


import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_SCREAMING_SNAKE_CASE :					Tuple			=	{"""UserAgent""": UserAgent().random}
_SCREAMING_SNAKE_CASE :					Union[str, Any]			=	"""https://www.mywaifulist.moe/random"""







def 						_lowercase       (     __lowerCamelCase			:					str		,__lowerCamelCase			:					str			)  ->   None:





     '''simple docstring'''





     UpperCamelCase__							:  str								=     requests.get(__lowerCamelCase		,headers=__lowerCamelCase			)
     with open(__lowerCamelCase		,'''wb'''			) as file:
          file.write(image.content			)







def 						_lowercase       (     )  ->   tuple[str, str, str]:





     '''simple docstring'''





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


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE :					Any			=	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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""LayoutLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[str]			=	[
       """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LayoutLMForMaskedLM""",
       """LayoutLMForSequenceClassification""",
       """LayoutLMForTokenClassification""",
       """LayoutLMForQuestionAnswering""",
       """LayoutLMModel""",
       """LayoutLMPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFLayoutLMForMaskedLM""",
       """TFLayoutLMForSequenceClassification""",
       """TFLayoutLMForTokenClassification""",
       """TFLayoutLMForQuestionAnswering""",
       """TFLayoutLMMainLayer""",
       """TFLayoutLMModel""",
       """TFLayoutLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

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




