"""simple docstring"""


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 					UpperCamelCase   (							lowerCAmelCase__       ,						unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_						      = BartTokenizer
       SCREAMING_SNAKE_CASE_						      = BartTokenizerFast
       SCREAMING_SNAKE_CASE_						      = True
       SCREAMING_SNAKE_CASE_						      = filter_roberta_detectors







       def 						a_		(					self) -> Any:
            super().setUp()
            snake_case_   			=   [
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
            ]
            snake_case_   			=   dict(zip(lowerCAmelCase__,				range(len(lowerCAmelCase__))))
            snake_case_   			=   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            snake_case_   			=   {'unk_token': '<unk>'}

            snake_case_   			=   os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES['vocab_file'])
            snake_case_   			=   os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES['merges_file'])
            with open(self.vocab_file,				'w',				encoding='utf-8') as fp:
                 fp.write(json.dumps(lowerCAmelCase__) + '\n')
            with open(self.merges_file,				'w',				encoding='utf-8') as fp:
                 fp.write('\n'.join(lowerCAmelCase__))







       def 						a_		(					self,				**lowerCAmelCase__) -> str:
            kwargs.update(self.special_tokens_map)
            return self.tokenizer_class.from_pretrained(self.tmpdirname,				**lowerCAmelCase__)







       def 						a_		(					self,				**lowerCAmelCase__) -> Dict:
            kwargs.update(self.special_tokens_map)
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,				**lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__) -> Optional[Any]:
            return "lower newer", "lower newer"







       @cached_property
       def 						a_		(					self) -> str:
            return BartTokenizer.from_pretrained('facebook/bart-large')







       @cached_property
       def 						a_		(					self) -> List[Any]:
            return BartTokenizerFast.from_pretrained('facebook/bart-large')







       @require_torch
       def 						a_		(					self) -> Union[str, Any]:
            snake_case_   			=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            snake_case_   			=   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 snake_case_   			=   tokenizer(lowerCAmelCase__,				max_length=len(lowerCAmelCase__),				padding=lowerCAmelCase__,				return_tensors='pt')
                 self.assertIsInstance(lowerCAmelCase__,				lowerCAmelCase__)

                 self.assertEqual((2, 9),				batch.input_ids.shape)
                 self.assertEqual((2, 9),				batch.attention_mask.shape)
                 snake_case_   			=   batch.input_ids.tolist()[0]
                 self.assertListEqual(lowerCAmelCase__,				lowerCAmelCase__)
                 # Test that special tokens are reset







       @require_torch
       def 						a_		(					self) -> Any:
            snake_case_   			=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 snake_case_   			=   tokenizer(lowerCAmelCase__,				padding=lowerCAmelCase__,				return_tensors='pt')
                 # check if input_ids are returned and no labels
                 self.assertIn('input_ids',				lowerCAmelCase__)
                 self.assertIn('attention_mask',				lowerCAmelCase__)
                 self.assertNotIn('labels',				lowerCAmelCase__)
                 self.assertNotIn('decoder_attention_mask',				lowerCAmelCase__)







       @require_torch
       def 						a_		(					self) -> int:
            snake_case_   			=   [
                'Summary of the text.',
                'Another summary.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 snake_case_   			=   tokenizer(text_target=lowerCAmelCase__,				max_length=32,				padding='max_length',				return_tensors='pt')
                 self.assertEqual(32,				targets['input_ids'].shape[1])







       @require_torch
       def 						a_		(					self) -> Optional[Any]:
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 snake_case_   			=   tokenizer(
                     ['I am a small frog' * 1024, 'I am a small frog'],				padding=lowerCAmelCase__,				truncation=lowerCAmelCase__,				return_tensors='pt')
                 self.assertIsInstance(lowerCAmelCase__,				lowerCAmelCase__)
                 self.assertEqual(batch.input_ids.shape,				(2, 1024))







       @require_torch
       def 						a_		(					self) -> str:
            snake_case_   			=   ['A long paragraph for summarization.']
            snake_case_   			=   [
                'Summary of the text.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 snake_case_   			=   tokenizer(lowerCAmelCase__,				return_tensors='pt')
                 snake_case_   			=   tokenizer(text_target=lowerCAmelCase__,				return_tensors='pt')
                 snake_case_   			=   inputs['input_ids']
                 snake_case_   			=   targets['input_ids']
                 self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
                 self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
                 self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
                 self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







       def 						a_		(					self) -> Optional[Any]:
            pass







       def 						a_		(					self) -> Dict:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
                      snake_case_   			=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__,				**lowerCAmelCase__)
                      snake_case_   			=   self.tokenizer_class.from_pretrained(lowerCAmelCase__,				**lowerCAmelCase__)
                      snake_case_   			=   'A, <mask> AllenNLP sentence.'
                      snake_case_   			=   tokenizer_r.encode_plus(lowerCAmelCase__,				add_special_tokens=lowerCAmelCase__,				return_token_type_ids=lowerCAmelCase__)
                      snake_case_   			=   tokenizer_p.encode_plus(lowerCAmelCase__,				add_special_tokens=lowerCAmelCase__,				return_token_type_ids=lowerCAmelCase__)

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r['token_type_ids']),				sum(tokens_p['token_type_ids']))

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask']),				sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask']),				)

                      snake_case_   			=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
                      snake_case_   			=   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p['input_ids'],				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
                      self.assertSequenceEqual(tokens_r['input_ids'],				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

                      self.assertSequenceEqual(
                          lowerCAmelCase__,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
                      self.assertSequenceEqual(
                          lowerCAmelCase__,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])


"""simple docstring"""


import bsa
import requests







def    UpperCAmelCase       (						UpperCAmelCase   )							-> 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 = 5   )							-> tuple:
     snake_case_   			=   int(float(UpperCAmelCase   )   )
     if num_movies < 1:
          return ()
     snake_case_   			=   (
         'https://www.imdb.com/search/title?title_type='
         f'feature&sort=num_votes,desc&count={num_movies}'
     )
     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

    __UpperCamelCase										=     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)
        )
    )

