

'''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_     =      random_anime_character()
						print(f"{title}\n\n{desc}\n\nImage saved : {image_title}")











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)