


'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''							)

							UpperCamelCase__     :Optional[Any]							      =				tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]					,     dtype=tf.intaa					,     )  # J'aime le camembert !"

							UpperCamelCase__     :Union[str, Any]							      =				model(UpperCamelCase_							)['''last_hidden_state''']
							UpperCamelCase__     :Any							      =				tf.TensorShape((1, 10, 768)							)
							self.assertEqual(output.shape					,     UpperCamelCase_							)
							# compare the actual values for a slice.
							UpperCamelCase__     :Any							      =				tf.convert_to_tensor(
							    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]					,     dtype=tf.floataa					,     )
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()					,     expected_slice.numpy()					,     atol=1e-4							)							)



'''simple docstring'''
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()