




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      ):


 '''simple docstring'''







 lowercase_       					=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 lowercase_       					=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def    lowerCAmelCase_						(							self						:	Any       ,       _lowerCAmelCase						:	List[str]       ,       _lowerCAmelCase						:	List[Any]       ,       _lowerCAmelCase						:	Optional[int]		):
       if isinstance(model.config       ,       _lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    list(tokenizer.lang_code_to_id.keys()		)[:2]
             SCREAMING_SNAKE_CASE_					=    TranslationPipeline(model=_lowerCAmelCase       ,       tokenizer=_lowerCAmelCase       ,       src_lang=_lowerCAmelCase       ,       tgt_lang=_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_					=    TranslationPipeline(model=_lowerCAmelCase       ,       tokenizer=_lowerCAmelCase		)
       return translator, ["Some string", "Some other text"]



 def    lowerCAmelCase_						(							self						:	Optional[Any]       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    translator('Some string'		)
       self.assertEqual(_lowerCAmelCase       ,       [{'translation_text': ANY(_lowerCAmelCase		)}]		)

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

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



 @require_torch
 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    pipeline('translation_en_to_ro'       ,       model='patrickvonplaten/t5-tiny-random'       ,       framework='pt'		)
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    pipeline('translation_en_to_ro'       ,       model='patrickvonplaten/t5-tiny-random'       ,       framework='tf'		)
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	int		):
       SCREAMING_SNAKE_CASE_					=    pipeline('translation_en_to_de'       ,       model='patrickvonplaten/t5-tiny-random'       ,       framework='pt'		)
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	int		):
       SCREAMING_SNAKE_CASE_					=    pipeline('translation_en_to_de'       ,       model='patrickvonplaten/t5-tiny-random'       ,       framework='tf'		)
       SCREAMING_SNAKE_CASE_					=    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      ):


 '''simple docstring'''







 @require_torch
 @slow
 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       # 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
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	List[str]		):
       SCREAMING_SNAKE_CASE_					=    MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'		)
       SCREAMING_SNAKE_CASE_					=    MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'		)

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

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

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



 @require_torch
 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    'patrickvonplaten/t5-tiny-random'
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    'patrickvonplaten/t5-tiny-random'
       with pytest.warns(_lowerCAmelCase       ,       match=R'.*translation_en_to_de.*'		):
             SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	List[Any]		):
       with self.assertRaises(_lowerCAmelCase		):
             pipeline(task='translation'		)





import bsa
import requests

def       UpperCAmelCase_							(   __UpperCAmelCase		:			bsa.element.ResultSet							)      ->				dict[str, str]:
      return {
          "name": soup.ha.a.text,
          "genre": soup.find('span'    ,    class_='genre'							).text.strip(),
          "rating": soup.strong.text,
          "page_link": f"https://www.imdb.com{soup.a.get('href'							)}",
      }

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int = 5							)      ->				tuple:
      SCREAMING_SNAKE_CASE_					=    int(float(__UpperCAmelCase							)							)
      if num_movies < 1:
            return ()
      SCREAMING_SNAKE_CASE_					=    (
          'https://www.imdb.com/search/title?title_type='
          f"feature&sort=num_votes,desc&count={num_movies}"
      )
      SCREAMING_SNAKE_CASE_					=    bsa.BeautifulSoup(requests.get(__UpperCAmelCase							).content    ,    'html.parser'							)
      return tuple(
          get_movie_data_from_soup(__UpperCAmelCase							)
          for movie in source.find_all('div'    ,    class_='lister-item mode-advanced'							)							)


if __name__ == "__main__":
   import json

   lowerCamelCase__				:     int			       =							int(input('How many movies would you like to see? '))
   print(
       ', '.join(
           json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
       )
   )