






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase_           = {'UserAgent': UserAgent().random}







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				A	 =      script.contents[0]
				A	 =      json.loads(data[data.find('{"config"'   ) : -1]   )
				return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      f'https://www.instagram.com/{username}/'
						A	 =      self.get_json()
		def       A(  self		:       Any		)    ->   dict:


						'''simple docstring'''
						A	 =      requests.get(self.url						,headers=_SCREAMING_SNAKE_CASE		).text
						A	 =      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		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						return f'{self.__class__.__name__}(\'{self.username}\')'
		def __str__(  self		:       Any		)    ->   str:


						'''simple docstring'''
						return f'{self.fullname} ({self.username}) is {self.biography}'
		@property
		def       A(  self		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						return self.user_data["username"]
		@property
		def       A(  self		:       Dict		)    ->   str:


						'''simple docstring'''
						return self.user_data["full_name"]
		@property
		def       A(  self		:       Any		)    ->   str:


						'''simple docstring'''
						return self.user_data["biography"]
		@property
		def       A(  self		:       int		)    ->   str:


						'''simple docstring'''
						return self.user_data["business_email"]
		@property
		def       A(  self		:       Any		)    ->   str:


						'''simple docstring'''
						return self.user_data["external_url"]
		@property
		def       A(  self		:       Any		)    ->   int:


						'''simple docstring'''
						return self.user_data["edge_followed_by"]["count"]
		@property
		def       A(  self		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						return self.user_data["edge_follow"]["count"]
		@property
		def       A(  self		:       str		)    ->   int:


						'''simple docstring'''
						return self.user_data["edge_owner_to_timeline_media"]["count"]
		@property
		def       A(  self		:       int		)    ->   str:


						'''simple docstring'''
						return self.user_data["profile_pic_url_hd"]
		@property
		def       A(  self		:       Optional[int]		)    ->   bool:


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







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


						'''simple docstring'''
						return self.user_data["is_private"]







def      snake_case		(							UpperCAmelCase	:       str = "github"   ):
				import os

				if os.environ.get('CI'   ):
								return  # test failing on GitHub Actions
				A	 =      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 > 1_50
				assert instagram_user.number_of_followers > 12_00_00
				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()
					lowerCAmelCase_           = 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 = }''')













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





