




'''simple docstring'''






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


A_ 			=		get_tests_dir("fixtures/test_sentencepiece.model")






@require_torch
@require_torchaudio
@require_sentencepiece
class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				List[str] ):
           snake_case__					:      Dict  			=						tempfile.mkdtemp()

           snake_case__					:      Union[str, Any]  			=						['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
           snake_case__					:      str  			=						dict(zip(__lowerCamelCase    ,       range(len(__lowerCamelCase ) ) ) )
           snake_case__					:      Union[str, Any]  			=						Path(self.tmpdirname )
           save_json(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['vocab_file'] )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['spm_file'] )

           snake_case__					:      Optional[Any]  			=						SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
           tokenizer.save_pretrained(self.tmpdirname )

           snake_case__					:      str  			=						{
               'feature_size': 24,
               'num_mel_bins': 24,
               'padding_value': 0.0,
               'sampling_rate': 16000,
               'return_attention_mask': False,
               'do_normalize': True,
           }
           save_json(__lowerCamelCase    ,       save_dir / FEATURE_EXTRACTOR_NAME )





     def  _lowerCAmelCase			(    self    :				str    ,       **__lowerCamelCase    :				int ):
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				int    ,       **__lowerCamelCase    :				List[Any] ):
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           shutil.rmtree(self.tmpdirname )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Dict  			=						self.get_tokenizer()
           snake_case__					:      Any  			=						self.get_feature_extractor()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           processor.save_pretrained(self.tmpdirname )
           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(self.tmpdirname )

           self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer.get_vocab() )
           self.assertIsInstance(processor.tokenizer    ,       __lowerCamelCase )

           self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string() )
           self.assertIsInstance(processor.feature_extractor    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      Any  			=						SpeechaTextProcessor(
               tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor() )
           processor.save_pretrained(self.tmpdirname )

           snake_case__					:      Tuple  			=						self.get_tokenizer(bos_token='(BOS)'    ,       eos_token='(EOS)' )
           snake_case__					:      int  			=						self.get_feature_extractor(do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(
               self.tmpdirname    ,       bos_token='(BOS)'    ,       eos_token='(EOS)'    ,       do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab() )
           self.assertIsInstance(processor.tokenizer    ,       __lowerCamelCase )

           self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string() )
           self.assertIsInstance(processor.feature_extractor    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      int  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      List[str]  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      List[str]  			=						floats_list((3, 1000) )

           snake_case__					:      Tuple  			=						feature_extractor(__lowerCamelCase    ,       return_tensors='np' )
           snake_case__					:      str  			=						processor(__lowerCamelCase    ,       return_tensors='np' )

           for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()    ,       input_processor[key].sum()    ,       delta=1E-2 )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						'This is a test string'

           snake_case__					:      int  			=						processor(text=__lowerCamelCase )

           snake_case__					:      Tuple  			=						tokenizer(__lowerCamelCase )

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key] )





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      str  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      str  			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           snake_case__					:      List[str]  			=						processor.batch_decode(__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						tokenizer.batch_decode(__lowerCamelCase )

           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      List[str]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      int  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           self.assertListEqual(
               processor.model_input_names    ,       feature_extractor.model_input_names    ,       msg='`processor` and `feature_extractor` model input names do not match'    ,       )






'''simple docstring'''






import bsa
import requests






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


if __name__ == "__main__":
      import json

      A_ 			=		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)
          )
      )
