





'''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 				lowerCAmelCase_						(						unittest.TestCase						):
 __lowerCamelCase  :							Any    					=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __lowerCamelCase  :							Union[str, Any]    					=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Union[str, Any]:
     if isinstance(model.config     ,       _lowerCAmelCase						):
         _lowerCAmelCase     ,     _lowerCAmelCase		     = list(tokenizer.lang_code_to_id.keys()						)[:2]
         _lowerCAmelCase		     = TranslationPipeline(model=_lowerCAmelCase     ,       tokenizer=_lowerCAmelCase     ,       src_lang=_lowerCAmelCase     ,       tgt_lang=_lowerCAmelCase						)
     else:
         _lowerCAmelCase		     = TranslationPipeline(model=_lowerCAmelCase     ,       tokenizer=_lowerCAmelCase						)
     return translator, ["Some string", "Some other text"]




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							int:
     _lowerCAmelCase		     = translator("Some string"						)
     self.assertEqual(_lowerCAmelCase     ,       [{"translation_text": ANY(_lowerCAmelCase						)}]						)

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

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




 @require_torch
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     _lowerCAmelCase		     = pipeline("translation_en_to_ro"     ,       model="patrickvonplaten/t5-tiny-random"     ,       framework="pt"						)
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = pipeline("translation_en_to_ro"     ,       model="patrickvonplaten/t5-tiny-random"     ,       framework="tf"						)
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = pipeline("translation_en_to_de"     ,       model="patrickvonplaten/t5-tiny-random"     ,       framework="pt"						)
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = pipeline("translation_en_to_de"     ,       model="patrickvonplaten/t5-tiny-random"     ,       framework="tf"						)
     _lowerCAmelCase		     = 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 				lowerCAmelCase_						(						unittest.TestCase						):




 @require_torch
 @slow
 def 							_snake_case				(		self						)      ->							List[str]:
     # 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
     _lowerCAmelCase		     = 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						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"						)
     _lowerCAmelCase		     = MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"						)

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

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

     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = pipeline(task="translation"     ,       model=_lowerCAmelCase     ,       tokenizer=_lowerCAmelCase     ,       src_lang="en_XX"     ,       tgt_lang="ar_AR"						)
     _lowerCAmelCase		     = translator("This is a test"						)
     self.assertEqual(_lowerCAmelCase     ,       [{"translation_text": "هذا إختبار"}]						)




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









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   int = 1000      ):



    '''simple docstring'''





    return sum(i for i in range(SCREAMING_SNAKE_CASE_      ) if i % 3 == 0 or i % 5 == 0      )


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


