





from typing import TYPE_CHECKING

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


lowercase     :			Union[str, Any]     						=	{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			List[str]     						=	["""GPT2TokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			Tuple     						=	[
           """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPT2DoubleHeadsModel""",
           """GPT2ForQuestionAnswering""",
           """GPT2ForSequenceClassification""",
           """GPT2ForTokenClassification""",
           """GPT2LMHeadModel""",
           """GPT2Model""",
           """GPT2PreTrainedModel""",
           """load_tf_weights_in_gpt2""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			List[str]     						=	[
           """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFGPT2DoubleHeadsModel""",
           """TFGPT2ForSequenceClassification""",
           """TFGPT2LMHeadModel""",
           """TFGPT2MainLayer""",
           """TFGPT2Model""",
           """TFGPT2PreTrainedModel""",
       ]

try:
       if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			List[Any]     						=	["""TFGPT2Tokenizer"""]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			Dict     						=	["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
       from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
       from .tokenization_gpta import GPTaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpta_fast import GPTaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpta import (
                  GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTaDoubleHeadsModel,
                  GPTaForQuestionAnswering,
                  GPTaForSequenceClassification,
                  GPTaForTokenClassification,
                  GPTaLMHeadModel,
                  GPTaModel,
                  GPTaPreTrainedModel,
                  load_tf_weights_in_gpta,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_gpta import (
                  TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFGPTaDoubleHeadsModel,
                  TFGPTaForSequenceClassification,
                  TFGPTaLMHeadModel,
                  TFGPTaMainLayer,
                  TFGPTaModel,
                  TFGPTaPreTrainedModel,
              )

       try:
              if not is_keras_nlp_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpta_tf import TFGPTaTokenizer

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
       import sys

       lowercase     :			int     						=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    BeautifulSoup(requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       ).text     ,							'''html.parser'''       )
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:title'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:image'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''p'''     ,							id='''description'''       ).get_text()
       _UpperCamelCase							,		_UpperCamelCase														=    os.path.splitext(os.path.basename(lowerCAmelCase       )       )
       _UpperCamelCase														=    title.strip().replace(''' '''     ,							'''_'''       )
       _UpperCamelCase														=    f'''{image_title}{image_extension}'''
       save_image(lowerCAmelCase     ,							lowerCAmelCase       )
       return (title, description, image_title)


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






