
"""simple docstring"""



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

UpperCAmelCase__          = [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

UpperCAmelCase__          = [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

UpperCAmelCase__          = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  UpperCAmelCase__          = F"down_blocks.{i}.resnets.{j}."
  UpperCAmelCase__          = F"input_blocks.{3*i + j + 1}.0."
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   UpperCAmelCase__          = F"down_blocks.{i}.attentions.{j}."
   UpperCAmelCase__          = F"input_blocks.{3*i + j + 1}.1."
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  UpperCAmelCase__          = F"up_blocks.{i}.resnets.{j}."
  UpperCAmelCase__          = F"output_blocks.{3*i + j}.0."
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   UpperCAmelCase__          = F"up_blocks.{i}.attentions.{j}."
   UpperCAmelCase__          = F"output_blocks.{3*i + j}.1."
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  UpperCAmelCase__          = F"down_blocks.{i}.downsamplers.0.conv."
  UpperCAmelCase__          = F"input_blocks.{3*(i+1)}.0.op."
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  UpperCAmelCase__          = F"up_blocks.{i}.upsamplers.0."
  UpperCAmelCase__          = F"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCAmelCase__          = 'mid_block.attentions.0.'
UpperCAmelCase__          = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 UpperCAmelCase__          = F"mid_block.resnets.{j}."
 UpperCAmelCase__          = F"middle_block.{2*j}."
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					List[Any]:
  # buyer beware: this is a *brittle* function,
  # and correct output requires that all of these pieces interact in
  # the exact order in which I have arranged them.
  _snake_case					     =       {k: k for k in unet_state_dict.keys()}
  for sd_name, hf_name in unet_conversion_map:
    _snake_case					     =       sd_name
  for k, v in mapping.items():
    if "resnets" in k:
      for sd_part, hf_part in unet_conversion_map_resnet:
        _snake_case					     =       v.replace(__lowerCamelCase ,					__lowerCamelCase		)
      _snake_case					     =       v
  for k, v in mapping.items():
    for sd_part, hf_part in unet_conversion_map_layer:
      _snake_case					     =       v.replace(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       v
  _snake_case					     =       {v: unet_state_dict[k] for k, v in mapping.items()}
  return new_state_dict


# ================#
# VAE Conversion #
# ================#

UpperCAmelCase__          = [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  UpperCAmelCase__          = F"encoder.down_blocks.{i}.resnets.{j}."
  UpperCAmelCase__          = F"encoder.down.{i}.block.{j}."
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  UpperCAmelCase__          = F"down_blocks.{i}.downsamplers.0."
  UpperCAmelCase__          = F"down.{i}.downsample."
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  UpperCAmelCase__          = F"up_blocks.{i}.upsamplers.0."
  UpperCAmelCase__          = F"up.{3-i}.upsample."
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  UpperCAmelCase__          = F"decoder.up_blocks.{i}.resnets.{j}."
  UpperCAmelCase__          = F"decoder.up.{3-i}.block.{j}."
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 UpperCAmelCase__          = F"mid_block.resnets.{i}."
 UpperCAmelCase__          = F"mid.block_{i+1}."
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCAmelCase__          = [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					Optional[int]:
  # convert HF linear weights to SD conv2d weights
  return w.reshape(*w.shape ,					1 ,					1		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str]		)     ->					str:
  _snake_case					     =       {k: k for k in vae_state_dict.keys()}
  for k, v in mapping.items():
    for sd_part, hf_part in vae_conversion_map:
      _snake_case					     =       v.replace(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        _snake_case					     =       v.replace(__lowerCamelCase ,					__lowerCamelCase		)
      _snake_case					     =       v
  _snake_case					     =       {v: vae_state_dict[k] for k, v in mapping.items()}
  _snake_case					     =       ['''q''', '''k''', '''v''', '''proj_out''']
  for k, v in new_state_dict.items():
    for weight_name in weights_to_convert:
      if f'''mid.attn_1.{weight_name}.weight''' in k:
        print(f'''Reshaping {k} for SD format'''		)
        _snake_case					     =       reshape_weight_for_sd(__lowerCamelCase		)
  return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


UpperCAmelCase__          = [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
UpperCAmelCase__          = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCAmelCase__          = re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCAmelCase__          = {'q': 0, 'k': 1, 'v': 2}
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					List[Any]:
  _snake_case					     =       {}
  _snake_case					     =       {}
  _snake_case					     =       {}
  for k, v in text_enc_dict.items():
    if (
        k.endswith('''.self_attn.q_proj.weight'''		)
        or k.endswith('''.self_attn.k_proj.weight'''		)
        or k.endswith('''.self_attn.v_proj.weight'''		)
    ):
      _snake_case					     =       k[: -len('''.q_proj.weight'''		)]
      _snake_case					     =       k[-len('''q_proj.weight'''		)]
      if k_pre not in capture_qkv_weight:
        _snake_case					     =       [None, None, None]
      _snake_case					     =       v
      continue

    if (
        k.endswith('''.self_attn.q_proj.bias'''		)
        or k.endswith('''.self_attn.k_proj.bias'''		)
        or k.endswith('''.self_attn.v_proj.bias'''		)
    ):
      _snake_case					     =       k[: -len('''.q_proj.bias'''		)]
      _snake_case					     =       k[-len('''q_proj.bias'''		)]
      if k_pre not in capture_qkv_bias:
        _snake_case					     =       [None, None, None]
      _snake_case					     =       v
      continue

    _snake_case					     =       textenc_pattern.sub(lambda __lowerCamelCase		: protected[re.escape(m.group(0		)		)] ,					__lowerCamelCase		)
    _snake_case					     =       v

  for k_pre, tensors in capture_qkv_weight.items():
    if None in tensors:
      raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''		)
    _snake_case					     =       textenc_pattern.sub(lambda __lowerCamelCase		: protected[re.escape(m.group(0		)		)] ,					__lowerCamelCase		)
    _snake_case					     =       torch.cat(__lowerCamelCase		)

  for k_pre, tensors in capture_qkv_bias.items():
    if None in tensors:
      raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''		)
    _snake_case					     =       textenc_pattern.sub(lambda __lowerCamelCase		: protected[re.escape(m.group(0		)		)] ,					__lowerCamelCase		)
    _snake_case					     =       torch.cat(__lowerCamelCase		)

  return new_state_dict
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int		)     ->					str:
  return text_enc_dict


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

 parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
 parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
 parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
 parser.add_argument(
     '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
 )

 UpperCAmelCase__          = parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 UpperCAmelCase__          = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
 UpperCAmelCase__          = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
 UpperCAmelCase__          = osp.join(args.model_path, 'text_encoder', 'model.safetensors')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  UpperCAmelCase__          = load_file(unet_path, device='cpu')
 else:
  UpperCAmelCase__          = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
  UpperCAmelCase__          = torch.load(unet_path, map_location='cpu')

 if osp.exists(vae_path):
  UpperCAmelCase__          = load_file(vae_path, device='cpu')
 else:
  UpperCAmelCase__          = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
  UpperCAmelCase__          = torch.load(vae_path, map_location='cpu')

 if osp.exists(text_enc_path):
  UpperCAmelCase__          = load_file(text_enc_path, device='cpu')
 else:
  UpperCAmelCase__          = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
  UpperCAmelCase__          = torch.load(text_enc_path, map_location='cpu')

 # Convert the UNet model
 UpperCAmelCase__          = convert_unet_state_dict(unet_state_dict)
 UpperCAmelCase__          = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 UpperCAmelCase__          = convert_vae_state_dict(vae_state_dict)
 UpperCAmelCase__          = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 UpperCAmelCase__          = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  UpperCAmelCase__          = {'transformer.' + k: v for k, v in text_enc_dict.items()}
  UpperCAmelCase__          = convert_text_enc_state_dict_vaa(text_enc_dict)
  UpperCAmelCase__          = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
 else:
  UpperCAmelCase__          = convert_text_enc_state_dict(text_enc_dict)
  UpperCAmelCase__          = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 UpperCAmelCase__          = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  UpperCAmelCase__          = {k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  UpperCAmelCase__          = {'state_dict': state_dict}
  torch.save(state_dict, args.checkpoint_path)





"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Any:
  stooge(__lowerCamelCase ,					0 ,					len(__lowerCamelCase		) - 1		)
  return arr
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case     ,					_snake_case					     =       arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case					     =       (int)((h - i + 1) / 3		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)

    # Recursively sort last 2/3 elements
    stooge(__lowerCamelCase ,					i + t ,					(__lowerCamelCase)		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)


if __name__ == "__main__":
 UpperCAmelCase__          = input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase__          = [int(item) for item in user_input.split(',')]
 print(stooge_sort(unsorted))



