
"""simple docstring"""

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase     :    int       =			{
    "sample_size": 3_2,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [3_2, 6_4],
    "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",
}

UpperCamelCase     :    Any       =			{
    "sample_size": 6_4,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    "attention_head_dim": 6_4,
    "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",
}

UpperCamelCase     :    Optional[int]       =			{
    "sample_size": 2_5_6,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    "attention_head_dim": 6_4,
    "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",
}

UpperCamelCase     :    str       =			{
    "num_train_timesteps": 4_0,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCamelCase     :    Any       =			{
    "num_train_timesteps": 2_0_1,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCamelCase     :    str       =			{
    "num_train_timesteps": 1_5_1,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}






def 							A						(    snake_case				:str		)				->     Tuple:
 if isinstance(snake_case    , snake_case		):
  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 							A						(    snake_case				:int    , snake_case				:Optional[int]    , snake_case				:Union[str, Any]    , snake_case				:Union[str, Any]    , snake_case				:Optional[int]=False		)				->     List[Any]:
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.in_layers.0.weight']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.in_layers.0.bias']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.in_layers.2.weight']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.in_layers.2.bias']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.emb_layers.1.weight']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.emb_layers.1.bias']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.out_layers.0.weight']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.out_layers.0.bias']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.out_layers.3.weight']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.out_layers.3.bias']

 if has_skip:
  __UpperCamelCase      =	checkpoint[f'{old_prefix}.skip_connection.weight']
  __UpperCamelCase      =	checkpoint[f'{old_prefix}.skip_connection.bias']

 return new_checkpoint






def 							A						(    snake_case				:str    , snake_case				:int    , snake_case				:List[str]    , snake_case				:Tuple    , snake_case				:str=None		)				->     Tuple:
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	checkpoint[f'{old_prefix}.qkv.weight'].chunk(3    , dim=0		)
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	checkpoint[f'{old_prefix}.qkv.bias'].chunk(3    , dim=0		)

 __UpperCamelCase      =	checkpoint[f'{old_prefix}.norm.weight']
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.norm.bias']

 __UpperCamelCase      =	weight_q.squeeze(-1		).squeeze(-1		)
 __UpperCamelCase      =	bias_q.squeeze(-1		).squeeze(-1		)
 __UpperCamelCase      =	weight_k.squeeze(-1		).squeeze(-1		)
 __UpperCamelCase      =	bias_k.squeeze(-1		).squeeze(-1		)
 __UpperCamelCase      =	weight_v.squeeze(-1		).squeeze(-1		)
 __UpperCamelCase      =	bias_v.squeeze(-1		).squeeze(-1		)

 __UpperCamelCase      =	(
     checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1		).squeeze(-1		)
 )
 __UpperCamelCase      =	checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1		).squeeze(-1		)

 return new_checkpoint






def 							A						(    snake_case				:str    , snake_case				:Union[str, Any]		)				->     str:
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)
 __UpperCamelCase      =	{}

 __UpperCamelCase      =	checkpoint['time_embed.0.weight']
 __UpperCamelCase      =	checkpoint['time_embed.0.bias']
 __UpperCamelCase      =	checkpoint['time_embed.2.weight']
 __UpperCamelCase      =	checkpoint['time_embed.2.bias']

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

 __UpperCamelCase      =	checkpoint['input_blocks.0.0.weight']
 __UpperCamelCase      =	checkpoint['input_blocks.0.0.bias']

 __UpperCamelCase      =	unet_config['down_block_types']
 __UpperCamelCase      =	unet_config['layers_per_block']
 __UpperCamelCase      =	unet_config['attention_head_dim']
 __UpperCamelCase      =	unet_config['block_out_channels']
 __UpperCamelCase      =	1
 __UpperCamelCase      =	channels_list[0]

 for i, layer_type in enumerate(snake_case		):
  __UpperCamelCase      =	channels_list[i]
  __UpperCamelCase      =	current_channels != prev_channels
  if layer_type == "ResnetDownsampleBlock2D":
   for j in range(snake_case		):
    __UpperCamelCase      =	f'down_blocks.{i}.resnets.{j}'
    __UpperCamelCase      =	f'input_blocks.{current_layer}.0'
    __UpperCamelCase      =	True if j == 0 and downsample_block_has_skip else False
    __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case    , has_skip=snake_case		)
    current_layer += 1

  elif layer_type == "AttnDownBlock2D":
   for j in range(snake_case		):
    __UpperCamelCase      =	f'down_blocks.{i}.resnets.{j}'
    __UpperCamelCase      =	f'input_blocks.{current_layer}.0'
    __UpperCamelCase      =	True if j == 0 and downsample_block_has_skip else False
    __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case    , has_skip=snake_case		)
    __UpperCamelCase      =	f'down_blocks.{i}.attentions.{j}'
    __UpperCamelCase      =	f'input_blocks.{current_layer}.1'
    __UpperCamelCase      =	convert_attention(
        snake_case    , snake_case    , snake_case    , snake_case    , snake_case		)
    current_layer += 1

  if i != len(snake_case		) - 1:
   __UpperCamelCase      =	f'down_blocks.{i}.downsamplers.0'
   __UpperCamelCase      =	f'input_blocks.{current_layer}.0'
   __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case		)
   current_layer += 1

  __UpperCamelCase      =	current_channels

 # hardcoded the mid-block for now
 __UpperCamelCase      =	'mid_block.resnets.0'
 __UpperCamelCase      =	'middle_block.0'
 __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case		)
 __UpperCamelCase      =	'mid_block.attentions.0'
 __UpperCamelCase      =	'middle_block.1'
 __UpperCamelCase      =	convert_attention(snake_case    , snake_case    , snake_case    , snake_case    , snake_case		)
 __UpperCamelCase      =	'mid_block.resnets.1'
 __UpperCamelCase      =	'middle_block.2'
 __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case		)

 __UpperCamelCase      =	0
 __UpperCamelCase      =	unet_config['up_block_types']

 for i, layer_type in enumerate(snake_case		):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1		):
    __UpperCamelCase      =	f'up_blocks.{i}.resnets.{j}'
    __UpperCamelCase      =	f'output_blocks.{current_layer}.0'
    __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case    , has_skip=snake_case		)
    current_layer += 1

   if i != len(snake_case		) - 1:
    __UpperCamelCase      =	f'up_blocks.{i}.upsamplers.0'
    __UpperCamelCase      =	f'output_blocks.{current_layer-1}.1'
    __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case		)
  elif layer_type == "AttnUpBlock2D":
   for j in range(layers_per_block + 1		):
    __UpperCamelCase      =	f'up_blocks.{i}.resnets.{j}'
    __UpperCamelCase      =	f'output_blocks.{current_layer}.0'
    __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case    , has_skip=snake_case		)
    __UpperCamelCase      =	f'up_blocks.{i}.attentions.{j}'
    __UpperCamelCase      =	f'output_blocks.{current_layer}.1'
    __UpperCamelCase      =	convert_attention(
        snake_case    , snake_case    , snake_case    , snake_case    , snake_case		)
    current_layer += 1

   if i != len(snake_case		) - 1:
    __UpperCamelCase      =	f'up_blocks.{i}.upsamplers.0'
    __UpperCamelCase      =	f'output_blocks.{current_layer-1}.2'
    __UpperCamelCase      =	convert_resnet(snake_case    , snake_case    , snake_case    , snake_case		)

 __UpperCamelCase      =	checkpoint['out.0.weight']
 __UpperCamelCase      =	checkpoint['out.0.bias']
 __UpperCamelCase      =	checkpoint['out.2.weight']
 __UpperCamelCase      =	checkpoint['out.2.bias']

 return new_checkpoint


if __name__ == "__main__":
      UpperCamelCase     :    Optional[int]       =			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.")

      UpperCamelCase     :    Any       =			parser.parse_args()
      UpperCamelCase     :    int       =			strabool(args.class_cond)

      UpperCamelCase     :    Any       =			os.path.basename(args.unet_path)
      print(f'''Checkpoint: {ckpt_name}''')

      # Get U-Net config
      if "imagenet64" in ckpt_name:
            UpperCamelCase     :    int       =			IMAGENET_64_UNET_CONFIG
      elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
            UpperCamelCase     :    Optional[int]       =			LSUN_256_UNET_CONFIG
      elif "test" in ckpt_name:
            UpperCamelCase     :    Tuple       =			TEST_UNET_CONFIG
      else:
            raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

      if not args.class_cond:
            UpperCamelCase     :    str       =			None

      UpperCamelCase     :    Tuple       =			con_pt_to_diffuser(args.unet_path, unet_config)

      UpperCamelCase     :    Union[str, Any]       =			UNetaDModel(**unet_config)
      image_unet.load_state_dict(converted_unet_ckpt)

      # Get scheduler config
      if "cd" in ckpt_name or "test" in ckpt_name:
            UpperCamelCase     :    Any       =			CD_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
            UpperCamelCase     :    Optional[int]       =			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)):
            UpperCamelCase     :    Optional[int]       =			CT_LSUN_256_SCHEDULER_CONFIG
      else:
            raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

      UpperCamelCase     :    Any       =			CMStochasticIterativeScheduler(**scheduler_config)

      UpperCamelCase     :    int       =			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
      consistency_model.save_pretrained(args.dump_path)





"""simple docstring"""

import heapq as hq
import math
from collections.abc import Iterator

class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	str(id_				)
      __UpperCamelCase      =	None
      __UpperCamelCase      =	None
      __UpperCamelCase      =	[]
      __UpperCamelCase      =	{}  # {vertex:distance}





     def __lt__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return self.key < other.key





     def __repr__(     self				):

      '''simple docstring'''




      return self.id





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      self.neighbors.append(__UpperCAmelCase				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	weight






def 							A						(    snake_case				:List[Any]    , snake_case				:Dict    , snake_case				:Any    , snake_case				:str		)				->     List[str]:
 # add the neighbors:
 graph[a - 1].add_neighbor(graph[b - 1]		)
 graph[b - 1].add_neighbor(graph[a - 1]		)
 # add the edges:
 graph[a - 1].add_edge(graph[b - 1]    , snake_case		)
 graph[b - 1].add_edge(graph[a - 1]    , snake_case		)






def 							A						(    snake_case				:list    , snake_case				:Vertex		)				->     list:
 __UpperCamelCase      =	[]
 for u in graph:
  __UpperCamelCase      =	math.inf
  __UpperCamelCase      =	None
 __UpperCamelCase      =	0
 __UpperCamelCase      =	graph[:]
 while q:
  __UpperCamelCase      =	min(snake_case		)
  q.remove(snake_case		)
  for v in u.neighbors:
   if (v in q) and (u.edges[v.id] < v.key):
    __UpperCamelCase      =	u
    __UpperCamelCase      =	u.edges[v.id]
 for i in range(1    , len(snake_case		)		):
  a.append((int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)		)
 return a






def 							A						(    snake_case				:list    , snake_case				:Vertex		)				->     Iterator[tuple]:
 for u in graph:
  __UpperCamelCase      =	math.inf
  __UpperCamelCase      =	None
 __UpperCamelCase      =	0

 __UpperCamelCase      =	list(snake_case		)
 hq.heapify(snake_case		)

 while h:
  __UpperCamelCase      =	hq.heappop(snake_case		)
  for v in u.neighbors:
   if (v in h) and (u.edges[v.id] < v.key):
    __UpperCamelCase      =	u
    __UpperCamelCase      =	u.edges[v.id]
    hq.heapify(snake_case		)

 for i in range(1    , len(snake_case		)		):
  yield (int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)






def 							A						(    )				->     None:
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()



