



"""simple docstring"""







import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
     from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   Dict:

  """simple docstring"""




  if is_torch_version('<'    ,   '2.0.0'						) or not hasattr(__SCREAMING_SNAKE_CASE    ,   '_dynamo'						):
    return False
  return isinstance(__SCREAMING_SNAKE_CASE    ,   torch._dynamo.eval_frame.OptimizedModule						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				bool = True						)						->   Any:

  """simple docstring"""




  lowerCAmelCase_   :				Union[str, Any]								= (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  lowerCAmelCase_   :				Optional[int]								= is_compiled_module(__SCREAMING_SNAKE_CASE						)
  if is_compiled:
    lowerCAmelCase_   :				Optional[Any]								= model
    lowerCAmelCase_   :				Tuple								= model._orig_mod

  if is_deepspeed_available():
    options += (DeepSpeedEngine,)

  while isinstance(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						):
    lowerCAmelCase_   :				Optional[Any]								= model.module

  if not keep_fpaa_wrapper:
    lowerCAmelCase_   :				str								= getattr(__SCREAMING_SNAKE_CASE    ,   'forward'						)
    lowerCAmelCase_   :				int								= model.__dict__.pop('_original_forward'    ,   __SCREAMING_SNAKE_CASE						)
    if original_forward is not None:
      while hasattr(__SCREAMING_SNAKE_CASE    ,   '__wrapped__'						):
        lowerCAmelCase_   :				Optional[int]								= forward.__wrapped__
        if forward == original_forward:
          break
      lowerCAmelCase_   :				int								= forward
    if getattr(__SCREAMING_SNAKE_CASE    ,   '_converted_to_transformer_engine'    ,   __SCREAMING_SNAKE_CASE						):
      convert_model(__SCREAMING_SNAKE_CASE    ,   to_transformer_engine=__SCREAMING_SNAKE_CASE						)

  if is_compiled:
    lowerCAmelCase_   :				List[Any]								= model
    lowerCAmelCase_   :				Optional[Any]								= compiled_model

  return model

def 				UpperCamelCase_    (    )						->   Optional[Any]:

  """simple docstring"""




  PartialState().wait_for_everyone()

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

  """simple docstring"""




  if PartialState().distributed_type == DistributedType.TPU:
    xm.save(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
  elif PartialState().local_process_index == 0:
    torch.save(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)

@contextmanager
def 				UpperCamelCase_    (    **lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  for key, value in kwargs.items():
    lowerCAmelCase_   :				List[Any]								= str(__SCREAMING_SNAKE_CASE						)

  yield

  for key in kwargs:
    if key.upper() in os.environ:
      del os.environ[key.upper()]

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

  """simple docstring"""




  if not hasattr(__SCREAMING_SNAKE_CASE    ,   '__qualname__'						) and not hasattr(__SCREAMING_SNAKE_CASE    ,   '__name__'						):
    lowerCAmelCase_   :				List[str]								= getattr(__SCREAMING_SNAKE_CASE    ,   '__class__'    ,   __SCREAMING_SNAKE_CASE						)
  if hasattr(__SCREAMING_SNAKE_CASE    ,   '__qualname__'						):
    return obj.__qualname__
  if hasattr(__SCREAMING_SNAKE_CASE    ,   '__name__'						):
    return obj.__name__
  return str(__SCREAMING_SNAKE_CASE						)

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

  """simple docstring"""




  for key, value in source.items():
    if isinstance(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						):
      lowerCAmelCase_   :				str								= destination.setdefault(__SCREAMING_SNAKE_CASE    ,   {}						)
      merge_dicts(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
    else:
      lowerCAmelCase_   :				List[str]								= value

  return destination

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = None						)						->   Union[str, Any]:

  """simple docstring"""




  if port is None:
    lowerCAmelCase_   :				Optional[int]								= 2_9500
  with socket.socket(socket.AF_INET    ,   socket.SOCK_STREAM						) as s:
    return s.connect_ex(('localhost', port)						) == 0






"""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)

