


'''simple docstring'''





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Optional[Any]					      =			BarthezTokenizer
			A						:				Optional[int]					      =			BarthezTokenizerFast
			A						:				Optional[int]					      =			True
			A						:				Tuple					      =			True
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Union[str, Any]:
						super().setUp()

						__A=	BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'	)
						tokenizer.save_pretrained(self.tmpdirname	)
						tokenizer.save_pretrained(self.tmpdirname			,    legacy_format=lowerCAmelCase_	)
						__A=	tokenizer
			def 					lowerCAmelCase     (  self							:			int	)      ->		Optional[Any]:
						__A=	'<pad>'
						__A=	1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_	)			,    lowerCAmelCase_	)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_	)			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Optional[Any]:
						__A=	list(self.get_tokenizer().get_vocab().keys()	)

						self.assertEqual(vocab_keys[0]			,    '<s>'	)
						self.assertEqual(vocab_keys[1]			,    '<pad>'	)
						self.assertEqual(vocab_keys[-1]			,    '<mask>'	)
						self.assertEqual(len(lowerCAmelCase_	)			,    101_122	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		int:
						self.assertEqual(self.get_tokenizer().vocab_size			,    101_122	)
			@require_torch
			def 					lowerCAmelCase     (  self							:			int	)      ->		List[Any]:
						__A=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
						__A=	[0, 57, 3_018, 70_307, 91, 2]

						__A=	self.tokenizer(
						    lowerCAmelCase_			,    max_length=len(lowerCAmelCase_	)			,    padding=lowerCAmelCase_			,    truncation=lowerCAmelCase_			,    return_tensors='pt'	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

						self.assertEqual((2, 6)			,    batch.input_ids.shape	)
						self.assertEqual((2, 6)			,    batch.attention_mask.shape	)
						__A=	batch.input_ids.tolist()[0]
						self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Any:
						if not self.test_rust_tokenizer:
									return

						__A=	self.get_tokenizer()
						__A=	self.get_rust_tokenizer()

						__A=	'I was born in 92000, and this is falsé.'

						__A=	tokenizer.tokenize(lowerCAmelCase_	)
						__A=	rust_tokenizer.tokenize(lowerCAmelCase_	)
						self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_	)

						__A=	tokenizer.encode(lowerCAmelCase_			,    add_special_tokens=lowerCAmelCase_	)
						__A=	rust_tokenizer.encode(lowerCAmelCase_			,    add_special_tokens=lowerCAmelCase_	)
						self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_	)

						__A=	self.get_rust_tokenizer()
						__A=	tokenizer.encode(lowerCAmelCase_	)
						__A=	rust_tokenizer.encode(lowerCAmelCase_	)
						self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_	)

			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						# fmt: off
						__A=	{'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# moussaKam/mbarthez is a french model. So we also use french texts.
						__A=	[
						    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
						    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
						    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
						    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
						    'telles que la traduction et la synthèse de texte.',
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=lowerCAmelCase_			,    model_name='moussaKam/mbarthez'			,    revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'			,    sequences=lowerCAmelCase_			,    )







'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text,'html.parser' )
			__A=	soup.find_all('td',attrs='titleColumn' )
			__A=	soup.find_all('td',class_='ratingColumn imdbRating' )
			return {
			    title.a.text: float(rating.strong.text )
			    for title, rating in zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




			__A=	get_imdb_top_aaa_movies()
			with open(_SCREAMING_SNAKE_CASE,'w',newline='' ) as out_file:
						__A=	csv.writer(_SCREAMING_SNAKE_CASE )
						writer.writerow(['Movie title', 'IMDb rating'] )
						for title, rating in movies.items():
									writer.writerow([title, rating] )


if __name__ == "__main__":
					write_movies()



