





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCamelCase			:	Dict					    =	{
    """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""",
}

__lowerCamelCase			:	Dict					    =	{
    """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""",
}

__lowerCamelCase			:	List[str]					    =	{
    """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""",
}

__lowerCamelCase			:	Tuple					    =	{
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__lowerCamelCase			:	int					    =	{
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__lowerCamelCase			:	Optional[Any]					    =	{
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}



def 						A_							(	_lowerCAmelCase	)		->					Any:
 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 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase=False	)		->					int:
 UpperCamelCase      :   Union[str, Any]			      =  checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
 UpperCamelCase      :   List[Any]			      =  checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
 UpperCamelCase      :   List[Any]			      =  checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
 UpperCamelCase      :   Optional[Any]			      =  checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
 UpperCamelCase      :   int			      =  checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
 UpperCamelCase      :   str			      =  checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
 UpperCamelCase      :   Optional[Any]			      =  checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
 UpperCamelCase      :   List[str]			      =  checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
 UpperCamelCase      :   Union[str, Any]			      =  checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
 UpperCamelCase      :   Optional[Any]			      =  checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

 if has_skip:
  UpperCamelCase      :   Optional[Any]			      =  checkpoint[F"""{old_prefix}.skip_connection.weight"""]
  UpperCamelCase      :   Union[str, Any]			      =  checkpoint[F"""{old_prefix}.skip_connection.bias"""]

 return new_checkpoint



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase=None	)		->					Optional[int]:
 UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   int			      =  checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3				,  dim=0	)
 UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Union[str, Any]			      =  checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3				,  dim=0	)

 UpperCamelCase      :   Optional[int]			      =  checkpoint[F"""{old_prefix}.norm.weight"""]
 UpperCamelCase      :   Any			      =  checkpoint[F"""{old_prefix}.norm.bias"""]

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

 UpperCamelCase      :   Dict			      =  (
     checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1	).squeeze(-1	)
 )
 UpperCamelCase      :   List[Any]			      =  checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1	).squeeze(-1	)

 return new_checkpoint



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Tuple:
 UpperCamelCase      :   List[Any]			      =  torch.load(_lowerCAmelCase				,  map_location="cpu"	)
 UpperCamelCase      :   Dict			      =  {}

 UpperCamelCase      :   Dict			      =  checkpoint["time_embed.0.weight"]
 UpperCamelCase      :   Optional[int]			      =  checkpoint["time_embed.0.bias"]
 UpperCamelCase      :   Dict			      =  checkpoint["time_embed.2.weight"]
 UpperCamelCase      :   Tuple			      =  checkpoint["time_embed.2.bias"]

 if unet_config["num_class_embeds"] is not None:
  UpperCamelCase      :   Optional[Any]			      =  checkpoint["label_emb.weight"]

 UpperCamelCase      :   int			      =  checkpoint["input_blocks.0.0.weight"]
 UpperCamelCase      :   Optional[Any]			      =  checkpoint["input_blocks.0.0.bias"]

 UpperCamelCase      :   Optional[Any]			      =  unet_config["down_block_types"]
 UpperCamelCase      :   Dict			      =  unet_config["layers_per_block"]
 UpperCamelCase      :   Optional[int]			      =  unet_config["attention_head_dim"]
 UpperCamelCase      :   Dict			      =  unet_config["block_out_channels"]
 UpperCamelCase      :   List[str]			      =  1
 UpperCamelCase      :   List[str]			      =  channels_list[0]

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

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

  if i != len(_lowerCAmelCase	) - 1:
   UpperCamelCase      :   Union[str, Any]			      =  F"""down_blocks.{i}.downsamplers.0"""
   UpperCamelCase      :   List[Any]			      =  F"""input_blocks.{current_layer}.0"""
   UpperCamelCase      :   Optional[int]			      =  convert_resnet(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)
   current_layer += 1

  UpperCamelCase      :   Tuple			      =  current_channels

 # hardcoded the mid-block for now
 UpperCamelCase      :   Dict			      =  "mid_block.resnets.0"
 UpperCamelCase      :   Union[str, Any]			      =  "middle_block.0"
 UpperCamelCase      :   List[Any]			      =  convert_resnet(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)
 UpperCamelCase      :   Any			      =  "mid_block.attentions.0"
 UpperCamelCase      :   Tuple			      =  "middle_block.1"
 UpperCamelCase      :   str			      =  convert_attention(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)
 UpperCamelCase      :   List[Any]			      =  "mid_block.resnets.1"
 UpperCamelCase      :   Optional[Any]			      =  "middle_block.2"
 UpperCamelCase      :   List[Any]			      =  convert_resnet(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)

 UpperCamelCase      :   int			      =  0
 UpperCamelCase      :   Optional[int]			      =  unet_config["up_block_types"]

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

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

   if i != len(_lowerCAmelCase	) - 1:
    UpperCamelCase      :   Union[str, Any]			      =  F"""up_blocks.{i}.upsamplers.0"""
    UpperCamelCase      :   Tuple			      =  F"""output_blocks.{current_layer-1}.2"""
    UpperCamelCase      :   int			      =  convert_resnet(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)

 UpperCamelCase      :   List[str]			      =  checkpoint["out.0.weight"]
 UpperCamelCase      :   int			      =  checkpoint["out.0.bias"]
 UpperCamelCase      :   Tuple			      =  checkpoint["out.2.weight"]
 UpperCamelCase      :   Tuple			      =  checkpoint["out.2.bias"]

 return new_checkpoint


if __name__ == "__main__":
    __lowerCamelCase			:	Dict					    =	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.""")

    __lowerCamelCase			:	Tuple					    =	parser.parse_args()
    __lowerCamelCase			:	Dict					    =	strabool(args.class_cond)

    __lowerCamelCase			:	Tuple					    =	os.path.basename(args.unet_path)
    print(f"""Checkpoint: {ckpt_name}""")

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

    if not args.class_cond:
        __lowerCamelCase			:	Any					    =	None

    __lowerCamelCase			:	Dict					    =	con_pt_to_diffuser(args.unet_path, unet_config)

    __lowerCamelCase			:	List[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:
        __lowerCamelCase			:	int					    =	CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        __lowerCamelCase			:	List[Any]					    =	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)):
        __lowerCamelCase			:	Tuple					    =	CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

    __lowerCamelCase			:	int					    =	CMStochasticIterativeScheduler(**scheduler_config)

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







from typing import Any



def 						A_							(	_lowerCAmelCase	)		->					list[Any]:
 if not input_list:
  return []
 UpperCamelCase      :   List[str]			      =  [input_list.count(_lowerCAmelCase	) for value in input_list]
 UpperCamelCase      :   Dict			      =  max(_lowerCAmelCase	)  # Gets the maximum count in the input list.
 # Gets values of modes
 return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase	) if value == y}	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
