to_ro"	,				model="patrickvonplaten/t5-tiny-random"	,				framework="tf"  )
										__magic_name__	  = 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				:					Any  )							->					Dict:
										__magic_name__	  = pipeline("translation_en_to_de"	,				model="patrickvonplaten/t5-tiny-random"	,				framework="pt"  )
										__magic_name__	  = 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				:					List[Any]  )							->					int:
										__magic_name__	  = pipeline("translation_en_to_de"	,				model="patrickvonplaten/t5-tiny-random"	,				framework="tf"  )
										__magic_name__	  = 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 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@require_torch
						@slow
						def 					__A					( self				:					Any  )							->					List[Any]:
										# We don't provide a default for this pair
										with self.assertRaises(_lowerCamelCase  ):
														pipeline(task="translation_cn_to_ar"  )

										# but we do for this one
										__magic_name__	  = 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				:					Dict  )							->					Any:
										__magic_name__	  = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"  )
										__magic_name__	  = MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"  )

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

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

										__magic_name__	  = 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
										__magic_name__	  = pipeline(task="translation"	,				model=_lowerCamelCase	,				tokenizer=_lowerCamelCase	,				src_lang="en_XX"	,				tgt_lang="ar_AR"  )
										__magic_name__	  = translator("This is a test"  )
										self.assertEqual(_lowerCamelCase	,				[{"translation_text": "هذا إختبار"}]  )
						@require_torch
						def 					__A					( self				:					str  )							->					int:
										__magic_name__	  = "patrickvonplaten/t5-tiny-random"
										__magic_name__	  = 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				:					List[Any]  )							->					str:
										__magic_name__	  = "patrickvonplaten/t5-tiny-random"
										with pytest.warns(_lowerCamelCase	,				match=r".*translation_en_to_de.*"  ):
														__magic_name__	  = 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				:					str  )							->					Tuple:
										with self.assertRaises(_lowerCamelCase  ):
														pipeline(task="translation"  )







'''simple docstring'''
from __future__ import annotations




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



