


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    							=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       _lowercase    							=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







       def 						snake_case_					(	self:				str,A_:				List[str],A_:				Optional[int],A_:				List[str]			):






            '''simple docstring'''




            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 						snake_case_					(	self:				int,A_:				Any,A_:				Any			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            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 						snake_case_					(	self:				List[Any]			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				str			):






            '''simple docstring'''




            __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 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




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






import math






def 						_A						(						_lowercase				)			->				list:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive integers have prime factors'				)
     __UpperCamelCase				=	[]
     while n % 2 == 0:
          pf.append(2				)
          __UpperCamelCase				=	int(n / 2				)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          while n % i == 0:
               pf.append(_lowercase				)
               __UpperCamelCase				=	int(n / i				)
     if n > 2:
          pf.append(_lowercase				)
     return pf






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     div *= temp
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          div *= temp
     if n > 1:
          div *= 2
     return div






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     if temp > 1:
          s *= (2**temp - 1) / (2 - 1)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          if temp > 1:
               s *= (i**temp - 1) / (i - 1)
     return int(_lowercase				)






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	n
     for x in set(prime_factors(_lowercase				)				):
          s *= (x - 1) / x
     return int(_lowercase				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


