



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VideoMAEImageProcessor






class 	_a			(      unittest.TestCase  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_=7    ,  lowercase_=3    ,  lowercase_=10    ,  lowercase_=18    ,  lowercase_=30    ,  lowercase_=400    ,  lowercase_=True    ,  lowercase_=None    ,  lowercase_=True    ,  lowercase_=[0.5, 0.5, 0.5]    ,  lowercase_=[0.5, 0.5, 0.5]    ,  lowercase_=None    ,  )	->					List[str]:
									lowerCAmelCase	:   str					     =					size if size is not None else {"""shortest_edge""": 18}
									lowerCAmelCase	:   List[Any]					     =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									lowerCAmelCase	:   Union[str, Any]					     =					parent
									lowerCAmelCase	:   Optional[int]					     =					batch_size
									lowerCAmelCase	:   str					     =					num_channels
									lowerCAmelCase	:   Optional[Any]					     =					num_frames
									lowerCAmelCase	:   Dict					     =					image_size
									lowerCAmelCase	:   Tuple					     =					min_resolution
									lowerCAmelCase	:   Optional[Any]					     =					max_resolution
									lowerCAmelCase	:   int					     =					do_resize
									lowerCAmelCase	:   Any					     =					size
									lowerCAmelCase	:   Optional[int]					     =					do_normalize
									lowerCAmelCase	:   Optional[int]					     =					image_mean
									lowerCAmelCase	:   Optional[Any]					     =					image_std
									lowerCAmelCase	:   Any					     =					crop_size






			def 							_snake_case   (    self )	->					Optional[Any]:
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}






@require_torch
@require_vision
class 	_a			(      snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   str	     =		VideoMAEImageProcessor if is_vision_available() else None






			def 							_snake_case   (    self )	->					str:
									lowerCAmelCase	:   List[str]					     =					VideoMAEImageProcessingTester(self )






			@property
			def 							_snake_case   (    self )	->					Dict:
									return self.image_processor_tester.prepare_image_processor_dict()






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(lowercase_    ,  """image_mean""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """image_std""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """do_normalize""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """do_resize""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """do_center_crop""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """size""" ) )






			def 							_snake_case   (    self )	->					str:
									lowerCAmelCase	:   Optional[int]					     =					self.image_processing_class.from_dict(self.image_processor_dict )
									self.assertEqual(image_processor.size    ,  {"""shortest_edge""": 18} )
									self.assertEqual(image_processor.crop_size    ,  {"""height""": 18, """width""": 18} )

									lowerCAmelCase	:   Optional[int]					     =					self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42    ,  crop_size=84 )
									self.assertEqual(image_processor.size    ,  {"""shortest_edge""": 42} )
									self.assertEqual(image_processor.crop_size    ,  {"""height""": 84, """width""": 84} )






			def 							_snake_case   (    self )	->					Optional[int]:
									pass






			def 							_snake_case   (    self )	->					Optional[Any]:
									# Initialize image_processing
									lowerCAmelCase	:   Any					     =					self.image_processing_class(**self.image_processor_dict )
									# create random PIL videos
									lowerCAmelCase	:   int					     =					prepare_video_inputs(self.image_processor_tester    ,  equal_resolution=lowercase_ )
									for video in video_inputs:
															self.assertIsInstance(lowercase_    ,  lowercase_ )
															self.assertIsInstance(video[0]    ,  Image.Image )

									# Test not batched input
									lowerCAmelCase	:   Any					     =					image_processing(video_inputs[0]    ,  return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,  (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,  )

									# Test batched
									lowerCAmelCase	:   List[str]					     =					image_processing(lowercase_    ,  return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,  )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									# Initialize image_processing
									lowerCAmelCase	:   str					     =					self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									lowerCAmelCase	:   Union[str, Any]					     =					prepare_video_inputs(self.image_processor_tester    ,  equal_resolution=lowercase_    ,  numpify=lowercase_ )
									for video in video_inputs:
															self.assertIsInstance(lowercase_    ,  lowercase_ )
															self.assertIsInstance(video[0]    ,  np.ndarray )

									# Test not batched input
									lowerCAmelCase	:   Any					     =					image_processing(video_inputs[0]    ,  return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,  (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,  )

									# Test batched
									lowerCAmelCase	:   Optional[int]					     =					image_processing(lowercase_    ,  return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,  )






			def 							_snake_case   (    self )	->					Any:
									# Initialize image_processing
									lowerCAmelCase	:   List[str]					     =					self.image_processing_class(**self.image_processor_dict )
									# create random PyTorch tensors
									lowerCAmelCase	:   Tuple					     =					prepare_video_inputs(self.image_processor_tester    ,  equal_resolution=lowercase_    ,  torchify=lowercase_ )
									for video in video_inputs:
															self.assertIsInstance(lowercase_    ,  lowercase_ )
															self.assertIsInstance(video[0]    ,  torch.Tensor )

									# Test not batched input
									lowerCAmelCase	:   List[str]					     =					image_processing(video_inputs[0]    ,  return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,  (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,  )

									# Test batched
									lowerCAmelCase	:   Optional[int]					     =					image_processing(lowercase_    ,  return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )    ,  )






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)
		    )
		)

