
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase__       ={
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

lowercase__       ={
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

lowercase__       ={
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

lowercase__       ={
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

lowercase__       ={
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

lowercase__       ={
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 if isinstance(lowerCAmelCase__  ,   lowerCAmelCase__  ):
  return v
 if v.lower() in ("yes", "true", "t", "y", "1"):
  return True
 elif v.lower() in ("no", "false", "f", "n", "0"):
  return False
 else:
  raise argparse.ArgumentTypeError('''boolean value expected'''  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Dict=False  ):
 __a :  str						=  checkpoint[f"{old_prefix}.in_layers.0.weight"]
 __a :  Optional[Any]						=  checkpoint[f"{old_prefix}.in_layers.0.bias"]
 __a :  Any						=  checkpoint[f"{old_prefix}.in_layers.2.weight"]
 __a :  List[Any]						=  checkpoint[f"{old_prefix}.in_layers.2.bias"]
 __a :  List[str]						=  checkpoint[f"{old_prefix}.emb_layers.1.weight"]
 __a :  Any						=  checkpoint[f"{old_prefix}.emb_layers.1.bias"]
 __a :  Optional[int]						=  checkpoint[f"{old_prefix}.out_layers.0.weight"]
 __a :  Union[str, Any]						=  checkpoint[f"{old_prefix}.out_layers.0.bias"]
 __a :  Dict						=  checkpoint[f"{old_prefix}.out_layers.3.weight"]
 __a :  Union[str, Any]						=  checkpoint[f"{old_prefix}.out_layers.3.bias"]

 if has_skip:
  __a :  Tuple						=  checkpoint[f"{old_prefix}.skip_connection.weight"]
  __a :  Union[str, Any]						=  checkpoint[f"{old_prefix}.skip_connection.bias"]

 return new_checkpoint




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int=None  ):
 __a				,       __a				,       __a :  str						=  checkpoint[f"{old_prefix}.qkv.weight"].chunk(3  ,   dim=0  )
 __a				,       __a				,       __a :  Any						=  checkpoint[f"{old_prefix}.qkv.bias"].chunk(3  ,   dim=0  )

 __a :  List[Any]						=  checkpoint[f"{old_prefix}.norm.weight"]
 __a :  List[str]						=  checkpoint[f"{old_prefix}.norm.bias"]

 __a :  Union[str, Any]						=  weight_q.squeeze(-1  ).squeeze(-1  )
 __a :  Any						=  bias_q.squeeze(-1  ).squeeze(-1  )
 __a :  int						=  weight_k.squeeze(-1  ).squeeze(-1  )
 __a :  Any						=  bias_k.squeeze(-1  ).squeeze(-1  )
 __a :  int						=  weight_v.squeeze(-1  ).squeeze(-1  )
 __a :  Optional[Any]						=  bias_v.squeeze(-1  ).squeeze(-1  )

 __a :  Optional[int]						=  (
     checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1  ).squeeze(-1  )
 )
 __a :  Optional[Any]						=  checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1  ).squeeze(-1  )

 return new_checkpoint




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  torch.load(lowerCAmelCase__  ,   map_location='''cpu'''  )
 __a :  Tuple						=  {}

 __a :  Union[str, Any]						=  checkpoint['''time_embed.0.weight''']
 __a :  Union[str, Any]						=  checkpoint['''time_embed.0.bias''']
 __a :  List[Any]						=  checkpoint['''time_embed.2.weight''']
 __a :  Any						=  checkpoint['''time_embed.2.bias''']

 if unet_config["num_class_embeds"] is not None:
  __a :  List[str]						=  checkpoint['''label_emb.weight''']

 __a :  Any						=  checkpoint['''input_blocks.0.0.weight''']
 __a :  int						=  checkpoint['''input_blocks.0.0.bias''']

 __a :  Tuple						=  unet_config['''down_block_types''']
 __a :  List[Any]						=  unet_config['''layers_per_block''']
 __a :  Union[str, Any]						=  unet_config['''attention_head_dim''']
 __a :  Tuple						=  unet_config['''block_out_channels''']
 __a :  List[Any]						=  1
 __a :  Any						=  channels_list[0]

 for i, layer_type in enumerate(lowerCAmelCase__  ):
  __a :  List[str]						=  channels_list[i]
  __a :  Optional[Any]						=  current_channels != prev_channels
  if layer_type == "ResnetDownsampleBlock2D":
   for j in range(lowerCAmelCase__  ):
    __a :  List[Any]						=  f"down_blocks.{i}.resnets.{j}"
    __a :  Dict						=  f"input_blocks.{current_layer}.0"
    __a :  List[str]						=  True if j == 0 and downsample_block_has_skip else False
    __a :  Optional[Any]						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   has_skip=lowerCAmelCase__  )
    current_layer += 1

  elif layer_type == "AttnDownBlock2D":
   for j in range(lowerCAmelCase__  ):
    __a :  int						=  f"down_blocks.{i}.resnets.{j}"
    __a :  Tuple						=  f"input_blocks.{current_layer}.0"
    __a :  Optional[int]						=  True if j == 0 and downsample_block_has_skip else False
    __a :  List[Any]						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   has_skip=lowerCAmelCase__  )
    __a :  int						=  f"down_blocks.{i}.attentions.{j}"
    __a :  int						=  f"input_blocks.{current_layer}.1"
    __a :  Union[str, Any]						=  convert_attention(
        lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
    current_layer += 1

  if i != len(lowerCAmelCase__  ) - 1:
   __a :  List[str]						=  f"down_blocks.{i}.downsamplers.0"
   __a :  List[str]						=  f"input_blocks.{current_layer}.0"
   __a :  Any						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
   current_layer += 1

  __a :  List[Any]						=  current_channels

 # hardcoded the mid-block for now
 __a :  Tuple						=  '''mid_block.resnets.0'''
 __a :  Tuple						=  '''middle_block.0'''
 __a :  Optional[Any]						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
 __a :  str						=  '''mid_block.attentions.0'''
 __a :  str						=  '''middle_block.1'''
 __a :  List[str]						=  convert_attention(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
 __a :  int						=  '''mid_block.resnets.1'''
 __a :  int						=  '''middle_block.2'''
 __a :  Union[str, Any]						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

 __a :  int						=  0
 __a :  Optional[Any]						=  unet_config['''up_block_types''']

 for i, layer_type in enumerate(lowerCAmelCase__  ):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1  ):
    __a :  Any						=  f"up_blocks.{i}.resnets.{j}"
    __a :  Optional[int]						=  f"output_blocks.{current_layer}.0"
    __a :  Tuple						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   has_skip=lowerCAmelCase__  )
    current_layer += 1

   if i != len(lowerCAmelCase__  ) - 1:
    __a :  List[Any]						=  f"up_blocks.{i}.upsamplers.0"
    __a :  Optional[Any]						=  f"output_blocks.{current_layer-1}.1"
    __a :  Tuple						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
  elif layer_type == "AttnUpBlock2D":
   for j in range(layers_per_block + 1  ):
    __a :  List[str]						=  f"up_blocks.{i}.resnets.{j}"
    __a :  Dict						=  f"output_blocks.{current_layer}.0"
    __a :  Tuple						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   has_skip=lowerCAmelCase__  )
    __a :  Optional[Any]						=  f"up_blocks.{i}.attentions.{j}"
    __a :  Union[str, Any]						=  f"output_blocks.{current_layer}.1"
    __a :  Dict						=  convert_attention(
        lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
    current_layer += 1

   if i != len(lowerCAmelCase__  ) - 1:
    __a :  Optional[int]						=  f"up_blocks.{i}.upsamplers.0"
    __a :  int						=  f"output_blocks.{current_layer-1}.2"
    __a :  Union[str, Any]						=  convert_resnet(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

 __a :  Any						=  checkpoint['''out.0.weight''']
 __a :  str						=  checkpoint['''out.0.bias''']
 __a :  Any						=  checkpoint['''out.2.weight''']
 __a :  Optional[int]						=  checkpoint['''out.2.bias''']

 return new_checkpoint


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

      parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
      parser.add_argument(
          '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
      )
      parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

      lowercase__       =parser.parse_args()
      lowercase__       =strabool(args.class_cond)

      lowercase__       =os.path.basename(args.unet_path)
      print(F"""Checkpoint: {ckpt_name}""")

      # Get U-Net config
      if "imagenet64" in ckpt_name:
            lowercase__       =IMAGENET_64_UNET_CONFIG
      elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
            lowercase__       =LSUN_256_UNET_CONFIG
      elif "test" in ckpt_name:
            lowercase__       =TEST_UNET_CONFIG
      else:
            raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

      if not args.class_cond:
            lowercase__       =None

      lowercase__       =con_pt_to_diffuser(args.unet_path, unet_config)

      lowercase__       =UNetaDModel(**unet_config)
      image_unet.load_state_dict(converted_unet_ckpt)

      # Get scheduler config
      if "cd" in ckpt_name or "test" in ckpt_name:
            lowercase__       =CD_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
            lowercase__       =CT_IMAGENET_64_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
            lowercase__       =CT_LSUN_256_SCHEDULER_CONFIG
      else:
            raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

      lowercase__       =CMStochasticIterativeScheduler(**scheduler_config)

      lowercase__       =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
      consistency_model.save_pretrained(args.dump_path)


from __future__ import annotations

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  0

    __a :  list[Any]						=  [-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes   ):
     self.m_component.update({node: node}   )
     component_size.append(1   )

    __a :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[Any]						=  self.m_component[v]

      if u_component != v_component:

       for component in (u_component, v_component):
        if (
            minimum_weight_edge[component] == -1
            or minimum_weight_edge[component][2] > w
        ):
         __a :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  self.m_component[v]

       if u_component != v_component:
        mst_weight += w
        self.union(snake_case_					,				snake_case_					,				snake_case_   )
        print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"   )
        num_of_components -= 1

     __a :  Optional[int]						=  [-1] * self.m_num_of_nodes
    print(f"The total weight of the minimal spanning tree is: {mst_weight}"   )





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
