






'''simple docstring'''







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase    :      Any  			=		{'UserAgent': UserAgent().random}
def 						A__			(				__lowerCAmelCase		:			List[Any]						):
      lowerCamelCase__         =  script.contents[0]
      lowerCamelCase__         =  json.loads(data[data.find("""{\"config\""""						) : -1]						)
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]




class 		UpperCamelCase__    :




  '''simple docstring'''





  def __init__(      self   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  F'''https://www.instagram.com/{username}/'''
        lowerCamelCase__         =  self.get_json()






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  requests.get(self.url   ,headers=_SCREAMING_SNAKE_CASE       ).text
        lowerCamelCase__         =  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       ):
        return F'''{self.__class__.__name__}(\'{self.username}\')'''






  def __str__(      self       ):
        return F'''{self.fullname} ({self.username}) is {self.biography}'''






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






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






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






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






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






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






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






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






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






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






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



def 						A__			(				__lowerCAmelCase		:			str = "github"						):
      import os

      if os.environ.get("""CI"""						):
            return  # test failing on GitHub Actions
      lowerCamelCase__         =  InstagramUser(__lowerCAmelCase						)
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data   ,    __lowerCAmelCase						)
      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    :      Union[str, 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 = }')









'''simple docstring'''







import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						if hor == 128:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""")

						elif hor == 32:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 64, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
						lowerCamelCase__         =  torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
						lowerCamelCase__         =  model.state_dict()
						lowerCamelCase__         =  {
						    """down_block_types""": down_block_types,
						    """block_out_channels""": block_out_channels,
						    """up_block_types""": up_block_types,
						    """layers_per_block""": 1,
						    """use_timestep_embedding""": True,
						    """out_block_type""": """OutConv1DBlock""",
						    """norm_num_groups""": 8,
						    """downsample_each_block""": False,
						    """in_channels""": 14,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """sample_size""": 6_5536,
						    """mid_block_type""": """MidResTemporalBlock1D""",
						    """act_fn""": """mish""",
						}
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)
						lowerCamelCase__         =  dict(zip(model.state_dict().keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)
						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    F'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''						)
						with open(F'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				):
						lowerCamelCase__         =  {
						    """in_channels""": 14,
						    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
						    """up_block_types""": (),
						    """out_block_type""": """ValueFunction""",
						    """mid_block_type""": """ValueFunctionMidBlock1D""",
						    """block_out_channels""": (32, 64, 128, 256),
						    """layers_per_block""": 1,
						    """downsample_each_block""": True,
						    """sample_size""": 6_5536,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """use_timestep_embedding""": True,
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """norm_num_groups""": 8,
						    """act_fn""": """mish""",
						}

						lowerCamelCase__         =  torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
						lowerCamelCase__         =  model
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)

						lowerCamelCase__         =  dict(zip(state_dict.keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)

						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""						)
						with open("""hub/hopper-medium-v2/value_function/config.json"""   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()

