code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE__				:    List[Any]         =    {
      'auto_factory': ['get_values'],
      'configuration_auto': ['ALL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CONFIG_MAPPING', 'MODEL_NAMES_MAPPING', 'AutoConfig'],
      'feature_extraction_auto': ['FEATURE_EXTRACTOR_MAPPING', 'AutoFeatureExtractor'],
      'image_processing_auto': ['IMAGE_PROCESSOR_MAPPING', 'AutoImageProcessor'],
      'processing_auto': ['PROCESSOR_MAPPING', 'AutoProcessor'],
      'tokenization_auto': ['TOKENIZER_MAPPING', 'AutoTokenizer'],
  }
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     SCREAMING_SNAKE_CASE__				:    Tuple         =    [
         'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
         'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING',
         'MODEL_FOR_AUDIO_XVECTOR_MAPPING',
         'MODEL_FOR_BACKBONE_MAPPING',
         'MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING',
         'MODEL_FOR_CAUSAL_LM_MAPPING',
         'MODEL_FOR_CTC_MAPPING',
         'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
         'MODEL_FOR_DEPTH_ESTIMATION_MAPPING',
         'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
         'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING',
         'MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING',
         'MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
         'MODEL_FOR_MASKED_LM_MAPPING',
         'MODEL_FOR_MASK_GENERATION_MAPPING',
         'MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
         'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
         'MODEL_FOR_OBJECT_DETECTION_MAPPING',
         'MODEL_FOR_PRETRAINING_MAPPING',
         'MODEL_FOR_QUESTION_ANSWERING_MAPPING',
         'MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
         'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
         'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
         'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
         'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
         'MODEL_FOR_TEXT_ENCODING_MAPPING',
         'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
         'MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING',
         'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING',
         'MODEL_FOR_VISION_2_SEQ_MAPPING',
         'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING',
         'MODEL_MAPPING',
         'MODEL_WITH_LM_HEAD_MAPPING',
         'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
         'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING',
         'AutoModel',
         'AutoBackbone',
         'AutoModelForAudioClassification',
         'AutoModelForAudioFrameClassification',
         'AutoModelForAudioXVector',
         'AutoModelForCausalLM',
         'AutoModelForCTC',
         'AutoModelForDepthEstimation',
         'AutoModelForImageClassification',
         'AutoModelForImageSegmentation',
         'AutoModelForInstanceSegmentation',
         'AutoModelForMaskGeneration',
         'AutoModelForTextEncoding',
         'AutoModelForMaskedImageModeling',
         'AutoModelForMaskedLM',
         'AutoModelForMultipleChoice',
         'AutoModelForNextSentencePrediction',
         'AutoModelForObjectDetection',
         'AutoModelForPreTraining',
         'AutoModelForQuestionAnswering',
         'AutoModelForSemanticSegmentation',
         'AutoModelForSeq2SeqLM',
         'AutoModelForSequenceClassification',
         'AutoModelForSpeechSeq2Seq',
         'AutoModelForTableQuestionAnswering',
         'AutoModelForTokenClassification',
         'AutoModelForUniversalSegmentation',
         'AutoModelForVideoClassification',
         'AutoModelForVision2Seq',
         'AutoModelForVisualQuestionAnswering',
         'AutoModelForDocumentQuestionAnswering',
         'AutoModelWithLMHead',
         'AutoModelForZeroShotImageClassification',
         'AutoModelForZeroShotObjectDetection',
     ]
  
  try:
     if not is_tf_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     SCREAMING_SNAKE_CASE__				:    Optional[int]         =    [
         'TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
         'TF_MODEL_FOR_CAUSAL_LM_MAPPING',
         'TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
         'TF_MODEL_FOR_MASK_GENERATION_MAPPING',
         'TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
         'TF_MODEL_FOR_MASKED_LM_MAPPING',
         'TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
         'TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
         'TF_MODEL_FOR_PRETRAINING_MAPPING',
         'TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
         'TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
         'TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
         'TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
         'TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
         'TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
         'TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
         'TF_MODEL_FOR_TEXT_ENCODING_MAPPING',
         'TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
         'TF_MODEL_FOR_VISION_2_SEQ_MAPPING',
         'TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
         'TF_MODEL_MAPPING',
         'TF_MODEL_WITH_LM_HEAD_MAPPING',
         'TFAutoModel',
         'TFAutoModelForAudioClassification',
         'TFAutoModelForCausalLM',
         'TFAutoModelForImageClassification',
         'TFAutoModelForMaskedImageModeling',
         'TFAutoModelForMaskedLM',
         'TFAutoModelForMaskGeneration',
         'TFAutoModelForMultipleChoice',
         'TFAutoModelForNextSentencePrediction',
         'TFAutoModelForPreTraining',
         'TFAutoModelForDocumentQuestionAnswering',
         'TFAutoModelForQuestionAnswering',
         'TFAutoModelForSemanticSegmentation',
         'TFAutoModelForSeq2SeqLM',
         'TFAutoModelForSequenceClassification',
         'TFAutoModelForSpeechSeq2Seq',
         'TFAutoModelForTableQuestionAnswering',
         'TFAutoModelForTextEncoding',
         'TFAutoModelForTokenClassification',
         'TFAutoModelForVision2Seq',
         'TFAutoModelForZeroShotImageClassification',
         'TFAutoModelWithLMHead',
     ]
  
  try:
     if not is_flax_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     SCREAMING_SNAKE_CASE__				:    int         =    [
         'FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
         'FLAX_MODEL_FOR_CAUSAL_LM_MAPPING',
         'FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
         'FLAX_MODEL_FOR_MASKED_LM_MAPPING',
         'FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
         'FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
         'FLAX_MODEL_FOR_PRETRAINING_MAPPING',
         'FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
         'FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
         'FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
         'FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
         'FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
         'FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING',
         'FLAX_MODEL_MAPPING',
         'FlaxAutoModel',
         'FlaxAutoModelForCausalLM',
         'FlaxAutoModelForImageClassification',
         'FlaxAutoModelForMaskedLM',
         'FlaxAutoModelForMultipleChoice',
         'FlaxAutoModelForNextSentencePrediction',
         'FlaxAutoModelForPreTraining',
         'FlaxAutoModelForQuestionAnswering',
         'FlaxAutoModelForSeq2SeqLM',
         'FlaxAutoModelForSequenceClassification',
         'FlaxAutoModelForSpeechSeq2Seq',
         'FlaxAutoModelForTokenClassification',
         'FlaxAutoModelForVision2Seq',
     ]
  
  
  if TYPE_CHECKING:
     from .auto_factory import get_values
     from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
     from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
     from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
     from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
     from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_auto import (
            MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_XVECTOR_MAPPING,
            MODEL_FOR_BACKBONE_MAPPING,
            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
            MODEL_FOR_CAUSAL_LM_MAPPING,
            MODEL_FOR_CTC_MAPPING,
            MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
            MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
            MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
            MODEL_FOR_MASK_GENERATION_MAPPING,
            MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            MODEL_FOR_MASKED_LM_MAPPING,
            MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            MODEL_FOR_OBJECT_DETECTION_MAPPING,
            MODEL_FOR_PRETRAINING_MAPPING,
            MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_TEXT_ENCODING_MAPPING,
            MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
            MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
            MODEL_FOR_VISION_2_SEQ_MAPPING,
            MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
            MODEL_MAPPING,
            MODEL_WITH_LM_HEAD_MAPPING,
            AutoBackbone,
            AutoModel,
            AutoModelForAudioClassification,
            AutoModelForAudioFrameClassification,
            AutoModelForAudioXVector,
            AutoModelForCausalLM,
            AutoModelForCTC,
            AutoModelForDepthEstimation,
            AutoModelForDocumentQuestionAnswering,
            AutoModelForImageClassification,
            AutoModelForImageSegmentation,
            AutoModelForInstanceSegmentation,
            AutoModelForMaskedImageModeling,
            AutoModelForMaskedLM,
            AutoModelForMaskGeneration,
            AutoModelForMultipleChoice,
            AutoModelForNextSentencePrediction,
            AutoModelForObjectDetection,
            AutoModelForPreTraining,
            AutoModelForQuestionAnswering,
            AutoModelForSemanticSegmentation,
            AutoModelForSeqaSeqLM,
            AutoModelForSequenceClassification,
            AutoModelForSpeechSeqaSeq,
            AutoModelForTableQuestionAnswering,
            AutoModelForTextEncoding,
            AutoModelForTokenClassification,
            AutoModelForUniversalSegmentation,
            AutoModelForVideoClassification,
            AutoModelForVisionaSeq,
            AutoModelForVisualQuestionAnswering,
            AutoModelForZeroShotImageClassification,
            AutoModelForZeroShotObjectDetection,
            AutoModelWithLMHead,
        )
  
     try:
        if not is_tf_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_tf_auto import (
            TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_MASK_GENERATION_MAPPING,
            TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            TF_MODEL_FOR_MASKED_LM_MAPPING,
            TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            TF_MODEL_FOR_PRETRAINING_MAPPING,
            TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
            TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_MAPPING,
            TF_MODEL_WITH_LM_HEAD_MAPPING,
            TFAutoModel,
            TFAutoModelForAudioClassification,
            TFAutoModelForCausalLM,
            TFAutoModelForDocumentQuestionAnswering,
            TFAutoModelForImageClassification,
            TFAutoModelForMaskedImageModeling,
            TFAutoModelForMaskedLM,
            TFAutoModelForMaskGeneration,
            TFAutoModelForMultipleChoice,
            TFAutoModelForNextSentencePrediction,
            TFAutoModelForPreTraining,
            TFAutoModelForQuestionAnswering,
            TFAutoModelForSemanticSegmentation,
            TFAutoModelForSeqaSeqLM,
            TFAutoModelForSequenceClassification,
            TFAutoModelForSpeechSeqaSeq,
            TFAutoModelForTableQuestionAnswering,
            TFAutoModelForTextEncoding,
            TFAutoModelForTokenClassification,
            TFAutoModelForVisionaSeq,
            TFAutoModelForZeroShotImageClassification,
            TFAutoModelWithLMHead,
        )
  
     try:
        if not is_flax_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_flax_auto import (
            FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_MASKED_LM_MAPPING,
            FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            FLAX_MODEL_FOR_PRETRAINING_MAPPING,
            FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
            FLAX_MODEL_MAPPING,
            FlaxAutoModel,
            FlaxAutoModelForCausalLM,
            FlaxAutoModelForImageClassification,
            FlaxAutoModelForMaskedLM,
            FlaxAutoModelForMultipleChoice,
            FlaxAutoModelForNextSentencePrediction,
            FlaxAutoModelForPreTraining,
            FlaxAutoModelForQuestionAnswering,
            FlaxAutoModelForSeqaSeqLM,
            FlaxAutoModelForSequenceClassification,
            FlaxAutoModelForSpeechSeqaSeq,
            FlaxAutoModelForTokenClassification,
            FlaxAutoModelForVisionaSeq,
        )
  
  else:
     import sys
  
     SCREAMING_SNAKE_CASE__				:    int         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  import os
  
  import requests
  from bsa import BeautifulSoup
  from fake_useragent import UserAgent
  
  SCREAMING_SNAKE_CASE__				:    int         =    {'UserAgent': UserAgent().random}
  SCREAMING_SNAKE_CASE__				:    Tuple         =    'https://www.mywaifulist.moe/random'
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       str    ):
    _UpperCAmelCase		:       Any       =    requests.get(snake_case__	,      headers=snake_case__    )
    with open(snake_case__	,      """wb"""    ) as file:
      file.write(image.content    )
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       List[Any]       =    BeautifulSoup(requests.get(snake_case__	,      headers=snake_case__    ).text	,      """html.parser"""    )
    _UpperCAmelCase		:       Optional[int]       =    soup.find("""meta"""	,      attrs={"""property""": """og:title"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       int       =    soup.find("""meta"""	,      attrs={"""property""": """og:image"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       Any       =    soup.find("""p"""	,      id="""description"""    ).get_text()
    _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    os.path.splitext(os.path.basename(snake_case__    )    )
    _UpperCAmelCase		:       Tuple       =    title.strip().replace(""" """	,      """_"""    )
    _UpperCAmelCase		:       Optional[Any]       =    f'''{image_title}{image_extension}'''
    save_image(snake_case__	,      snake_case__    )
    return (title, description, image_title)
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Optional[int]         =    random_anime_character()
   print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')
  
  
  
  
  
  