



'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_A					:     int      =logging.get_logger(__name__)


if is_vision_available():
		import PIL



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    ["""pixel_values"""]




					def __init__(	self					:						List[Any]  ,	UpperCamelCase_					:						bool = True  ,	UpperCamelCase_					:						Dict[str, int] = None  ,	UpperCamelCase_					:						PILImageResampling = PILImageResampling.BILINEAR  ,	UpperCamelCase_					:						bool = True  ,	UpperCamelCase_					:						bool = False  ,	UpperCamelCase_					:						bool = True  ,	UpperCamelCase_					:						bool = True  ,	UpperCamelCase_					:						Union[int, float] = 1 / 255  ,	UpperCamelCase_					:						bool = True  ,	UpperCamelCase_					:						Optional[Union[float, List[float]]] = None  ,	UpperCamelCase_					:						Optional[Union[float, List[float]]] = None  ,	**UpperCamelCase_					:						Dict  ,	)	->							None:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_       )

						_lowercase       :							Union[str, Any]  	=		size if size is not None else {'height': 2560, 'width': 1920}
						if isinstance(UpperCamelCase_  ,	(tuple, list)       ):
							# The previous feature extractor size parameter was in (width, height) format
							_lowercase       :							Any  	=		size[::-1]
						_lowercase       :							Optional[Any]  	=		get_size_dict(UpperCamelCase_       )

						_lowercase       :							str  	=		do_resize
						_lowercase       :							Optional[Any]  	=		size
						_lowercase       :							List[str]  	=		resample
						_lowercase       :							List[str]  	=		do_thumbnail
						_lowercase       :							Tuple  	=		do_align_long_axis
						_lowercase       :							Any  	=		do_pad
						_lowercase       :							List[Any]  	=		do_rescale
						_lowercase       :							List[str]  	=		rescale_factor
						_lowercase       :							Union[str, Any]  	=		do_normalize
						_lowercase       :							str  	=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_lowercase       :							List[Any]  	=		image_std if image_std is not None else IMAGENET_STANDARD_STD




					def     __UpperCAmelCase  (	self					:						Dict  ,	UpperCamelCase_					:						np.ndarray  ,	UpperCamelCase_					:						Dict[str, int]  ,	UpperCamelCase_					:						Optional[Union[str, ChannelDimension]] = None       )	->							np.ndarray:



						'''simple docstring'''





						_lowercase	,     _lowercase       :							int  	=		get_image_size(UpperCamelCase_       )
						_lowercase	,     _lowercase       :							Tuple  	=		size['height'], size['width']

						if (output_width < output_height and input_width > input_height) or (
						    output_width > output_height and input_width < input_height
						):
							_lowercase       :							str  	=		np.rotaa(UpperCamelCase_  ,	3       )

						if data_format is not None:
							_lowercase       :							Any  	=		to_channel_dimension_format(UpperCamelCase_  ,	UpperCamelCase_       )

						return image




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						np.ndarray  ,	UpperCamelCase_					:						Dict[str, int]  ,	UpperCamelCase_					:						bool = False  ,	UpperCamelCase_					:						Optional[Union[str, ChannelDimension]] = None  ,	)	->							np.ndarray:



						'''simple docstring'''





						_lowercase	,     _lowercase       :							List[Any]  	=		size['height'], size['width']
						_lowercase	,     _lowercase       :							int  	=		get_image_size(UpperCamelCase_       )

						_lowercase       :							Optional[int]  	=		output_width - input_width
						_lowercase       :							Optional[Any]  	=		output_height - input_height

						if random_padding:
							_lowercase       :							Any  	=		np.random.randint(low=0  ,	high=delta_height + 1       )
							_lowercase       :							List[Any]  	=		np.random.randint(low=0  ,	high=delta_width + 1       )
						else:
							_lowercase       :							Union[str, Any]  	=		delta_height // 2
							_lowercase       :							Dict  	=		delta_width // 2

						_lowercase       :							int  	=		delta_height - pad_top
						_lowercase       :							Dict  	=		delta_width - pad_left

						_lowercase       :							Union[str, Any]  	=		((pad_top, pad_bottom), (pad_left, pad_right))
						return pad(UpperCamelCase_  ,	UpperCamelCase_  ,	data_format=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	*UpperCamelCase_					:						Optional[int]  ,	**UpperCamelCase_					:						Any       )	->							Dict:



						'''simple docstring'''





						logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'       )
						return self.pad_image(*UpperCamelCase_  ,	**UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						np.ndarray  ,	UpperCamelCase_					:						Dict[str, int]  ,	UpperCamelCase_					:						PILImageResampling = PILImageResampling.BICUBIC  ,	UpperCamelCase_					:						Optional[Union[str, ChannelDimension]] = None  ,	**UpperCamelCase_					:						Union[str, Any]  ,	)	->							np.ndarray:



						'''simple docstring'''





						_lowercase	,     _lowercase       :							List[str]  	=		get_image_size(UpperCamelCase_       )
						_lowercase	,     _lowercase       :							List[str]  	=		size['height'], size['width']

						# We always resize to the smallest of either the input or output size.
						_lowercase       :							str  	=		min(UpperCamelCase_  ,	UpperCamelCase_       )
						_lowercase       :							Optional[Any]  	=		min(UpperCamelCase_  ,	UpperCamelCase_       )

						if height == input_height and width == input_width:
							return image

						if input_height > input_width:
							_lowercase       :							int  	=		int(input_width * height / input_height       )
						elif input_width > input_height:
							_lowercase       :							Dict  	=		int(input_height * width / input_width       )

						return resize(
						    UpperCamelCase_  ,	size=(height, width)  ,	resample=UpperCamelCase_  ,	reducing_gap=2.0  ,	data_format=UpperCamelCase_  ,	**UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						int  ,	UpperCamelCase_					:						np.ndarray  ,	UpperCamelCase_					:						Dict[str, int]  ,	UpperCamelCase_					:						PILImageResampling = PILImageResampling.BICUBIC  ,	UpperCamelCase_					:						Optional[Union[str, ChannelDimension]] = None  ,	**UpperCamelCase_					:						List[str]  ,	)	->							np.ndarray:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		get_size_dict(UpperCamelCase_       )
						_lowercase       :							Any  	=		min(size['height']  ,	size['width']       )
						_lowercase       :							Optional[int]  	=		get_resize_output_image_size(UpperCamelCase_  ,	size=UpperCamelCase_  ,	default_to_square=UpperCamelCase_       )
						_lowercase       :							List[str]  	=		resize(UpperCamelCase_  ,	size=UpperCamelCase_  ,	resample=UpperCamelCase_  ,	data_format=UpperCamelCase_  ,	**UpperCamelCase_       )
						return resized_image




					def     __UpperCAmelCase  (	self					:						List[str]  ,	UpperCamelCase_					:						np.ndarray  ,	UpperCamelCase_					:						Union[int, float]  ,	UpperCamelCase_					:						Optional[Union[str, ChannelDimension]] = None  ,	**UpperCamelCase_					:						Tuple  ,	)	->							Dict:



						'''simple docstring'''





						return rescale(UpperCamelCase_  ,	scale=UpperCamelCase_  ,	data_format=UpperCamelCase_  ,	**UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						np.ndarray  ,	UpperCamelCase_					:						Union[float, List[float]]  ,	UpperCamelCase_					:						Union[float, List[float]]  ,	UpperCamelCase_					:						Optional[Union[str, ChannelDimension]] = None  ,	**UpperCamelCase_					:						Optional[Any]  ,	)	->							np.ndarray:



						'''simple docstring'''





						return normalize(UpperCamelCase_  ,	mean=UpperCamelCase_  ,	std=UpperCamelCase_  ,	data_format=UpperCamelCase_  ,	**UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Tuple  ,	UpperCamelCase_					:						ImageInput  ,	UpperCamelCase_					:						bool = None  ,	UpperCamelCase_					:						Dict[str, int] = None  ,	UpperCamelCase_					:						PILImageResampling = None  ,	UpperCamelCase_					:						bool = None  ,	UpperCamelCase_					:						bool = None  ,	UpperCamelCase_					:						bool = None  ,	UpperCamelCase_					:						bool = False  ,	UpperCamelCase_					:						bool = None  ,	UpperCamelCase_					:						float = None  ,	UpperCamelCase_					:						bool = None  ,	UpperCamelCase_					:						Optional[Union[float, List[float]]] = None  ,	UpperCamelCase_					:						Optional[Union[float, List[float]]] = None  ,	UpperCamelCase_					:						Optional[Union[str, TensorType]] = None  ,	UpperCamelCase_					:						Optional[ChannelDimension] = ChannelDimension.FIRST  ,	**UpperCamelCase_					:						Optional[Any]  ,	)	->							PIL.Image.Image:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		do_resize if do_resize is not None else self.do_resize
						_lowercase       :							str  	=		size if size is not None else self.size
						if isinstance(UpperCamelCase_  ,	(tuple, list)       ):
							# Previous feature extractor had size in (width, height) format
							_lowercase       :							int  	=		size[::-1]
						_lowercase       :							Union[str, Any]  	=		get_size_dict(UpperCamelCase_       )
						_lowercase       :							int  	=		resample if resample is not None else self.resample
						_lowercase       :							List[Any]  	=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
						_lowercase       :							Dict  	=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
						_lowercase       :							Union[str, Any]  	=		do_pad if do_pad is not None else self.do_pad
						_lowercase       :							Union[str, Any]  	=		do_rescale if do_rescale is not None else self.do_rescale
						_lowercase       :							int  	=		rescale_factor if rescale_factor is not None else self.rescale_factor
						_lowercase       :							int  	=		do_normalize if do_normalize is not None else self.do_normalize
						_lowercase       :							int  	=		image_mean if image_mean is not None else self.image_mean
						_lowercase       :							List[Any]  	=		image_std if image_std is not None else self.image_std

						_lowercase       :							Union[str, Any]  	=		make_list_of_images(UpperCamelCase_       )

						if not valid_images(UpperCamelCase_       ):
							raise ValueError(
							    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
							    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

						if do_resize and size is None:
							raise ValueError('Size must be specified if do_resize is True.'       )

						if do_rescale and rescale_factor is None:
							raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

						if do_pad and size is None:
							raise ValueError('Size must be specified if do_pad is True.'       )

						if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('Image mean and std must be specified if do_normalize is True.'       )

						# All transformations expect numpy arrays.
						_lowercase       :							Any  	=		[to_numpy_array(UpperCamelCase_       ) for image in images]

						if do_align_long_axis:
							_lowercase       :							Optional[Any]  	=		[self.align_long_axis(UpperCamelCase_  ,	size=UpperCamelCase_       ) for image in images]

						if do_resize:
							_lowercase       :							str  	=		[self.resize(image=UpperCamelCase_  ,	size=UpperCamelCase_  ,	resample=UpperCamelCase_       ) for image in images]

						if do_thumbnail:
							_lowercase       :							Union[str, Any]  	=		[self.thumbnail(image=UpperCamelCase_  ,	size=UpperCamelCase_       ) for image in images]

						if do_pad:
							_lowercase       :							Optional[Any]  	=		[self.pad_image(image=UpperCamelCase_  ,	size=UpperCamelCase_  ,	random_padding=UpperCamelCase_       ) for image in images]

						if do_rescale:
							_lowercase       :							Optional[Any]  	=		[self.rescale(image=UpperCamelCase_  ,	scale=UpperCamelCase_       ) for image in images]

						if do_normalize:
							_lowercase       :							List[str]  	=		[self.normalize(image=UpperCamelCase_  ,	mean=UpperCamelCase_  ,	std=UpperCamelCase_       ) for image in images]

						_lowercase       :							List[str]  	=		[to_channel_dimension_format(UpperCamelCase_  ,	UpperCamelCase_       ) for image in images]

						_lowercase       :							Tuple  	=		{'pixel_values': images}
						return BatchFeature(data=UpperCamelCase_  ,	tensor_type=UpperCamelCase_       )





'''simple docstring'''

import bsa
import requests







def     __UpperCamelCase					(				_lowercase       )			->      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					(				_lowercase = 5       )			->      tuple:
	_lowercase       :							List[Any]  	=		int(float(_lowercase       )       )
	if num_movies < 1:
		return ()
	_lowercase       :							Any  	=		(
	    'https://www.imdb.com/search/title?title_type='
	    f'''feature&sort=num_votes,desc&count={num_movies}'''
	)
	_lowercase       :							Tuple  	=		bsa.BeautifulSoup(requests.get(_lowercase       ).content,					'html.parser'       )
	return tuple(
	    get_movie_data_from_soup(_lowercase       )
	    for movie in source.find_all('div',					class_='lister-item mode-advanced'       )       )


if __name__ == "__main__":
		import json

		_A					:     Optional[int]      =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)
		    )
		)
