

'''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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase_						   =			logging.get_logger(__name__)


if is_vision_available():
						import PIL







class lowerCamelCase      (						_a							):
				snake_case_           =   ['pixel_values']

				def __init__(					self,	lowercase_ = True,	lowercase_ = None,	lowercase_ = PILImageResampling.BICUBIC,	lowercase_ = True,	lowercase_ = None,	lowercase_ = True,	lowercase_ = 1 / 255,	lowercase_ = True,	lowercase_ = None,	lowercase_ = None,	lowercase_ = True,	**lowercase_,	)						->       None:
					super().__init__(**lowercase_   )
					snake_case							 =				size if size is not None else {'shortest_edge': 224}
					snake_case							 =				get_size_dict(lowercase_,	default_to_square=lowercase_   )
					snake_case							 =				crop_size if crop_size is not None else {'height': 224, 'width': 224}
					snake_case							 =				get_size_dict(lowercase_   )

					snake_case							 =				do_resize
					snake_case							 =				size
					snake_case							 =				resample
					snake_case							 =				do_center_crop
					snake_case							 =				crop_size
					snake_case							 =				do_rescale
					snake_case							 =				rescale_factor
					snake_case							 =				do_normalize
					snake_case							 =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					snake_case							 =				image_std if image_std is not None else OPENAI_CLIP_STD
					snake_case							 =				do_convert_rgb

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_ = PILImageResampling.BICUBIC,	lowercase_ = None,	**lowercase_,	)						->       np.ndarray:
					snake_case							 =				get_size_dict(lowercase_,	default_to_square=lowercase_   )
					snake_case							 =				get_resize_output_image_size(
					    lowercase_,	size=(size['height'], size['width']),	default_to_square=lowercase_   )
					return resize(lowercase_,	size=lowercase_,	resample=lowercase_,	data_format=lowercase_,	**lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_ = None,	**lowercase_,	)						->       np.ndarray:
					snake_case							 =				get_size_dict(lowercase_   )
					return center_crop(lowercase_,	size=(size['height'], size['width']),	data_format=lowercase_,	**lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_ = None,	**lowercase_,	)						->       Dict:
					return rescale(lowercase_,	scale=lowercase_,	data_format=lowercase_,	**lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_ = None,	**lowercase_,	)						->       np.ndarray:
					return normalize(lowercase_,	mean=lowercase_,	std=lowercase_,	data_format=lowercase_,	**lowercase_   )






				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	lowercase_ = ChannelDimension.FIRST,	**lowercase_,	)						->       PIL.Image.Image:
					snake_case							 =				do_resize if do_resize is not None else self.do_resize
					snake_case							 =				size if size is not None else self.size
					snake_case							 =				get_size_dict(lowercase_,	default_to_square=lowercase_   )
					snake_case							 =				resample if resample is not None else self.resample
					snake_case							 =				do_center_crop if do_center_crop is not None else self.do_center_crop
					snake_case							 =				crop_size if crop_size is not None else self.crop_size
					snake_case							 =				get_size_dict(lowercase_   )
					snake_case							 =				do_rescale if do_rescale is not None else self.do_rescale
					snake_case							 =				rescale_factor if rescale_factor is not None else self.rescale_factor
					snake_case							 =				do_normalize if do_normalize is not None else self.do_normalize
					snake_case							 =				image_mean if image_mean is not None else self.image_mean
					snake_case							 =				image_std if image_std is not None else self.image_std
					snake_case							 =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					snake_case							 =				make_list_of_images(lowercase_   )

					if not valid_images(lowercase_   ):
						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_center_crop and crop_size is None:
						raise ValueError('Crop size must be specified if do_center_crop is True.'   )

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

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
						snake_case							 =				[convert_to_rgb(lowercase_   ) for image in images]

					# All transformations expect numpy arrays.
					snake_case							 =				[to_numpy_array(lowercase_   ) for image in images]

					if do_resize:
						snake_case							 =				[self.resize(image=lowercase_,	size=lowercase_,	resample=lowercase_   ) for image in images]

					if do_center_crop:
						snake_case							 =				[self.center_crop(image=lowercase_,	size=lowercase_   ) for image in images]

					if do_rescale:
						snake_case							 =				[self.rescale(image=lowercase_,	scale=lowercase_   ) for image in images]

					if do_normalize:
						snake_case							 =				[self.normalize(image=lowercase_,	mean=lowercase_,	std=lowercase_   ) for image in images]

					snake_case							 =				[to_channel_dimension_format(lowercase_,	lowercase_   ) for image in images]

					snake_case							 =				{'pixel_values': images}
					return BatchFeature(data=lowercase_,	tensor_type=lowercase_   )








'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase_						   =			{"UserAgent": UserAgent().random}
lowerCAmelCase_						   =			"https://www.mywaifulist.moe/random"

def     __magic_name__       ( A					,   A							)			->		None:
	snake_case							 =				requests.get(A					,   headers=A							)
	with open(A					,   'wb'							) as file:
		file.write(image.content							)

def     __magic_name__       ( )			->		tuple[str, str, str]:
	snake_case							 =				BeautifulSoup(requests.get(A					,   headers=A							).text					,   'html.parser'							)
	snake_case							 =				soup.find('meta'					,   attrs={'property': 'og:title'}							).attrs['content']
	snake_case							 =				soup.find('meta'					,   attrs={'property': 'og:image'}							).attrs['content']
	snake_case							 =				soup.find('p'					,   id='description'							).get_text()
	snake_case , snake_case							 =				os.path.splitext(os.path.basename(A							)							)
	snake_case							 =				title.strip().replace(' '					,   '_'							)
	snake_case							 =				F'''{image_title}{image_extension}'''
	save_image(A					,   A							)
	return (title, description, image_title)


if __name__ == "__main__":
						lowerCAmelCase_   ,	lowerCAmelCase_   ,	lowerCAmelCase_						   =			random_anime_character()
						print(f"{title}\n\n{desc}\n\nImage saved : {image_title}")





