

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_     :    Dict     		=   logging.get_logger(__name__)

a_     :    Any     		=   {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class   UpperCamelCase			(  __snake_case ):
    __UpperCamelCase    ="transfo-xl"
    __UpperCamelCase    =["mems"]
    __UpperCamelCase    ={
        "n_token": "vocab_size",
        "hidden_size": "d_model",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(      self			:   List[str]				,     snake_case__			:   Union[str, Any]=2_6_7_7_3_5				,     snake_case__			:   Any=[2_0_0_0_0, 4_0_0_0_0, 2_0_0_0_0_0]				,     snake_case__			:   Any=1_0_2_4				,     snake_case__			:   Dict=1_0_2_4				,     snake_case__			:   Union[str, Any]=1_6				,     snake_case__			:   Optional[Any]=6_4				,     snake_case__			:   Union[str, Any]=4_0_9_6				,     snake_case__			:   List[Any]=4				,     snake_case__			:   Optional[int]=False				,     snake_case__			:   List[str]=1_8				,     snake_case__			:   Tuple=1_6_0_0				,     snake_case__			:   int=1_0_0_0				,     snake_case__			:   Union[str, Any]=True				,     snake_case__			:   int=True				,     snake_case__			:   int=0				,     snake_case__			:   Optional[Any]=-1				,     snake_case__			:   Dict=True				,     snake_case__			:   Any=0.1				,     snake_case__			:   Optional[int]=0.0				,     snake_case__			:   Union[str, Any]=True				,     snake_case__			:   str="normal"				,     snake_case__			:   Optional[Any]=0.01				,     snake_case__			:   List[str]=0.01				,     snake_case__			:   Tuple=0.02				,     snake_case__			:   str=1E-5				,     snake_case__			:   List[Any]=0				,     **snake_case__			:   Optional[Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      vocab_size
     SCREAMING_SNAKE_CASE		    =      []
     self.cutoffs.extend(A__     )
     if proj_share_all_but_first:
      SCREAMING_SNAKE_CASE		    =      [False] + [True] * len(self.cutoffs     )
     else:
      SCREAMING_SNAKE_CASE		    =      [False] + [False] * len(self.cutoffs     )
     SCREAMING_SNAKE_CASE		    =      d_model
     SCREAMING_SNAKE_CASE		    =      d_embed
     SCREAMING_SNAKE_CASE		    =      d_head
     SCREAMING_SNAKE_CASE		    =      d_inner
     SCREAMING_SNAKE_CASE		    =      div_val
     SCREAMING_SNAKE_CASE		    =      pre_lnorm
     SCREAMING_SNAKE_CASE		    =      n_layer
     SCREAMING_SNAKE_CASE		    =      n_head
     SCREAMING_SNAKE_CASE		    =      mem_len
     SCREAMING_SNAKE_CASE		    =      same_length
     SCREAMING_SNAKE_CASE		    =      attn_type
     SCREAMING_SNAKE_CASE		    =      clamp_len
     SCREAMING_SNAKE_CASE		    =      sample_softmax
     SCREAMING_SNAKE_CASE		    =      adaptive
     SCREAMING_SNAKE_CASE		    =      dropout
     SCREAMING_SNAKE_CASE		    =      dropatt
     SCREAMING_SNAKE_CASE		    =      untie_r
     SCREAMING_SNAKE_CASE		    =      init
     SCREAMING_SNAKE_CASE		    =      init_range
     SCREAMING_SNAKE_CASE		    =      proj_init_std
     SCREAMING_SNAKE_CASE		    =      init_std
     SCREAMING_SNAKE_CASE		    =      layer_norm_epsilon
     super().__init__(eos_token_id=A__				,     **A__     )

    @property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""     )
     return -1




    @max_position_embeddings.setter
    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   Dict     ):

     """simple docstring"""






     raise NotImplementedError(
         F"""The model {self.model_type} is one of the few models that has no sequence length limit."""     )









from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_UpperCamelCase			:	Union[str, Any]								={'UserAgent': UserAgent().random}



def  a__ (__lowercase							:Optional[Any]   )		-> dict:
				_A	:				str   =      script.contents[0]
				_A	:				Dict   =      json.loads(data[data.find('''{"config"'''   ) : -1]   )
				return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class 						UpperCAmelCase__ :







		def __init__(					self       ,A__		):
						_A	:				Any   =      f"""https://www.instagram.com/{username}/"""
						_A	:				Optional[Any]   =      self.get_json()







		def  A__      (					self		):
						_A	:				str   =      requests.get(self.url       ,headers=A__		).text
						_A	:				Dict   =      BeautifulSoup(A__       ,'''html.parser'''		).find_all('''script'''		)
						try:
										return extract_user_profile(scripts[4]		)
						except (json.decoder.JSONDecodeError, KeyError):
										return extract_user_profile(scripts[3]		)







		def __repr__(					self		):
						return f"""{self.__class__.__name__}('{self.username}')"""







		def __str__(					self		):
						return f"""{self.fullname} ({self.username}) is {self.biography}"""







		@property
		def  A__      (					self		):
						return self.user_data["username"]







		@property
		def  A__      (					self		):
						return self.user_data["full_name"]







		@property
		def  A__      (					self		):
						return self.user_data["biography"]







		@property
		def  A__      (					self		):
						return self.user_data["business_email"]







		@property
		def  A__      (					self		):
						return self.user_data["external_url"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_followed_by"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_follow"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_owner_to_timeline_media"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["profile_pic_url_hd"]







		@property
		def  A__      (					self		):
						return self.user_data["is_verified"]







		@property
		def  A__      (					self		):
						return self.user_data["is_private"]



def  a__ (__lowercase							:str = "github"   )		-> None:
				import os

				if os.environ.get('''CI'''   ):
								return  # test failing on GitHub Actions
				_A	:				Optional[int]   =      InstagramUser(__lowercase   )
				assert instagram_user.user_data
				assert isinstance(instagram_user.user_data					,  __lowercase   )
				assert instagram_user.username == username
				if username != "github":
								return
				assert instagram_user.fullname == "GitHub"
				assert instagram_user.biography == "Built for developers."
				assert instagram_user.number_of_posts > 150
				assert instagram_user.number_of_followers > 12_0000
				assert instagram_user.number_of_followings > 15
				assert instagram_user.email == "support@github.com"
				assert instagram_user.website == "https://github.com/readme"
				assert instagram_user.profile_picture_url.startswith('''https://instagram.'''   )
				assert instagram_user.is_verified is True
				assert instagram_user.is_private is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCamelCase			:	List[Any]								=InstagramUser('github')
	print(instagram_user)
	print(f'''{instagram_user.number_of_posts = }''')
	print(f'''{instagram_user.number_of_followers = }''')
	print(f'''{instagram_user.number_of_followings = }''')
	print(f'''{instagram_user.email = }''')
	print(f'''{instagram_user.website = }''')
	print(f'''{instagram_user.profile_picture_url = }''')
	print(f'''{instagram_user.is_verified = }''')
	print(f'''{instagram_user.is_private = }''')



