





"""simple docstring"""




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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  checkpoint

  A_  :       Union[str, Any]										=  {}

  A_  :       List[str]										=  vae_state_dict['encoder.conv_in.weight']
  A_  :       Union[str, Any]										=  vae_state_dict['encoder.conv_in.bias']
  A_  :       Any										=  vae_state_dict['encoder.conv_out.weight']
  A_  :       Any										=  vae_state_dict['encoder.conv_out.bias']
  A_  :       Dict										=  vae_state_dict['encoder.norm_out.weight']
  A_  :       str										=  vae_state_dict['encoder.norm_out.bias']

  A_  :       str										=  vae_state_dict['decoder.conv_in.weight']
  A_  :       str										=  vae_state_dict['decoder.conv_in.bias']
  A_  :       Optional[int]										=  vae_state_dict['decoder.conv_out.weight']
  A_  :       str										=  vae_state_dict['decoder.conv_out.bias']
  A_  :       Optional[Any]										=  vae_state_dict['decoder.norm_out.weight']
  A_  :       int										=  vae_state_dict['decoder.norm_out.bias']

  A_  :       Union[str, Any]										=  vae_state_dict['quant_conv.weight']
  A_  :       Optional[Any]										=  vae_state_dict['quant_conv.bias']
  A_  :       Optional[int]										=  vae_state_dict['post_quant_conv.weight']
  A_  :       str										=  vae_state_dict['post_quant_conv.bias']

  # Retrieves the keys for the encoder down blocks only
  A_  :       Optional[Any]										=  len({'.'.join(layer.split('.'					)[:3]					) for layer in vae_state_dict if 'encoder.down' in layer}					)
  A_  :       Union[str, Any]										=  {
      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_  :       Optional[Any]										=  len({'.'.join(layer.split('.'					)[:3]					) for layer in vae_state_dict if 'decoder.up' in layer}					)
  A_  :       Optional[int]										=  {
      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_  :       str										=  [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_  :       Dict										=  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""					)
      A_  :       str										=  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""					)

    A_  :       Any										=  renew_vae_resnet_paths(__UpperCamelCase					)
    A_  :       Union[str, Any]										=  {'old': f"""down.{i}.block""", 'new': f"""down_blocks.{i}.resnets"""}
    assign_to_checkpoint(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					additional_replacements=[meta_path] ,					config=__UpperCamelCase					)

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

    A_  :       Dict										=  renew_vae_resnet_paths(__UpperCamelCase					)
    A_  :       List[str]										=  {'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_  :       Optional[Any]										=  [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  A_  :       Tuple										=  renew_vae_attention_paths(__UpperCamelCase					)
  A_  :       Union[str, Any]										=  {'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_  :       int										=  num_up_blocks - 1 - i
    A_  :       Any										=  [
        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_  :       Union[str, Any]										=  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      A_  :       Dict										=  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

    A_  :       Dict										=  renew_vae_resnet_paths(__UpperCamelCase					)
    A_  :       Any										=  {'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_  :       str										=  [key for key in vae_state_dict if 'decoder.mid.block' in key]
  A_  :       Any										=  2
  for i in range(1 ,					num_mid_res_blocks + 1					):
    A_  :       Optional[Any]										=  [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

    A_  :       Optional[Any]										=  renew_vae_resnet_paths(__UpperCamelCase					)
    A_  :       Union[str, Any]										=  {'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_  :       Union[str, Any]										=  [key for key in vae_state_dict if 'decoder.mid.attn' in key]
  A_  :       Any										=  renew_vae_attention_paths(__UpperCamelCase					)
  A_  :       Optional[Any]										=  {'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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					):






  """simple docstring"""





  A_  :       int										=  requests.get(
      ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'					)
  A_  :       Any										=  io.BytesIO(r.content					)

  A_  :       Union[str, Any]										=  OmegaConf.load(__UpperCamelCase					)
  A_  :       Union[str, Any]										=  512
  A_  :       Optional[int]										=  'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('safetensors'					):
    from safetensors import safe_open

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

  # Convert the VAE model.
  A_  :       Union[str, Any]										=  create_vae_diffusers_config(__UpperCamelCase ,					image_size=__UpperCamelCase					)
  A_  :       Union[str, Any]										=  custom_convert_ldm_vae_checkpoint(__UpperCamelCase ,					__UpperCamelCase					)

  A_  :       List[Any]										=  AutoencoderKL(**__UpperCamelCase					)
  vae.load_state_dict(__UpperCamelCase					)
  vae.save_pretrained(__UpperCamelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						Optional[Any]						=					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_     :						List[Any]						=					parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
   # Create universe of discourse in Python using linspace ()
   __lowerCamelCase			: List[Any]							   =		np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

   # Create two fuzzy sets by defining any membership function
   # (trapmf(), gbellmf(), gaussmf(), etc).
   __lowerCamelCase			: int							   =		[0, 25, 50]
   __lowerCamelCase			: Tuple							   =		[25, 50, 75]
   __lowerCamelCase			: List[str]							   =		fuzz.membership.trimf(X, abca)
   __lowerCamelCase			: Tuple							   =		fuzz.membership.trimf(X, abca)

   # Compute the different operations using inbuilt functions.
   __lowerCamelCase			: List[str]							   =		np.ones(75)
   __lowerCamelCase			: Tuple							   =		np.zeros((75,))
   # 1. Union = max(µA(x), µB(x))
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, young, X, middle_aged)[1]
   # 2. Intersection = min(µA(x), µB(x))
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, young, X, middle_aged)[1]
   # 3. Complement (A) = (1- min(µA(x))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_not(young)
   # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
   # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
   __lowerCamelCase			: List[Any]							   =		young + middle_aged - (young * middle_aged)
   # 6. Algebraic Product = (µA(x) * µB(x))
   __lowerCamelCase			: int							   =		young * middle_aged
   # 7. Bounded Sum = min[1,(µA(x), µB(x))]
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
   # 8. Bounded difference = min[0,(µA(x), µB(x))]
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

   # max-min composition
   # max-product composition

   # Plot each set A, set B and each operation result using plot() and subplot().
   from matplotlib import pyplot as plt

   plt.figure()

   plt.subplot(4, 3, 1)
   plt.plot(X, young)
   plt.title('''Young''')
   plt.grid(True)

   plt.subplot(4, 3, 2)
   plt.plot(X, middle_aged)
   plt.title('''Middle aged''')
   plt.grid(True)

   plt.subplot(4, 3, 3)
   plt.plot(X, union)
   plt.title('''union''')
   plt.grid(True)

   plt.subplot(4, 3, 4)
   plt.plot(X, intersection)
   plt.title('''intersection''')
   plt.grid(True)

   plt.subplot(4, 3, 5)
   plt.plot(X, complement_a)
   plt.title('''complement_a''')
   plt.grid(True)

   plt.subplot(4, 3, 6)
   plt.plot(X, difference)
   plt.title('''difference a/b''')
   plt.grid(True)

   plt.subplot(4, 3, 7)
   plt.plot(X, alg_sum)
   plt.title('''alg_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 8)
   plt.plot(X, alg_product)
   plt.title('''alg_product''')
   plt.grid(True)

   plt.subplot(4, 3, 9)
   plt.plot(X, bdd_sum)
   plt.title('''bdd_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 10)
   plt.plot(X, bdd_difference)
   plt.title('''bdd_difference''')
   plt.grid(True)

   plt.subplots_adjust(hspace=0.5)
   plt.show()
