

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__		   =    logging.get_logger(__name__)

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       "mt5"
       __SCREAMING_SNAKE_CASE													=       ["past_key_values"]


       def __init__(		self			,   _UpperCamelCase=25_01_12			,   _UpperCamelCase=5_12			,   _UpperCamelCase=64			,   _UpperCamelCase=10_24			,   _UpperCamelCase=8			,   _UpperCamelCase=None			,   _UpperCamelCase=6			,   _UpperCamelCase=32			,   _UpperCamelCase=1_28			,   _UpperCamelCase=0.1			,   _UpperCamelCase=1E-6			,   _UpperCamelCase=1.0			,   _UpperCamelCase="gated-gelu"			,   _UpperCamelCase=True			,   _UpperCamelCase=True			,   _UpperCamelCase="T5Tokenizer"			,   _UpperCamelCase=False			,   _UpperCamelCase=0			,   _UpperCamelCase=1			,   _UpperCamelCase=0			,   **_UpperCamelCase			,   )			->       Any:






              """simple docstring"""


              super().__init__(
                  is_encoder_decoder=_UpperCamelCase			,   tokenizer_class=_UpperCamelCase			,   tie_word_embeddings=_UpperCamelCase			,   pad_token_id=_UpperCamelCase			,   eos_token_id=_UpperCamelCase			,   decoder_start_token_id=_UpperCamelCase			,   **_UpperCamelCase			,   )
              __snake_case					  =       vocab_size
              __snake_case					  =       d_model
              __snake_case					  =       d_kv
              __snake_case					  =       d_ff
              __snake_case					  =       num_layers
              __snake_case					  =       (
                  num_decoder_layers if num_decoder_layers is not None else self.num_layers
              )  # default = symmetry
              __snake_case					  =       num_heads
              __snake_case					  =       relative_attention_num_buckets
              __snake_case					  =       relative_attention_max_distance
              __snake_case					  =       dropout_rate
              __snake_case					  =       layer_norm_epsilon
              __snake_case					  =       initializer_factor
              __snake_case					  =       feed_forward_proj
              __snake_case					  =       use_cache

              __snake_case					  =       self.feed_forward_proj.split("""-"""      )
              __snake_case					  =       act_info[-1]
              __snake_case					  =       act_info[0] == """gated"""

              if len(_UpperCamelCase      ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase      ) > 2:
                     raise ValueError(
                         F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                         """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                         """'gated-gelu' or 'relu'"""      )

              # for backwards compatibility
              if feed_forward_proj == "gated-gelu":
                     __snake_case					  =       """gelu_new"""


       @property
       def  a		(		self      )			->       Union[str, Any]:






              """simple docstring"""


              return self.d_model


       @property
       def  a		(		self      )			->       str:






              """simple docstring"""


              return self.num_heads






       @property
       def  a		(		self      )			->       Optional[int]:






              """simple docstring"""


              return self.num_layers






class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def  a		(		self      )			->       Mapping[str, Mapping[int, str]]:






              """simple docstring"""


              __snake_case					  =       {
                  """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
                  """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
              }
              if self.use_past:
                     __snake_case					  =       """past_encoder_sequence + sequence"""
                     __snake_case					  =       {0: """batch"""}
                     __snake_case					  =       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                     __snake_case					  =       {0: """batch""", 1: """decoder_sequence"""}
                     __snake_case					  =       {0: """batch""", 1: """decoder_sequence"""}

              if self.use_past:
                     self.fill_with_past_key_values_(_UpperCamelCase			,   direction="""inputs"""      )

              return common_inputs


       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def  a		(		self      )			->       int:






              """simple docstring"""


              return 13






       @property
       def  a		(		self      )			->       float:






              """simple docstring"""


              return 5E-4




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase__		   =    {'''UserAgent''': UserAgent().random}
UpperCamelCase__		   =    '''https://www.mywaifulist.moe/random'''







def 	lowerCamelCase__			(   __A						:str  ,__A						:str		):


       """simple docstring"""




       __snake_case					  =       requests.get(__A  ,headers=__A		)
       with open(__A  ,"""wb"""		) as file:
              file.write(image.content		)







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __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__":
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__		   =    random_anime_character()
    print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')

