




'''simple docstring'''






import json
import os
import tempfile

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


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=False							,			__SCREAMING_SNAKE_CASE=False							,			__SCREAMING_SNAKE_CASE=False		)						->      Optional[Any]:
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      snake_case__					:      Dict  			=						[]
      for i in range(image_processor_tester.batch_size		):
            if equal_resolution:
                  snake_case__					:      Any  			=						image_processor_tester.max_resolution
            else:
                  # To avoid getting image width/height 0
                  snake_case__					:      int  			=						image_processor_tester.min_resolution
                  if getattr(__snake_case							,			'size_divisor'							,			__snake_case		):
                        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                        snake_case__					:      List[str]  			=						max(image_processor_tester.size_divisor							,			__snake_case		)
                  snake_case__			,       snake_case__					:      str  			=						np.random.choice(np.arange(__snake_case							,			image_processor_tester.max_resolution		)							,			2		)
            image_inputs.append(
                np.random.randint(255							,			size=(image_processor_tester.num_channels, width, height)							,			dtype=np.uinta		)		)

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            snake_case__					:      List[str]  			=						[Image.fromarray(np.moveaxis(__snake_case							,			0							,			-1		)		) for image in image_inputs]

      if torchify:
            snake_case__					:      Any  			=						[torch.from_numpy(__snake_case		) for image in image_inputs]

      return image_inputs






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=10							,			__SCREAMING_SNAKE_CASE=10							,			__SCREAMING_SNAKE_CASE=False							,			__SCREAMING_SNAKE_CASE=False		)						->      List[str]:
      snake_case__					:      str  			=						[]
      for i in range(image_processor_tester.num_frames		):
            video.append(np.random.randint(255							,			size=(image_processor_tester.num_channels, width, height)							,			dtype=np.uinta		)		)

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            snake_case__					:      Any  			=						[Image.fromarray(np.moveaxis(__snake_case							,			0							,			-1		)		) for frame in video]

      if torchify:
            snake_case__					:      str  			=						[torch.from_numpy(__snake_case		) for frame in video]

      return video






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=False							,			__SCREAMING_SNAKE_CASE=False							,			__SCREAMING_SNAKE_CASE=False		)						->      Tuple:
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      snake_case__					:      Tuple  			=						[]
      for i in range(image_processor_tester.batch_size		):
            if equal_resolution:
                  snake_case__					:      Optional[Any]  			=						image_processor_tester.max_resolution
            else:
                  snake_case__			,       snake_case__					:      Tuple  			=						np.random.choice(
                      np.arange(image_processor_tester.min_resolution							,			image_processor_tester.max_resolution		)							,			2		)
                  snake_case__					:      List[Any]  			=						prepare_video(
                      image_processor_tester=__snake_case							,			width=__snake_case							,			height=__snake_case							,			numpify=__snake_case							,			torchify=__snake_case							,			)
            video_inputs.append(__snake_case		)

      return video_inputs






class 					lowercase_			:
     A_       							= None





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Tuple  			=						self.image_processing_class(**self.image_processor_dict )
           snake_case__					:      List[str]  			=						json.loads(image_processor.to_json_string() )
           for key, value in self.image_processor_dict.items():
                 self.assertEqual(obj[key]    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Tuple  			=						self.image_processing_class(**self.image_processor_dict )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 snake_case__					:      List[Any]  			=						os.path.join(__lowerCamelCase    ,       'image_processor.json' )
                 image_processor_first.to_json_file(__lowerCamelCase )
                 snake_case__					:      Union[str, Any]  			=						self.image_processing_class.from_json_file(__lowerCamelCase )

           self.assertEqual(image_processor_second.to_dict()    ,       image_processor_first.to_dict() )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      Dict  			=						self.image_processing_class(**self.image_processor_dict )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 snake_case__					:      List[Any]  			=						image_processor_first.save_pretrained(__lowerCamelCase )[0]
                 check_json_file_has_correct_format(__lowerCamelCase )
                 snake_case__					:      Optional[Any]  			=						self.image_processing_class.from_pretrained(__lowerCamelCase )

           self.assertEqual(image_processor_second.to_dict()    ,       image_processor_first.to_dict() )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      int  			=						self.image_processing_class()
           self.assertIsNotNone(__lowerCamelCase )





     @require_torch
     @require_vision
     def  _lowerCAmelCase			(    self    :				Optional[int] ):







           if self.test_cast_dtype is not None:
                 # Initialize image_processor
                 snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )

                 # create random PyTorch tensors
                 snake_case__					:      Any  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       torchify=__lowerCamelCase )

                 snake_case__					:      int  			=						image_processor(__lowerCamelCase    ,       return_tensors='pt' )
                 # for layoutLM compatiblity
                 self.assertEqual(encoding.pixel_values.device    ,       torch.device('cpu' ) )
                 self.assertEqual(encoding.pixel_values.dtype    ,       torch.floataa )

                 snake_case__					:      List[Any]  			=						image_processor(__lowerCamelCase    ,       return_tensors='pt' ).to(torch.floataa )
                 self.assertEqual(encoding.pixel_values.device    ,       torch.device('cpu' ) )
                 self.assertEqual(encoding.pixel_values.dtype    ,       torch.floataa )

                 snake_case__					:      List[str]  			=						image_processor(__lowerCamelCase    ,       return_tensors='pt' ).to('cpu'    ,       torch.bfloataa )
                 self.assertEqual(encoding.pixel_values.device    ,       torch.device('cpu' ) )
                 self.assertEqual(encoding.pixel_values.dtype    ,       torch.bfloataa )

                 with self.assertRaises(__lowerCamelCase ):
                       snake_case__					:      str  			=						image_processor(__lowerCamelCase    ,       return_tensors='pt' ).to(torch.bfloataa    ,       'cpu' )

                 # Try with text + image feature
                 snake_case__					:      Union[str, Any]  			=						image_processor(__lowerCamelCase    ,       return_tensors='pt' )
                 encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
                 snake_case__					:      int  			=						encoding.to(torch.floataa )

                 self.assertEqual(encoding.pixel_values.device    ,       torch.device('cpu' ) )
                 self.assertEqual(encoding.pixel_values.dtype    ,       torch.floataa )
                 self.assertEqual(encoding.input_ids.dtype    ,       torch.long )






'''simple docstring'''






import bsa
import requests






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      dict[str, str]:
      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		)						->      tuple:
      snake_case__					:      Tuple  			=						int(float(__SCREAMING_SNAKE_CASE		)		)
      if num_movies < 1:
            return ()
      snake_case__					:      Any  			=						(
          'https://www.imdb.com/search/title?title_type='
          f"feature&sort=num_votes,desc&count={num_movies}"
      )
      snake_case__					:      Union[str, 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

      A_ 			=		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)
          )
      )
