
"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


A__      :	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""",
}

A__      :	List[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""",
}

A__      :	Optional[int]          =    {
    """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""",
}

A__      :	Dict          =    {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

A__      :	Dict          =    {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

A__      :	Optional[int]          =    {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}



def 							a__ (				lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     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	:       Union[str, Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Tuple=False		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						checkpoint[F"{old_prefix}.in_layers.0.weight"]
     UpperCAmelCase__							: Optional[Any] 							=						checkpoint[F"{old_prefix}.in_layers.0.bias"]
     UpperCAmelCase__							: str 							=						checkpoint[F"{old_prefix}.in_layers.2.weight"]
     UpperCAmelCase__							: int 							=						checkpoint[F"{old_prefix}.in_layers.2.bias"]
     UpperCAmelCase__							: Optional[int] 							=						checkpoint[F"{old_prefix}.emb_layers.1.weight"]
     UpperCAmelCase__							: List[str] 							=						checkpoint[F"{old_prefix}.emb_layers.1.bias"]
     UpperCAmelCase__							: Dict 							=						checkpoint[F"{old_prefix}.out_layers.0.weight"]
     UpperCAmelCase__							: Tuple 							=						checkpoint[F"{old_prefix}.out_layers.0.bias"]
     UpperCAmelCase__							: str 							=						checkpoint[F"{old_prefix}.out_layers.3.weight"]
     UpperCAmelCase__							: Optional[Any] 							=						checkpoint[F"{old_prefix}.out_layers.3.bias"]

     if has_skip:
          UpperCAmelCase__							: int 							=						checkpoint[F"{old_prefix}.skip_connection.weight"]
          UpperCAmelCase__							: str 							=						checkpoint[F"{old_prefix}.skip_connection.bias"]

     return new_checkpoint



def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[int]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						checkpoint[F"{old_prefix}.qkv.weight"].chunk(3       ,    dim=0		)
     UpperCAmelCase__							: Optional[int] 							=						checkpoint[F"{old_prefix}.qkv.bias"].chunk(3       ,    dim=0		)

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

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

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

     return new_checkpoint



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: List[str] 							=						torch.load(lowerCAmelCase       ,    map_location="cpu"		)
     UpperCAmelCase__							: Optional[Any] 							=						{}

     UpperCAmelCase__							: Optional[int] 							=						checkpoint["time_embed.0.weight"]
     UpperCAmelCase__							: str 							=						checkpoint["time_embed.0.bias"]
     UpperCAmelCase__							: Optional[Any] 							=						checkpoint["time_embed.2.weight"]
     UpperCAmelCase__							: int 							=						checkpoint["time_embed.2.bias"]

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

     UpperCAmelCase__							: List[str] 							=						checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase__							: int 							=						checkpoint["input_blocks.0.0.bias"]

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

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

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

          if i != len(lowerCAmelCase		) - 1:
               UpperCAmelCase__							: Dict 							=						F"down_blocks.{i}.downsamplers.0"
               UpperCAmelCase__							: Any 							=						F"input_blocks.{current_layer}.0"
               UpperCAmelCase__							: str 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
               current_layer += 1

          UpperCAmelCase__							: Union[str, Any] 							=						current_channels

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

     UpperCAmelCase__							: str 							=						0
     UpperCAmelCase__							: Tuple 							=						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__							: 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__							: str 							=						F"up_blocks.{i}.upsamplers.0"
                    UpperCAmelCase__							: Any 							=						F"output_blocks.{current_layer-1}.1"
                    UpperCAmelCase__							: Optional[Any] 							=						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__							: Tuple 							=						F"output_blocks.{current_layer}.0"
                    UpperCAmelCase__							: Optional[Any] 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    has_skip=lowerCAmelCase		)
                    UpperCAmelCase__							: List[str] 							=						F"up_blocks.{i}.attentions.{j}"
                    UpperCAmelCase__							: Dict 							=						F"output_blocks.{current_layer}.1"
                    UpperCAmelCase__							: int 							=						convert_attention(
                        lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
                    current_layer += 1

               if i != len(lowerCAmelCase		) - 1:
                    UpperCAmelCase__							: int 							=						F"up_blocks.{i}.upsamplers.0"
                    UpperCAmelCase__							: Tuple 							=						F"output_blocks.{current_layer-1}.2"
                    UpperCAmelCase__							: int 							=						convert_resnet(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     UpperCAmelCase__							: Union[str, Any] 							=						checkpoint["out.0.weight"]
     UpperCAmelCase__							: Optional[int] 							=						checkpoint["out.0.bias"]
     UpperCAmelCase__							: Any 							=						checkpoint["out.2.weight"]
     UpperCAmelCase__							: str 							=						checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
    A__      :	Union[str, Any]          =    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.""")

    A__      :	List[str]          =    parser.parse_args()
    A__      :	Union[str, Any]          =    strabool(args.class_cond)

    A__      :	List[str]          =    os.path.basename(args.unet_path)
    print(f"""Checkpoint: {ckpt_name}""")

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

    if not args.class_cond:
        A__      :	Optional[int]          =    None

    A__      :	List[Any]          =    con_pt_to_diffuser(args.unet_path, unet_config)

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

    A__      :	List[Any]          =    CMStochasticIterativeScheduler(**scheduler_config)

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





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(a - b		) for a, b in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)



def 							a__ (				lowerCAmelCase	:       list[float]		):






     '''simple docstring'''
     if point:
          if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
               for item in point:
                    if not isinstance(lowerCAmelCase       ,    (int, float)		):
                         UpperCAmelCase__							: Tuple 							=						(
                             "Expected a list of numbers as input, found "
                             F"{type(lowerCAmelCase		).__name__}"
                         )
                         raise TypeError(lowerCAmelCase		)
          else:
               UpperCAmelCase__							: Dict 							=						F"Expected a list of numbers as input, found {type(lowerCAmelCase		).__name__}"
               raise TypeError(lowerCAmelCase		)
     else:
          raise ValueError("Missing an input"		)



def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(x - y		) for x, y in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



