



"""simple docstring"""







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
     import torch.distributed.checkpoint as dist_cp
     from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
     from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
     from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
     from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowercase__		:				Any          =	get_logger(__name__)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				List[Any]=0						)						->   str:

  """simple docstring"""




  os.makedirs(__SCREAMING_SNAKE_CASE    ,   exist_ok=__SCREAMING_SNAKE_CASE						)
  with FSDP.state_dict_type(
      __SCREAMING_SNAKE_CASE    ,   fsdp_plugin.state_dict_type    ,   fsdp_plugin.state_dict_config    ,   fsdp_plugin.optim_state_dict_config						):
    lowerCAmelCase_   :				str								= model.state_dict()
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      lowerCAmelCase_   :				Dict								= f"{MODEL_NAME}.bin" if model_index == 0 else f"{MODEL_NAME}_{model_index}.bin"
      lowerCAmelCase_   :				Union[str, Any]								= os.path.join(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
      if accelerator.process_index == 0:
        logger.info(f"Saving model to {output_model_file}"						)
        torch.save(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
        logger.info(f"Model saved to {output_model_file}"						)
    elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
      lowerCAmelCase_   :				str								= (
          f"{MODEL_NAME}_rank{accelerator.process_index}.bin"
          if model_index == 0
          else f"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
      )
      lowerCAmelCase_   :				Dict								= os.path.join(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
      logger.info(f"Saving model to {output_model_file}"						)
      torch.save(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
      logger.info(f"Model saved to {output_model_file}"						)
    elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
      lowerCAmelCase_   :				List[str]								= os.path.join(__SCREAMING_SNAKE_CASE    ,   f"{MODEL_NAME}_{model_index}"						)
      os.makedirs(__SCREAMING_SNAKE_CASE    ,   exist_ok=__SCREAMING_SNAKE_CASE						)
      logger.info(f"Saving model to {ckpt_dir}"						)
      lowerCAmelCase_   :				Any								= {'model': state_dict}

      dist_cp.save_state_dict(
          state_dict=__SCREAMING_SNAKE_CASE    ,   storage_writer=dist_cp.FileSystemWriter(__SCREAMING_SNAKE_CASE						)    ,   planner=DefaultSavePlanner()    ,   )
      logger.info(f"Model saved to {ckpt_dir}"						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str=0						)						->   Tuple:

  """simple docstring"""




  accelerator.wait_for_everyone()
  with FSDP.state_dict_type(
      __SCREAMING_SNAKE_CASE    ,   fsdp_plugin.state_dict_type    ,   fsdp_plugin.state_dict_config    ,   fsdp_plugin.optim_state_dict_config						):
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      if type(__SCREAMING_SNAKE_CASE						) != FSDP and accelerator.process_index != 0:
        if not fsdp_plugin.sync_module_states:
          raise ValueError(
              'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
              'initializing FSDP object'						)
        return
      lowerCAmelCase_   :				Optional[int]								= f"{MODEL_NAME}.bin" if model_index == 0 else f"{MODEL_NAME}_{model_index}.bin"
      lowerCAmelCase_   :				Optional[int]								= os.path.join(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
      logger.info(f"Loading model from {input_model_file}"						)
      lowerCAmelCase_   :				Union[str, Any]								= torch.load(__SCREAMING_SNAKE_CASE						)
      logger.info(f"Model loaded from {input_model_file}"						)
    elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
      lowerCAmelCase_   :				Dict								= (
          f"{MODEL_NAME}_rank{accelerator.process_index}.bin"
          if model_index == 0
          else f"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
      )
      lowerCAmelCase_   :				Union[str, Any]								= os.path.join(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
      logger.info(f"Loading model from {input_model_file}"						)
      lowerCAmelCase_   :				Optional[int]								= torch.load(__SCREAMING_SNAKE_CASE						)
      logger.info(f"Model loaded from {input_model_file}"						)
    elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
      lowerCAmelCase_   :				Optional[Any]								= (
          os.path.join(__SCREAMING_SNAKE_CASE    ,   f"{MODEL_NAME}_{model_index}"						)
          if f"{MODEL_NAME}" not in input_dir
          else input_dir
      )
      logger.info(f"Loading model from {ckpt_dir}"						)
      lowerCAmelCase_   :				Any								= {'model': model.state_dict()}
      dist_cp.load_state_dict(
          state_dict=__SCREAMING_SNAKE_CASE    ,   storage_reader=dist_cp.FileSystemReader(__SCREAMING_SNAKE_CASE						)    ,   planner=DefaultLoadPlanner()    ,   )
      lowerCAmelCase_   :				Dict								= state_dict['model']
      logger.info(f"Model loaded from {ckpt_dir}"						)
    model.load_state_dict(__SCREAMING_SNAKE_CASE						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Optional[Any]=0						)						->   Optional[Any]:

  """simple docstring"""




  os.makedirs(__SCREAMING_SNAKE_CASE    ,   exist_ok=__SCREAMING_SNAKE_CASE						)
  with FSDP.state_dict_type(
      __SCREAMING_SNAKE_CASE    ,   fsdp_plugin.state_dict_type    ,   fsdp_plugin.state_dict_config    ,   fsdp_plugin.optim_state_dict_config						):
    lowerCAmelCase_   :				Tuple								= FSDP.optim_state_dict(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      if accelerator.process_index == 0:
        lowerCAmelCase_   :				List[str]								= (
            f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin"
        )
        lowerCAmelCase_   :				str								= os.path.join(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
        logger.info(f"Saving Optimizer state to {output_optimizer_file}"						)
        torch.save(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
        logger.info(f"Optimizer state saved in {output_optimizer_file}"						)
    else:
      lowerCAmelCase_   :				List[Any]								= os.path.join(__SCREAMING_SNAKE_CASE    ,   f"{OPTIMIZER_NAME}_{optimizer_index}"						)
      os.makedirs(__SCREAMING_SNAKE_CASE    ,   exist_ok=__SCREAMING_SNAKE_CASE						)
      logger.info(f"Saving Optimizer state to {ckpt_dir}"						)
      dist_cp.save_state_dict(
          state_dict={'optimizer': optim_state}    ,   storage_writer=dist_cp.FileSystemWriter(__SCREAMING_SNAKE_CASE						)    ,   planner=DefaultSavePlanner()    ,   )
      logger.info(f"Optimizer state saved in {ckpt_dir}"						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Tuple=0						)						->   int:

  """simple docstring"""




  accelerator.wait_for_everyone()
  with FSDP.state_dict_type(
      __SCREAMING_SNAKE_CASE    ,   fsdp_plugin.state_dict_type    ,   fsdp_plugin.state_dict_config    ,   fsdp_plugin.optim_state_dict_config						):
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      lowerCAmelCase_   :				Union[str, Any]								= None
      # below check should work but currently it isn't working (mostly opytorch issue),
      # in the meantime disabling it at the cost of excess memory usage
      # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
      lowerCAmelCase_   :				List[str]								= (
          f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin"
      )
      lowerCAmelCase_   :				Dict								= os.path.join(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
      logger.info(f"Loading Optimizer state from {input_optimizer_file}"						)
      lowerCAmelCase_   :				Tuple								= torch.load(__SCREAMING_SNAKE_CASE						)
      logger.info(f"Optimizer state loaded from {input_optimizer_file}"						)
    else:
      lowerCAmelCase_   :				Tuple								= (
          os.path.join(__SCREAMING_SNAKE_CASE    ,   f"{OPTIMIZER_NAME}_{optimizer_index}"						)
          if f"{OPTIMIZER_NAME}" not in input_dir
          else input_dir
      )
      logger.info(f"Loading Optimizer from {ckpt_dir}"						)
      lowerCAmelCase_   :				List[Any]								= load_sharded_optimizer_state_dict(
          model_state_dict=model.state_dict()    ,   optimizer_key='optimizer'    ,   storage_reader=dist_cp.FileSystemReader(__SCREAMING_SNAKE_CASE						)    ,   )
      lowerCAmelCase_   :				List[str]								= optim_state['optimizer']
      logger.info(f"Optimizer loaded from {ckpt_dir}"						)
    lowerCAmelCase_   :				List[str]								= FSDP.optim_state_dict_to_load(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
    optimizer.load_state_dict(__SCREAMING_SNAKE_CASE						)






"""simple docstring"""







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     if isinstance(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str=False      ):
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.0.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.in_layers.0.bias''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.weight''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.bias''']
     lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.out_layers.0.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.0.bias''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.out_layers.3.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.skip_connection.weight''']
          lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str=None      ):
     lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,			dim=0      )
     lowercase_							,  lowercase_							,  lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,			dim=0      )

     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.norm.weight''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.norm.bias''']

     lowercase_	:				List[str]													=	weight_q.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Tuple													=	bias_q.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Optional[Any]													=	weight_k.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				int													=	bias_k.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Optional[int]													=	weight_v.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Tuple													=	bias_v.squeeze(-1      ).squeeze(-1      )

     lowercase_	:				List[str]													=	(
         checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1      ).squeeze(-1      )
     )
     lowercase_	:				int													=	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1      ).squeeze(-1      )

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Any													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )
     lowercase_	:				Any													=	{}

     lowercase_	:				Dict													=	checkpoint['time_embed.0.weight']
     lowercase_	:				Optional[Any]													=	checkpoint['time_embed.0.bias']
     lowercase_	:				List[str]													=	checkpoint['time_embed.2.weight']
     lowercase_	:				Any													=	checkpoint['time_embed.2.bias']

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

     lowercase_	:				Optional[Any]													=	checkpoint['input_blocks.0.0.weight']
     lowercase_	:				Optional[int]													=	checkpoint['input_blocks.0.0.bias']

     lowercase_	:				Dict													=	unet_config['down_block_types']
     lowercase_	:				int													=	unet_config['layers_per_block']
     lowercase_	:				int													=	unet_config['attention_head_dim']
     lowercase_	:				Any													=	unet_config['block_out_channels']
     lowercase_	:				Optional[Any]													=	1
     lowercase_	:				Tuple													=	channels_list[0]

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

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

          if i != len(__SCREAMING_SNAKE_CASE      ) - 1:
               lowercase_	:				Tuple													=	F'''down_blocks.{i}.downsamplers.0'''
               lowercase_	:				str													=	F'''input_blocks.{current_layer}.0'''
               lowercase_	:				Tuple													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
               current_layer += 1

          lowercase_	:				int													=	current_channels

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

     lowercase_	:				Union[str, Any]													=	0
     lowercase_	:				Union[str, Any]													=	unet_config['up_block_types']

     for i, layer_type in enumerate(__SCREAMING_SNAKE_CASE      ):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1      ):
                    lowercase_	:				Any													=	F'''up_blocks.{i}.resnets.{j}'''
                    lowercase_	:				Any													=	F'''output_blocks.{current_layer}.0'''
                    lowercase_	:				Union[str, Any]													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			has_skip=__SCREAMING_SNAKE_CASE      )
                    current_layer += 1

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

               if i != len(__SCREAMING_SNAKE_CASE      ) - 1:
                    lowercase_	:				Any													=	F'''up_blocks.{i}.upsamplers.0'''
                    lowercase_	:				str													=	F'''output_blocks.{current_layer-1}.2'''
                    lowercase_	:				int													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     lowercase_	:				List[str]													=	checkpoint['out.0.weight']
     lowercase_	:				Dict													=	checkpoint['out.0.bias']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.weight']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.bias']

     return new_checkpoint


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=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.")

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       __SCREAMING_SNAKE_CASE    	=strabool(args.class_cond)

       __SCREAMING_SNAKE_CASE    	=os.path.basename(args.unet_path)
       print(F"Checkpoint: {ckpt_name}")

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

       if not args.class_cond:
              __SCREAMING_SNAKE_CASE    	=None

       __SCREAMING_SNAKE_CASE    	=con_pt_to_diffuser(args.unet_path, unet_config)

       __SCREAMING_SNAKE_CASE    	=UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              __SCREAMING_SNAKE_CASE    	=CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              __SCREAMING_SNAKE_CASE    	=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)):
              __SCREAMING_SNAKE_CASE    	=CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

       __SCREAMING_SNAKE_CASE    	=CMStochasticIterativeScheduler(**scheduler_config)

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

