






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel

def 			_lowerCamelCase	(						__A    :							Union[str, Any]		,		__A    :							Optional[Any]		,		__A    :							Dict )      ->					Optional[int]:
    _UpperCAmelCase				:      List[Any]			  =				OmegaConf.load(__A )
    _UpperCAmelCase				:      Any			  =				torch.load(__A		,		map_location='''cpu''' )['''model''']
    _UpperCAmelCase				:      Optional[int]			  =				list(state_dict.keys() )

    # extract state_dict for VQVAE
    _UpperCAmelCase				:      List[str]			  =				{}
    _UpperCAmelCase				:      Optional[Any]			  =				'''first_stage_model.'''
    for key in keys:
        if key.startswith(__A ):
            _UpperCAmelCase				:      Any			  =				state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase				:      Optional[int]			  =				{}
    _UpperCAmelCase				:      List[str]			  =				'''model.diffusion_model.'''
    for key in keys:
        if key.startswith(__A ):
            _UpperCAmelCase				:      List[str]			  =				state_dict[key]

    _UpperCAmelCase				:      List[Any]			  =				config.model.params.first_stage_config.params
    _UpperCAmelCase				:      Any			  =				config.model.params.unet_config.params

    _UpperCAmelCase				:      Dict			  =				VQModel(**__A ).eval()
    vqvae.load_state_dict(__A )

    _UpperCAmelCase				:      Dict			  =				UNetLDMModel(**__A ).eval()
    unet.load_state_dict(__A )

    _UpperCAmelCase				:      str			  =				DDIMScheduler(
        timesteps=config.model.params.timesteps		,		beta_schedule='''scaled_linear'''		,		beta_start=config.model.params.linear_start		,		beta_end=config.model.params.linear_end		,		clip_sample=__A		,		)

    _UpperCAmelCase				:      Optional[Any]			  =				LDMPipeline(__A		,		__A		,		__A )
    pipeline.save_pretrained(__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)












# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					str:
    return 1 / (1 + np.exp(-z ))

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any] )      ->					Optional[int]:
    return (-y * np.log(__A ) - (1 - y) * np.log(1 - h )).mean()

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      int			  =				np.dot(__A		,		__A )
    return np.sum(y * scores - np.log(1 + np.exp(__A ) ) )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							str		,		__A    :							Dict=70_000 )      ->					Optional[Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				np.zeros(x.shape[1] )

    for iterations in range(__A ):
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Optional[Any]			  =				np.dot(x.T		,		h - y ) / y.size
        _UpperCAmelCase				:      Optional[Any]			  =				theta - alpha * gradient  # updating the weights
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Any			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				cost_function(__A		,		__A )
        if iterations % 100 == 0:
            print(f'''loss: {j} \t''' )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						datasets.load_iris()
  SCREAMING_SNAKE_CASE 							=						iris.data[:, :2]
  SCREAMING_SNAKE_CASE 							=						(iris.target != 0) * 1

  SCREAMING_SNAKE_CASE 							=						0.1
  SCREAMING_SNAKE_CASE 							=						logistic_reg(alpha, x, y, max_iterations=70000)
  print('theta: ', theta)  # printing the theta i.e our weights vector

  def      _lowerCamelCase	(						__A    :							Any )      ->					int:
      return sigmoid_function(
          np.dot(__A		,		__A ) )  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 0].min(), x[:, 0].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 1].min(), x[:, 1].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  SCREAMING_SNAKE_CASE 							=						np.c_[xxa.ravel(), xxa.ravel()]
  SCREAMING_SNAKE_CASE 							=						predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()


  plt.show()




