






'''simple docstring'''


import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	Optional[int]									=						requests.get(lowercase__       ,		headers=lowercase__		)
 with open(lowercase__       ,		'wb'		) as file:
  file.write(image.content		)






def   lowerCAmelCase__			(					):
 __a    :	Optional[int]									=						BeautifulSoup(requests.get(lowercase__       ,		headers=lowercase__		).text       ,		'html.parser'		)
 __a    :	Dict									=						soup.find('meta'       ,		attrs={'property': 'og:title'}		).attrs["content"]
 __a    :	Union[str, Any]									=						soup.find('meta'       ,		attrs={'property': 'og:image'}		).attrs["content"]
 __a    :	str									=						soup.find('p'       ,		id='description'		).get_text()
 __a    :	List[Any]									=						os.path.splitext(os.path.basename(lowercase__		)		)
 __a    :	List[str]									=						title.strip().replace(' '       ,		'_'		)
 __a    :	Optional[Any]									=						f'''{image_title}{image_extension}'''
 save_image(lowercase__       ,		lowercase__		)
 return (title, description, image_title)


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













'''simple docstring'''


from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def   lowerCAmelCase__			(					cls		):
   return cls






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		):
 __a    :	int									=						device
 __a    :	Union[str, Any]									=						shape[0]

 __a    :	Union[str, Any]									=						layout or torch.strided
 __a    :	Any									=						device or torch.device('cpu'		)

 if generator is not None:
  __a    :	Optional[int]									=						generator.device.type if not isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   __a    :	int									=						'cpu'
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''		)
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''		)

 if isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  __a    :	Optional[int]									=						(1,) + shape[1:]
  __a    :	Dict									=						[
      torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=generator[i]       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		)
      for i in range(SCREAMING_SNAKE_CASE__		)
  ]
  __a    :	List[str]									=						torch.cat(SCREAMING_SNAKE_CASE__       ,		dim=0		).to(SCREAMING_SNAKE_CASE__		)
 else:
  __a    :	Tuple									=						torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

 return latents






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if is_torch_version('<'       ,		'2.0.0'		) or not hasattr(SCREAMING_SNAKE_CASE__       ,		'_dynamo'		):
  return False
 return isinstance(SCREAMING_SNAKE_CASE__       ,		torch._dynamo.eval_frame.OptimizedModule		)




