
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase__         ={
    '''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''',
}

lowercase__         ={
    '''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''',
}

lowercase__         ={
    '''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''',
}

lowercase__         ={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowercase__         ={
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowercase__         ={
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}




def   __UpperCamelCase	(							lowerCAmelCase__				:							Optional[Any]    ):
 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   __UpperCamelCase	(							lowerCAmelCase__				:							List[str]    ,  lowerCAmelCase__				:							Optional[int]    ,  lowerCAmelCase__				:							Union[str, Any]    ,  lowerCAmelCase__				:							Tuple    ,  lowerCAmelCase__				:							Optional[Any]=False    ):
 __a					:    Optional[int]													=    checkpoint[f"{old_prefix}.in_layers.0.weight"]
 __a					:    int													=    checkpoint[f"{old_prefix}.in_layers.0.bias"]
 __a					:    Tuple													=    checkpoint[f"{old_prefix}.in_layers.2.weight"]
 __a					:    Optional[Any]													=    checkpoint[f"{old_prefix}.in_layers.2.bias"]
 __a					:    Dict													=    checkpoint[f"{old_prefix}.emb_layers.1.weight"]
 __a					:    str													=    checkpoint[f"{old_prefix}.emb_layers.1.bias"]
 __a					:    Optional[Any]													=    checkpoint[f"{old_prefix}.out_layers.0.weight"]
 __a					:    Optional[int]													=    checkpoint[f"{old_prefix}.out_layers.0.bias"]
 __a					:    Any													=    checkpoint[f"{old_prefix}.out_layers.3.weight"]
 __a					:    int													=    checkpoint[f"{old_prefix}.out_layers.3.bias"]

 if has_skip:
  __a					:    Dict													=    checkpoint[f"{old_prefix}.skip_connection.weight"]
  __a					:    Tuple													=    checkpoint[f"{old_prefix}.skip_connection.bias"]

 return new_checkpoint




def   __UpperCamelCase	(							lowerCAmelCase__				:							Optional[Any]    ,  lowerCAmelCase__				:							Tuple    ,  lowerCAmelCase__				:							Tuple    ,  lowerCAmelCase__				:							int    ,  lowerCAmelCase__				:							str=None    ):
 __a					:    Tuple													=    checkpoint[f"{old_prefix}.qkv.weight"].chunk(3    ,  dim=0    )
 __a					:    Dict													=    checkpoint[f"{old_prefix}.qkv.bias"].chunk(3    ,  dim=0    )

 __a					:    Tuple													=    checkpoint[f"{old_prefix}.norm.weight"]
 __a					:    Optional[int]													=    checkpoint[f"{old_prefix}.norm.bias"]

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

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

 return new_checkpoint




def   __UpperCamelCase	(							lowerCAmelCase__				:							List[Any]    ,  lowerCAmelCase__				:							List[str]    ):
 __a					:    Any													=    torch.load(A_    ,  map_location='''cpu'''    )
 __a					:    str													=    {}

 __a					:    Union[str, Any]													=    checkpoint['''time_embed.0.weight''']
 __a					:    str													=    checkpoint['''time_embed.0.bias''']
 __a					:    Any													=    checkpoint['''time_embed.2.weight''']
 __a					:    List[str]													=    checkpoint['''time_embed.2.bias''']

 if unet_config["num_class_embeds"] is not None:
  __a					:    Optional[Any]													=    checkpoint['''label_emb.weight''']

 __a					:    Union[str, Any]													=    checkpoint['''input_blocks.0.0.weight''']
 __a					:    Optional[Any]													=    checkpoint['''input_blocks.0.0.bias''']

 __a					:    Optional[int]													=    unet_config['''down_block_types''']
 __a					:    List[str]													=    unet_config['''layers_per_block''']
 __a					:    Any													=    unet_config['''attention_head_dim''']
 __a					:    List[str]													=    unet_config['''block_out_channels''']
 __a					:    Dict													=    1
 __a					:    List[str]													=    channels_list[0]

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

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

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

  __a					:    str													=    current_channels

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

 __a					:    Dict													=    0
 __a					:    int													=    unet_config['''up_block_types''']

 for i, layer_type in enumerate(A_    ):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1    ):
    __a					:    Dict													=    f"up_blocks.{i}.resnets.{j}"
    __a					:    Tuple													=    f"output_blocks.{current_layer}.0"
    __a					:    Tuple													=    convert_resnet(A_    ,  A_    ,  A_    ,  A_    ,  has_skip=A_    )
    current_layer += 1

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

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

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

 return new_checkpoint


if __name__ == "__main__":
      lowercase__         =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.')

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

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

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

      if not args.class_cond:
            lowercase__         =None

      lowercase__         =con_pt_to_diffuser(args.unet_path, unet_config)

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

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

      lowercase__         =CMStochasticIterativeScheduler(**scheduler_config)

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



"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	for i in range(len(A_					) - 1		,	0		,	-1					):
		lowerCAmelCase__      :		Optional[Any]    							=      False

		for j in range(A_		,	0		,	-1					):
			if unsorted[j] < unsorted[j - 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j - 1], unsorted[j]
				lowerCAmelCase__      :		Dict    							=      True

		for j in range(A_					):
			if unsorted[j] > unsorted[j + 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j + 1], unsorted[j]
				lowerCAmelCase__      :		Any    							=      True

		if not swapped:
			break
	return unsorted


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__UpperCamelCase    : int				 =						input('''Enter numbers separated by a comma:\n''').strip()
							__UpperCamelCase    : Optional[Any]				 =						[int(item) for item in user_input.split(''',''')]
							print(F'''{cocktail_shaker_sort(unsorted) = }''')




