


import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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





def     lowercase__	(  lowerCamelCase     :  int				,	lowerCamelCase     :  Dict				)					->     Dict:
  lowerCAmelCase__    :    Optional[int]								=					requests.get(lowerCamelCase				,	headers=lowerCamelCase				)
  with open(lowerCamelCase				,	"wb"				) as file:
    file.write(image.content				)





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


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




"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=      {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

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

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "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

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
