






import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      checkpoint

				A	 =      {}

				A	 =      vae_state_dict['encoder.conv_in.weight']
				A	 =      vae_state_dict['encoder.conv_in.bias']
				A	 =      vae_state_dict['encoder.conv_out.weight']
				A	 =      vae_state_dict['encoder.conv_out.bias']
				A	 =      vae_state_dict['encoder.norm_out.weight']
				A	 =      vae_state_dict['encoder.norm_out.bias']

				A	 =      vae_state_dict['decoder.conv_in.weight']
				A	 =      vae_state_dict['decoder.conv_in.bias']
				A	 =      vae_state_dict['decoder.conv_out.weight']
				A	 =      vae_state_dict['decoder.conv_out.bias']
				A	 =      vae_state_dict['decoder.norm_out.weight']
				A	 =      vae_state_dict['decoder.norm_out.bias']

				A	 =      vae_state_dict['quant_conv.weight']
				A	 =      vae_state_dict['quant_conv.bias']
				A	 =      vae_state_dict['post_quant_conv.weight']
				A	 =      vae_state_dict['post_quant_conv.bias']

				# Retrieves the keys for the encoder down blocks only
				A	 =      len({'.'.join(layer.split('.'   )[:3]   ) for layer in vae_state_dict if 'encoder.down' in layer}   )
				A	 =      {
				    layer_id: [key for key in vae_state_dict if f'down.{layer_id}' in key] for layer_id in range(UpperCAmelCase   )
				}

				# Retrieves the keys for the decoder up blocks only
				A	 =      len({'.'.join(layer.split('.'   )[:3]   ) for layer in vae_state_dict if 'decoder.up' in layer}   )
				A	 =      {
				    layer_id: [key for key in vae_state_dict if f'up.{layer_id}' in key] for layer_id in range(UpperCAmelCase   )
				}

				for i in range(UpperCAmelCase   ):
								A	 =      [key for key in down_blocks[i] if f'down.{i}' in key and f'down.{i}.downsample' not in key]

								if f'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
												A	 =      vae_state_dict.pop(
												    f'encoder.down.{i}.downsample.conv.weight'   )
												A	 =      vae_state_dict.pop(
												    f'encoder.down.{i}.downsample.conv.bias'   )

								A	 =      renew_vae_resnet_paths(UpperCAmelCase   )
								A	 =      {'old': f'down.{i}.block', 'new': f'down_blocks.{i}.resnets'}
								assign_to_checkpoint(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						additional_replacements=[meta_path],						config=UpperCAmelCase   )

				A	 =      [key for key in vae_state_dict if 'encoder.mid.block' in key]
				A	 =      2
				for i in range(1,						num_mid_res_blocks + 1   ):
								A	 =      [key for key in mid_resnets if f'encoder.mid.block_{i}' in key]

								A	 =      renew_vae_resnet_paths(UpperCAmelCase   )
								A	 =      {'old': f'mid.block_{i}', 'new': f'mid_block.resnets.{i - 1}'}
								assign_to_checkpoint(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						additional_replacements=[meta_path],						config=UpperCAmelCase   )

				A	 =      [key for key in vae_state_dict if 'encoder.mid.attn' in key]
				A	 =      renew_vae_attention_paths(UpperCAmelCase   )
				A	 =      {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
				assign_to_checkpoint(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						additional_replacements=[meta_path],						config=UpperCAmelCase   )
				conv_attn_to_linear(UpperCAmelCase   )

				for i in range(UpperCAmelCase   ):
								A	 =      num_up_blocks - 1 - i
								A	 =      [
								    key for key in up_blocks[block_id] if f'up.{block_id}' in key and f'up.{block_id}.upsample' not in key
								]

								if f'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
												A	 =      vae_state_dict[
												    f'decoder.up.{block_id}.upsample.conv.weight'
												]
												A	 =      vae_state_dict[
												    f'decoder.up.{block_id}.upsample.conv.bias'
												]

								A	 =      renew_vae_resnet_paths(UpperCAmelCase   )
								A	 =      {'old': f'up.{block_id}.block', 'new': f'up_blocks.{i}.resnets'}
								assign_to_checkpoint(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						additional_replacements=[meta_path],						config=UpperCAmelCase   )

				A	 =      [key for key in vae_state_dict if 'decoder.mid.block' in key]
				A	 =      2
				for i in range(1,						num_mid_res_blocks + 1   ):
								A	 =      [key for key in mid_resnets if f'decoder.mid.block_{i}' in key]

								A	 =      renew_vae_resnet_paths(UpperCAmelCase   )
								A	 =      {'old': f'mid.block_{i}', 'new': f'mid_block.resnets.{i - 1}'}
								assign_to_checkpoint(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						additional_replacements=[meta_path],						config=UpperCAmelCase   )

				A	 =      [key for key in vae_state_dict if 'decoder.mid.attn' in key]
				A	 =      renew_vae_attention_paths(UpperCAmelCase   )
				A	 =      {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
				assign_to_checkpoint(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						additional_replacements=[meta_path],						config=UpperCAmelCase   )
				conv_attn_to_linear(UpperCAmelCase   )
				return new_checkpoint







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str,						):
				# Only support V1
				A	 =      requests.get(
				    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'   )
				A	 =      io.BytesIO(r.content   )

				A	 =      OmegaConf.load(UpperCAmelCase   )
				A	 =      5_12
				A	 =      'cuda' if torch.cuda.is_available() else 'cpu'
				if checkpoint_path.endswith('safetensors'   ):
								from safetensors import safe_open

								A	 =      {}
								with safe_open(UpperCAmelCase,						framework='pt',						device='cpu'   ) as f:
												for key in f.keys():
																A	 =      f.get_tensor(UpperCAmelCase   )
				else:
								A	 =      torch.load(UpperCAmelCase,						map_location=UpperCAmelCase   )['state_dict']

				# Convert the VAE model.
				A	 =      create_vae_diffusers_config(UpperCAmelCase,						image_size=UpperCAmelCase   )
				A	 =      custom_convert_ldm_vae_checkpoint(UpperCAmelCase,						UpperCAmelCase   )

				A	 =      AutoencoderKL(**UpperCAmelCase   )
				vae.load_state_dict(UpperCAmelCase   )
				vae.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()

					parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

					lowerCAmelCase_           = parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)













from math import factorial
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      real
						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      [1] * rank
						else:
										A	 =      rank
		def __repr__(  self		:       Any		)    ->   Optional[Any]:


						'''simple docstring'''
						return (
						    f'{self.real}+'
						    f'{"+".join(str(_SCREAMING_SNAKE_CASE		)+"E"+str(n+1		)for n,dual in enumerate(self.duals		)		)}'
						)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.duals.copy()
						while cur[-1] == 0:
										cur.pop(-1		)
						return Dual(self.real						,_SCREAMING_SNAKE_CASE		)
		def __add__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										return Dual(self.real + other						,self.duals		)
						A	 =      self.duals.copy()
						A	 =      other.duals.copy()
						if len(_SCREAMING_SNAKE_CASE		) > len(_SCREAMING_SNAKE_CASE		):
										o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						elif len(_SCREAMING_SNAKE_CASE		) < len(_SCREAMING_SNAKE_CASE		):
										s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						A	 =      []
						for i in range(len(_SCREAMING_SNAKE_CASE		)		):
										new_duals.append(s_dual[i] + o_dual[i]		)
						return Dual(self.real + other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __add__
		def __sub__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						return self + other * -1
		def __mul__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i * other		)
										return Dual(self.real * other						,_SCREAMING_SNAKE_CASE		)
						A	 =      [0] * (len(self.duals		) + len(other.duals		) + 1)
						for i, item in enumerate(self.duals		):
										for j, jtem in enumerate(other.duals		):
														new_duals[i + j + 1] += item * jtem
						for k in range(len(self.duals		)		):
										new_duals[k] += self.duals[k] * other.real
						for index in range(len(other.duals		)		):
										new_duals[index] += other.duals[index] * self.real
						return Dual(self.real * other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __mul__
		def __truediv__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   List[str]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i / other		)
										return Dual(self.real / other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError
		def __floordiv__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   Dict:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i // other		)
										return Dual(self.real // other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError







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


						'''simple docstring'''
						if n < 0 or isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										raise ValueError('power must be a positive integer'		)
						if n == 0:
										return 1
						if n == 1:
										return self
						A	 =      self
						for _ in range(n - 1		):
										x *= self
						return x







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       int   ):
				if not callable(UpperCAmelCase   ):
								raise ValueError('differentiate() requires a function as input for func'   )
				if not isinstance(UpperCAmelCase,						(float, int)   ):
								raise ValueError('differentiate() requires a float as input for position'   )
				if not isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								raise ValueError('differentiate() requires an int as input for order'   )
				A	 =      Dual(UpperCAmelCase,						1   )
				A	 =      func(UpperCAmelCase   )
				if order == 0:
								return result.real
				return result.duals[order - 1] * factorial(UpperCAmelCase   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







					def      snake_case		(							UpperCAmelCase	:       int   ):
									return y**2 * y**4



					print(differentiate(f, 9, 2))





