'''simple docstring'''




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__UpperCAmelCase												={
    "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												={
    "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												={
    "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												={
    "num_train_timesteps": 4_0,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__UpperCAmelCase												={
    "num_train_timesteps": 2_0_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__UpperCAmelCase												={
    "num_train_timesteps": 1_5_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   List[Any]:
    if isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
        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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   Optional[int]:
    __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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    )     ->   Dict:
    __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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  torch.load(UpperCamelCase__    ,  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(UpperCamelCase__    ):
        __lowerCamelCase          =  channels_list[i]
        __lowerCamelCase          =  current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(UpperCamelCase__    ):
                __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(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  has_skip=UpperCamelCase__    )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(UpperCamelCase__    ):
                __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(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  has_skip=UpperCamelCase__    )
                __lowerCamelCase          =  f"""down_blocks.{i}.attentions.{j}"""
                __lowerCamelCase          =  f"""input_blocks.{current_layer}.1"""
                __lowerCamelCase          =  convert_attention(
                    UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )
                current_layer += 1

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

        __lowerCamelCase          =  current_channels

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

    __lowerCamelCase          =  0
    __lowerCamelCase          =  unet_config['''up_block_types''']

    for i, layer_type in enumerate(UpperCamelCase__    ):
        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(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  has_skip=UpperCamelCase__    )
                current_layer += 1

            if i != len(UpperCamelCase__    ) - 1:
                __lowerCamelCase          =  f"""up_blocks.{i}.upsamplers.0"""
                __lowerCamelCase          =  f"""output_blocks.{current_layer-1}.1"""
                __lowerCamelCase          =  convert_resnet(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )
        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(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  has_skip=UpperCamelCase__    )
                __lowerCamelCase          =  f"""up_blocks.{i}.attentions.{j}"""
                __lowerCamelCase          =  f"""output_blocks.{current_layer}.1"""
                __lowerCamelCase          =  convert_attention(
                    UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )
                current_layer += 1

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

    __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__":
  __UpperCAmelCase												=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												=parser.parse_args()
  __UpperCAmelCase												=strabool(args.class_cond)

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

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

  if not args.class_cond:
    __UpperCAmelCase												=None

  __UpperCAmelCase												=con_pt_to_diffuser(args.unet_path, unet_config)

  __UpperCAmelCase												=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												=CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __UpperCAmelCase												=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												=CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

  __UpperCAmelCase												=CMStochasticIterativeScheduler(**scheduler_config)

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

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  set({'''(''', '''[''', '''{'''}    )
    __lowerCamelCase          =  set({''')''', ''']''', '''}'''}    )
    __lowerCamelCase          =  {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

    for i in range(len(UpperCamelCase__    )    ):
        if s[i] in open_brackets:
            stack.append(s[i]    )

        elif s[i] in closed_brackets and (
            len(UpperCamelCase__    ) == 0 or (len(UpperCamelCase__    ) > 0 and open_to_closed[stack.pop()] != s[i])
        ):
            return False

    return len(UpperCamelCase__    ) == 0






def __lowerCAmelCase			(							)     ->   Union[str, Any]:
    __lowerCamelCase          =  input('''Enter sequence of brackets: '''    )
    if is_balanced(UpperCamelCase__    ):
        print(UpperCamelCase__    ,  '''is balanced'''    )
    else:
        print(UpperCamelCase__    ,  '''is not balanced'''    )


if __name__ == "__main__":
  main()
