






import math

import flax.linen as nn
import jax.numpy as jnp




def      __A					(     _lowercase					,			_lowercase					,			_lowercase = 1					,			_lowercase = 1					,			_lowercase = 1.0e4					,			_lowercase = False					,			_lowercase = 1.0					,			):

					'''simple docstring'''







					assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
					assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
					_A								= float(embedding_dim // 2 )
					_A								= math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
					_A								= min_timescale * jnp.exp(jnp.arange(_lowercase					,			dtype=jnp.floataa ) * -log_timescale_increment )
					_A								= jnp.expand_dims(_lowercase					,			1 ) * jnp.expand_dims(_lowercase					,			0 )

					# scale embeddings
					_A								= scale * emb

					if flip_sin_to_cos:
										_A								= jnp.concatenate([jnp.cos(_lowercase ), jnp.sin(_lowercase )]					,			axis=1 )
					else:
										_A								= jnp.concatenate([jnp.sin(_lowercase ), jnp.cos(_lowercase )]					,			axis=1 )
					_A								= jnp.reshape(_lowercase					,			[jnp.shape(_lowercase )[0], embedding_dim] )
					return signal





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					32
							A_						  =					jnp.floataa

							@nn.compact
							def __call__(  self:   List[Any]		,     __A:   Tuple   )    ->				Optional[Any]:
												_A								= nn.Dense(self.time_embed_dim		,     dtype=self.dtype		,     name='''linear_1'''   )(__A   )
												_A								= nn.silu(__A   )
												_A								= nn.Dense(self.time_embed_dim		,     dtype=self.dtype		,     name='''linear_2'''   )(__A   )
												return temb





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					32
							A_						  =					False
							A_						  =					1

							@nn.compact
							def __call__(  self:   Union[str, Any]		,     __A:   List[str]   )    ->				Any:
												return get_sinusoidal_embeddings(
												    __A		,     embedding_dim=self.dim		,     flip_sin_to_cos=self.flip_sin_to_cos		,     freq_shift=self.freq_shift   )











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







					_A								= url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					_A								= BeautifulSoup(requests.get(_lowercase ).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(_lowercase					,			_lowercase )
					}




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







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


if __name__ == "__main__":
				write_movies()



