



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor






class 	_a			(      unittest.TestCase  ):






			@property
			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   Union[str, Any]					     =					1
									lowerCAmelCase	:   int					     =					3
									lowerCAmelCase	:   Optional[Any]					     =					8
									lowerCAmelCase	:   str					     =					8

									lowerCAmelCase	:   Optional[Any]					     =					torch.rand((batch_size, num_channels, height, width) )

									return sample






			def 							_snake_case   (    self    ,  lowercase_ )	->					Union[str, Any]:
									if isinstance(image[0]    ,  PIL.Image.Image ):
															return np.stack([np.array(lowercase_ ) for i in image]    ,  axis=0 )
									elif isinstance(lowercase_    ,  torch.Tensor ):
															return image.cpu().numpy().transpose(0    ,  2    ,  3    ,  1 )
									return image






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   Dict					     =					VaeImageProcessor(do_resize=lowercase_    ,  do_normalize=lowercase_ )

									lowerCAmelCase	:   List[str]					     =					self.dummy_sample
									lowerCAmelCase	:   int					     =					self.to_np(lowercase_ )

									for output_type in ["pt", "np", "pil"]:
															lowerCAmelCase	:   Union[str, Any]					     =					image_processor.postprocess(
															    image_processor.preprocess(lowercase_ )    ,  output_type=lowercase_    ,  )
															lowerCAmelCase	:   Union[str, Any]					     =					self.to_np(lowercase_ )
															lowerCAmelCase	:   Union[str, Any]					     =					(input_np * 255).round() if output_type == """pil""" else input_np
															assert (
															    np.abs(in_np - out_np ).max() < 1e-6
															), f"""decoded output does not match input for output_type {output_type}"""






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   List[Any]					     =					VaeImageProcessor(do_resize=lowercase_    ,  do_normalize=lowercase_ )
									lowerCAmelCase	:   Dict					     =					self.dummy_sample.cpu().numpy().transpose(0    ,  2    ,  3    ,  1 )

									for output_type in ["pt", "np", "pil"]:
															lowerCAmelCase	:   List[Any]					     =					image_processor.postprocess(image_processor.preprocess(lowercase_ )    ,  output_type=lowercase_ )

															lowerCAmelCase	:   int					     =					self.to_np(lowercase_ )
															lowerCAmelCase	:   List[str]					     =					(input_np * 255).round() if output_type == """pil""" else input_np
															assert (
															    np.abs(in_np - out_np ).max() < 1e-6
															), f"""decoded output does not match input for output_type {output_type}"""






			def 							_snake_case   (    self )	->					List[Any]:
									lowerCAmelCase	:   Tuple					     =					VaeImageProcessor(do_resize=lowercase_    ,  do_normalize=lowercase_ )

									lowerCAmelCase	:   str					     =					self.dummy_sample.cpu().numpy().transpose(0    ,  2    ,  3    ,  1 )
									lowerCAmelCase	:   Any					     =					image_processor.numpy_to_pil(lowercase_ )

									for output_type in ["pt", "np", "pil"]:
															lowerCAmelCase	:   List[str]					     =					image_processor.postprocess(image_processor.preprocess(lowercase_ )    ,  output_type=lowercase_ )
															for i, o in zip(lowercase_    ,  lowercase_ ):
																					lowerCAmelCase	:   Tuple					     =					np.array(lowercase_ )
																					lowerCAmelCase	:   Tuple					     =					self.to_np(lowercase_ ) if output_type == """pil""" else (self.to_np(lowercase_ ) * 255).round()
																					assert (
																					    np.abs(in_np - out_np ).max() < 1e-6
																					), f"""decoded output does not match input for output_type {output_type}"""






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   Dict					     =					VaeImageProcessor(do_resize=lowercase_    ,  do_normalize=lowercase_ )

									lowerCAmelCase	:   Tuple					     =					self.dummy_sample
									lowerCAmelCase	:   List[str]					     =					input_pt_ad.squeeze(0 )

									lowerCAmelCase	:   Tuple					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )
									lowerCAmelCase	:   int					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )

									lowerCAmelCase	:   List[Any]					     =					self.to_np(self.dummy_sample )
									lowerCAmelCase	:   List[Any]					     =					input_np_ad.squeeze(0 )

									lowerCAmelCase	:   List[str]					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )
									lowerCAmelCase	:   Any					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )

									assert np.abs(out_pt_ad - out_pt_ad ).max() < 1e-6
									assert np.abs(out_np_ad - out_np_ad ).max() < 1e-6






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Dict					     =					VaeImageProcessor(do_resize=lowercase_    ,  do_normalize=lowercase_ )

									lowerCAmelCase	:   Optional[int]					     =					self.dummy_sample
									lowerCAmelCase	:   Tuple					     =					list(lowercase_ )

									lowerCAmelCase	:   Union[str, Any]					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )

									lowerCAmelCase	:   List[Any]					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )

									lowerCAmelCase	:   int					     =					self.to_np(self.dummy_sample )
									list(lowercase_ )

									lowerCAmelCase	:   Any					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )

									lowerCAmelCase	:   Union[str, Any]					     =					image_processor.postprocess(
									    image_processor.preprocess(lowercase_ )    ,  output_type="""np"""    ,  )

									assert np.abs(out_pt_ad - out_pt_list ).max() < 1e-6
									assert np.abs(out_np_ad - out_np_list ).max() < 1e-6






import bsa
import requests







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						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  ):


						'''simple docstring'''




						lowerCAmelCase	:   Dict					     =					int(float(SCREAMING_SNAKE_CASE__  )  )
						if num_movies < 1:
												return ()
						lowerCAmelCase	:   List[str]					     =					(
						    """https://www.imdb.com/search/title?title_type="""
						    F"""feature&sort=num_votes,desc&count={num_movies}"""
						)
						lowerCAmelCase	:   Optional[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

		lowerCAmelCase					:				Union[str, Any]   	=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)
		    )
		)

