





from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__a      : Any 				=				{'''UserAgent''': UserAgent().random}
def 		__magic_name__				( lowercase_				)		->					Dict:


						'''simple docstring'''


						UpperCamelCase  							= script.contents[0]
						UpperCamelCase  							= json.loads(data[data.find("{\"config\""				) : -1]				)
						return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]


class 							__UpperCAmelCase							:



						"""simple docstring"""







						def __init__(   self	,		SCREAMING_SNAKE_CASE				) ->      Tuple:





												"""simple docstring"""
												UpperCamelCase  							= f'''https://www.instagram.com/{username}/'''
												UpperCamelCase  							= self.get_json()




						def 		__lowerCAmelCase			(   self				) ->      Optional[Any]:





												"""simple docstring"""
												UpperCamelCase  							= requests.get(self.url	,		headers=SCREAMING_SNAKE_CASE				).text
												UpperCamelCase  							= BeautifulSoup(SCREAMING_SNAKE_CASE	,		"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				) ->      int:





												"""simple docstring"""
												return f'''{self.__class__.__name__}(\'{self.username}\')'''




						def __str__(   self				) ->      Union[str, Any]:





												"""simple docstring"""
												return f'''{self.fullname} ({self.username}) is {self.biography}'''




						@property
						def 		__lowerCAmelCase			(   self				) ->      int:





												"""simple docstring"""
												return self.user_data["username"]




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





												"""simple docstring"""
												return self.user_data["full_name"]




						@property
						def 		__lowerCAmelCase			(   self				) ->      List[Any]:





												"""simple docstring"""
												return self.user_data["biography"]




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





												"""simple docstring"""
												return self.user_data["business_email"]




						@property
						def 		__lowerCAmelCase			(   self				) ->      int:





												"""simple docstring"""
												return self.user_data["external_url"]




						@property
						def 		__lowerCAmelCase			(   self				) ->      Any:





												"""simple docstring"""
												return self.user_data["edge_followed_by"]["count"]




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





												"""simple docstring"""
												return self.user_data["edge_follow"]["count"]




						@property
						def 		__lowerCAmelCase			(   self				) ->      int:





												"""simple docstring"""
												return self.user_data["edge_owner_to_timeline_media"]["count"]




						@property
						def 		__lowerCAmelCase			(   self				) ->      int:





												"""simple docstring"""
												return self.user_data["profile_pic_url_hd"]




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





												"""simple docstring"""
												return self.user_data["is_verified"]




						@property
						def 		__lowerCAmelCase			(   self				) ->      Dict:





												"""simple docstring"""
												return self.user_data["is_private"]
def 		__magic_name__				( lowercase_ = "github"				)		->					Tuple:


						'''simple docstring'''


						import os

						if os.environ.get("CI"				):
												return  # test failing on GitHub Actions
						UpperCamelCase  							= InstagramUser(UpperCamelCase_				)
						assert instagram_user.user_data
						assert isinstance(instagram_user.user_data						,		UpperCamelCase_				)
						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 > 120000
						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()
		__a      : Optional[int] 				=				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 = }')








'''simple docstring'''
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def       a_				(	UpperCamelCase_ ):
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														A_			    =		k.replace(UpperCamelCase_						,  UpperCamelCase_ )

							if k.startswith("encoder" ):
														A_			    =		k.replace(".attn"						,  ".self_attn" )
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "final_layer_norm" )
							elif k.startswith("decoder" ):
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "encoder_attn_layer_norm" )
														A_			    =		k.replace("norm3"						,  "final_layer_norm" )
							return k







def       a_				(	UpperCamelCase_ ):
							A_			    =		[
							    "model.encoder.layernorm_embedding.weight",
							    "model.encoder.layernorm_embedding.bias",
							    "model.decoder.layernorm_embedding.weight",
							    "model.decoder.layernorm_embedding.bias",
							]
							for k in keys:
														A_			    =		sd.pop(UpperCamelCase_ )
														A_			    =		k.replace("layernorm_embedding"						,  "layer_norm" )
														assert new_k not in sd
														A_			    =		v


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		['''START''']







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		torch.load(UpperCamelCase_						,  map_location="cpu" )
							A_			    =		model["model"]
							A_			    =		BlenderbotConfig.from_json_file(UpperCamelCase_ )
							A_			    =		BlenderbotForConditionalGeneration(UpperCamelCase_ )
							A_			    =		m.model.state_dict().keys()
							A_			    =		[]
							A_			    =		{}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

														A_			    =		rename_state_dict_key(UpperCamelCase_ )
														if new_k not in valid_keys:
																					failures.append([k, new_k] )
														else:
																					A_			    =		v
							if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
														rename_layernorm_keys(UpperCamelCase_ )
							m.model.load_state_dict(UpperCamelCase_						,  strict=UpperCamelCase_ )
							m.half()
							m.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
							parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
							parser.add_argument(
							    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
							)
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





