




'''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       _a  (     unittest.TestCase			):
       __a    :					List[Any]           = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       __a    :					Any           = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     Any       ,    lowercase						:     int       ,    lowercase						:     Optional[Any]    ):



           '''simple docstring'''

           if isinstance(model.config       ,    _A    ):
               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    A				(     self						:     str       ,    lowercase						:     Optional[int]       ,    lowercase						:     Dict    ):



           '''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    A				(     self						:     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    A				(     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    A				(     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    A				(     self						:     Tuple    ):



           '''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       _a  (     unittest.TestCase			):
       @require_torch
       @slow
       def    A				(     self						:     Union[str, Any]    ):



           '''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    A				(     self						:     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    A				(     self						:     List[Any]    ):



           '''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    A				(     self						:     Tuple    ):



           '''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    A				(     self						:     int    ):



           '''simple docstring'''

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










'''simple docstring'''







def 						snake_case_							(_a     :							int = 9_9_8_0_0_1					):
    UpperCAmelCase      	=		0
    for i in range(9_9_9   ,       9_9   ,       -1					):  # 3 digit numbers range from 999 down to 100
        for j in range(9_9_9   ,       9_9   ,       -1					):
            UpperCAmelCase      	=		str(i * j					)
            if product_string == product_string[::-1] and i * j < n:
                UpperCAmelCase      	=		max(_a   ,       i * j					)
    return answer


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




