




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCamelCase				:		List[Any]     =   {
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "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				:		str     =   {
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "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				:		Union[str, Any]     =   {
    "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				:		str     =   {
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowerCamelCase				:		Union[str, Any]     =   {
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowerCamelCase				:		Any     =   {
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict     ):


      '''simple docstring'''



      if isinstance(lowercase	,							lowercase     ):
            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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]	,							lowercase	:       int	,							lowercase	:       List[str]=False     ):


      '''simple docstring'''



      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

      if has_skip:
            lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.skip_connection.weight"""]
            lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.skip_connection.bias"""]

      return new_checkpoint

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]	,							lowercase	:       Any	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]	,							lowercase	:       Dict=None     ):


      '''simple docstring'''



      lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3	,							dim=0     )
      lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3	,							dim=0     )

      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.norm.weight"""]
      lowerCamelCase_		     =    checkpoint[f"""{old_prefix}.norm.bias"""]

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

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

      return new_checkpoint

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str	,							lowercase	:       int     ):


      '''simple docstring'''



      lowerCamelCase_		     =    torch.load(lowercase	,							map_location='cpu'     )
      lowerCamelCase_		     =    {}

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

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

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

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

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

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

            if i != len(lowercase     ) - 1:
                  lowerCamelCase_		     =    f"""down_blocks.{i}.downsamplers.0"""
                  lowerCamelCase_		     =    f"""input_blocks.{current_layer}.0"""
                  lowerCamelCase_		     =    convert_resnet(lowercase	,							lowercase	,							lowercase	,							lowercase     )
                  current_layer += 1

            lowerCamelCase_		     =    current_channels

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

      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    unet_config['up_block_types']

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

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

                  if i != len(lowercase     ) - 1:
                        lowerCamelCase_		     =    f"""up_blocks.{i}.upsamplers.0"""
                        lowerCamelCase_		     =    f"""output_blocks.{current_layer-1}.2"""
                        lowerCamelCase_		     =    convert_resnet(lowercase	,							lowercase	,							lowercase	,							lowercase     )

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

      return new_checkpoint


if __name__ == "__main__":
 lowerCamelCase				:		str     =   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				:		List[str]     =   parser.parse_args()
 lowerCamelCase				:		Dict     =   strabool(args.class_cond)

 lowerCamelCase				:		Optional[Any]     =   os.path.basename(args.unet_path)
 print(F"""Checkpoint: {ckpt_name}""")

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

 if not args.class_cond:
  lowerCamelCase				:		List[Any]     =   None

 lowerCamelCase				:		Optional[Any]     =   con_pt_to_diffuser(args.unet_path, unet_config)

 lowerCamelCase				:		str     =   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				:		str     =   CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  lowerCamelCase				:		Optional[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				:		Union[str, Any]     =   CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 lowerCamelCase				:		Optional[int]     =   CMStochasticIterativeScheduler(**scheduler_config)

 lowerCamelCase				:		Any     =   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       float	,							lowercase	:       list[float]     ):


      '''simple docstring'''



      if discount_rate < 0:
            raise ValueError('Discount rate cannot be negative'     )
      if not cash_flows:
            raise ValueError('Cash flows list cannot be empty'     )
      lowerCamelCase_		     =    sum(
          cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(lowercase     )     )
      return round(lowercase	,							ndigits=2     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



