"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_a   :	Optional[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   :	Optional[int] 					=     {
    '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   :	List[str] 					=     {
    '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   :	Union[str, Any] 					=     {
    'num_train_timesteps': 40,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

_a   :	Optional[int] 					=     {
    'num_train_timesteps': 201,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

_a   :	Any 					=     {
    'num_train_timesteps': 151,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]			) ->   str:





  if isinstance(A_    ,A_			):
    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  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: Union[str, Any]=False			) ->   Union[str, Any]:
  _lowerCAmelCase		: Optional[int]						    =  checkpoint[f"{old_prefix}.in_layers.0.weight"]
  _lowerCAmelCase		: Dict						    =  checkpoint[f"{old_prefix}.in_layers.0.bias"]
  _lowerCAmelCase		: Optional[int]						    =  checkpoint[f"{old_prefix}.in_layers.2.weight"]
  _lowerCAmelCase		: List[Any]						    =  checkpoint[f"{old_prefix}.in_layers.2.bias"]
  _lowerCAmelCase		: Union[str, Any]						    =  checkpoint[f"{old_prefix}.emb_layers.1.weight"]
  _lowerCAmelCase		: Dict						    =  checkpoint[f"{old_prefix}.emb_layers.1.bias"]
  _lowerCAmelCase		: Any						    =  checkpoint[f"{old_prefix}.out_layers.0.weight"]
  _lowerCAmelCase		: List[str]						    =  checkpoint[f"{old_prefix}.out_layers.0.bias"]
  _lowerCAmelCase		: Optional[int]						    =  checkpoint[f"{old_prefix}.out_layers.3.weight"]
  _lowerCAmelCase		: Optional[int]						    =  checkpoint[f"{old_prefix}.out_layers.3.bias"]

  if has_skip:
    _lowerCAmelCase		: Union[str, Any]						    =  checkpoint[f"{old_prefix}.skip_connection.weight"]
    _lowerCAmelCase		: List[str]						    =  checkpoint[f"{old_prefix}.skip_connection.bias"]

  return new_checkpoint





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: int    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Tuple=None			) ->   Any:
  _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  checkpoint[f"{old_prefix}.qkv.weight"].chunk(3    ,dim=0			)
  _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: int						    =  checkpoint[f"{old_prefix}.qkv.bias"].chunk(3    ,dim=0			)

  _lowerCAmelCase		: Union[str, Any]						    =  checkpoint[f"{old_prefix}.norm.weight"]
  _lowerCAmelCase		: List[str]						    =  checkpoint[f"{old_prefix}.norm.bias"]

  _lowerCAmelCase		: Any						    =  weight_q.squeeze(-1			).squeeze(-1			)
  _lowerCAmelCase		: List[str]						    =  bias_q.squeeze(-1			).squeeze(-1			)
  _lowerCAmelCase		: Any						    =  weight_k.squeeze(-1			).squeeze(-1			)
  _lowerCAmelCase		: List[str]						    =  bias_k.squeeze(-1			).squeeze(-1			)
  _lowerCAmelCase		: Any						    =  weight_v.squeeze(-1			).squeeze(-1			)
  _lowerCAmelCase		: str						    =  bias_v.squeeze(-1			).squeeze(-1			)

  _lowerCAmelCase		: str						    =  (
      checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1			).squeeze(-1			)
  )
  _lowerCAmelCase		: Tuple						    =  checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1			).squeeze(-1			)

  return new_checkpoint





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any    ,_lowerCamelCase		: List[str]			) ->   str:
  _lowerCAmelCase		: int						    =  torch.load(A_    ,map_location="""cpu"""			)
  _lowerCAmelCase		: Union[str, Any]						    =  {}

  _lowerCAmelCase		: Tuple						    =  checkpoint["""time_embed.0.weight"""]
  _lowerCAmelCase		: List[Any]						    =  checkpoint["""time_embed.0.bias"""]
  _lowerCAmelCase		: Optional[int]						    =  checkpoint["""time_embed.2.weight"""]
  _lowerCAmelCase		: List[Any]						    =  checkpoint["""time_embed.2.bias"""]

  if unet_config["num_class_embeds"] is not None:
    _lowerCAmelCase		: Union[str, Any]						    =  checkpoint["""label_emb.weight"""]

  _lowerCAmelCase		: Optional[Any]						    =  checkpoint["""input_blocks.0.0.weight"""]
  _lowerCAmelCase		: Dict						    =  checkpoint["""input_blocks.0.0.bias"""]

  _lowerCAmelCase		: List[Any]						    =  unet_config["""down_block_types"""]
  _lowerCAmelCase		: Any						    =  unet_config["""layers_per_block"""]
  _lowerCAmelCase		: Union[str, Any]						    =  unet_config["""attention_head_dim"""]
  _lowerCAmelCase		: Optional[Any]						    =  unet_config["""block_out_channels"""]
  _lowerCAmelCase		: Tuple						    =  1
  _lowerCAmelCase		: Optional[int]						    =  channels_list[0]

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

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

    if i != len(A_			) - 1:
      _lowerCAmelCase		: List[str]						    =  f"down_blocks.{i}.downsamplers.0"
      _lowerCAmelCase		: List[str]						    =  f"input_blocks.{current_layer}.0"
      _lowerCAmelCase		: List[Any]						    =  convert_resnet(A_    ,A_    ,A_    ,A_			)
      current_layer += 1

    _lowerCAmelCase		: Tuple						    =  current_channels

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

  _lowerCAmelCase		: Optional[Any]						    =  0
  _lowerCAmelCase		: Dict						    =  unet_config["""up_block_types"""]

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

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

      if i != len(A_			) - 1:
        _lowerCAmelCase		: List[str]						    =  f"up_blocks.{i}.upsamplers.0"
        _lowerCAmelCase		: str						    =  f"output_blocks.{current_layer-1}.2"
        _lowerCAmelCase		: Any						    =  convert_resnet(A_    ,A_    ,A_    ,A_			)

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

  return new_checkpoint


if __name__ == "__main__":
     _a   :	Tuple 					=     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   :	int 					=     parser.parse_args()
     _a   :	int 					=     strabool(args.class_cond)

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

     # Get U-Net config
     if "imagenet64" in ckpt_name:
          _a   :	int 					=     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   :	int 					=     TEST_UNET_CONFIG
     else:
          raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

     if not args.class_cond:
          _a   :	str 					=     None

     _a   :	Union[str, Any] 					=     con_pt_to_diffuser(args.unet_path, unet_config)

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

     _a   :	Union[str, Any] 					=     CMStochasticIterativeScheduler(**scheduler_config)

     _a   :	Dict 					=     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
     consistency_model.save_pretrained(args.dump_path)





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




