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 					__snake_case    (     unittest.TestCase      ):
					__lowerCamelCase			   =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					__lowerCamelCase			   =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    str:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= translator('Some string'						)
									self.assertEqual(_A       ,			[{'translation_text': ANY(_A						)}]						)

									snake_case__   :      int   				= translator(['Some string']						)
									self.assertEqual(_A       ,			[{'translation_text': ANY(_A						)}]						)

									snake_case__   :      Union[str, Any]   				= translator(['Some string', 'other string']						)
									self.assertEqual(_A       ,			[{'translation_text': ANY(_A						)}, {'translation_text': ANY(_A						)}]						)




					@require_torch
					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      str   				= pipeline('translation_en_to_ro'       ,			model='patrickvonplaten/t5-tiny-random'       ,			framework='pt'						)
									snake_case__   :      Optional[Any]   				= 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						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      str   				= pipeline('translation_en_to_ro'       ,			model='patrickvonplaten/t5-tiny-random'       ,			framework='tf'						)
									snake_case__   :      str   				= 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						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Dict   				= pipeline('translation_en_to_de'       ,			model='patrickvonplaten/t5-tiny-random'       ,			framework='pt'						)
									snake_case__   :      Optional[Any]   				= 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						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Dict   				= pipeline('translation_en_to_de'       ,			model='patrickvonplaten/t5-tiny-random'       ,			framework='tf'						)
									snake_case__   :      List[Any]   				= 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 					__snake_case    (     unittest.TestCase      ):




					@require_torch
					@slow
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									with self.assertRaises(_A						):
													pipeline(task='translation_cn_to_ar'						)

									# but we do for this one
									snake_case__   :      Optional[Any]   				= 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						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'						)
									snake_case__   :      int   				= MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'						)

									snake_case__   :      List[Any]   				= pipeline(task='translation'       ,			model=_A       ,			tokenizer=_A						)
									# Missing src_lang, tgt_lang
									with self.assertRaises(_A						):
													translator('This is a test'						)

									snake_case__   :      Tuple   				= translator('This is a test'       ,			src_lang='en_XX'       ,			tgt_lang='ar_AR'						)
									self.assertEqual(_A       ,			[{'translation_text': 'هذا إختبار'}]						)

									snake_case__   :      List[str]   				= 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
									snake_case__   :      Optional[Any]   				= pipeline(task='translation'       ,			model=_A       ,			tokenizer=_A       ,			src_lang='en_XX'       ,			tgt_lang='ar_AR'						)
									snake_case__   :      Tuple   				= translator('This is a test'						)
									self.assertEqual(_A       ,			[{'translation_text': 'هذا إختبار'}]						)




					@require_torch
					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= "patrickvonplaten/t5-tiny-random"
									snake_case__   :      List[Any]   				= 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						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      int   				= "patrickvonplaten/t5-tiny-random"
									with pytest.warns(_A       ,			match=r'.*translation_en_to_de.*'						):
													snake_case__   :      Optional[Any]   				= 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						)	->    List[str]:



									'''simple docstring'''







									with self.assertRaises(_A						):
													pipeline(task='translation'						)







def 						snake_case__							(       __lowercase = 1_0_0_0							)		->		int:







						"""simple docstring"""

						A__						:				int         =			0
						A__						:				Optional[int]         =			0
						while 1:
												num += 3
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
						return total


if __name__ == "__main__":
		print(f"""{solution() = }""")