


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


snake_case				:    Optional[Any]       				=     {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 10_00,
    '''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''',
}

snake_case				:    List[str]       				=     {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 10_00,
    '''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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''',
}

snake_case				:    Optional[Any]       				=     {
    '''sample_size''': 2_56,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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''',
}

snake_case				:    List[str]       				=     {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}

snake_case				:    int       				=     {
    '''num_train_timesteps''': 2_01,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}

snake_case				:    Any       				=     {
    '''num_train_timesteps''': 1_51,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      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_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any]=False						):




      """simple docstring"""





      a						:Union[str, Any] 							=       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      a						:str 							=       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      a						:str 							=       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      a						:List[str] 							=       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      a						:str 							=       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      a						:str 							=       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      a						:Optional[int] 							=       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      a						:Optional[Any] 							=       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      a						:Optional[Any] 							=       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      a						:List[Any] 							=       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            a						:Optional[int] 							=       checkpoint[F'''{old_prefix}.skip_connection.weight''']
            a						:str 							=       checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint
def 				__lowerCamelCase						( UpperCAmelCase_						:		Dict ,  UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Dict=None						):




      """simple docstring"""





      a							,				a							,				a						:List[Any] 							=       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3 ,  dim=0						)
      a							,				a							,				a						:Any 							=       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3 ,  dim=0						)

      a						:Union[str, Any] 							=       checkpoint[F'''{old_prefix}.norm.weight''']
      a						:Union[str, Any] 							=       checkpoint[F'''{old_prefix}.norm.bias''']

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

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

      return new_checkpoint
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      a						:Any 							=       torch.load(UpperCAmelCase_ ,  map_location='''cpu'''						)
      a						:Optional[int] 							=       {}

      a						:Optional[int] 							=       checkpoint['''time_embed.0.weight''']
      a						:Optional[int] 							=       checkpoint['''time_embed.0.bias''']
      a						:Any 							=       checkpoint['''time_embed.2.weight''']
      a						:List[Any] 							=       checkpoint['''time_embed.2.bias''']

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

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

      a						:List[str] 							=       unet_config['''down_block_types''']
      a						:Optional[int] 							=       unet_config['''layers_per_block''']
      a						:int 							=       unet_config['''attention_head_dim''']
      a						:Optional[int] 							=       unet_config['''block_out_channels''']
      a						:Union[str, Any] 							=       1
      a						:Optional[Any] 							=       channels_list[0]

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

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

            if i != len(UpperCAmelCase_						) - 1:
                  a						:int 							=       F'''down_blocks.{i}.downsamplers.0'''
                  a						:List[str] 							=       F'''input_blocks.{current_layer}.0'''
                  a						:List[Any] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                  current_layer += 1

            a						:Union[str, Any] 							=       current_channels

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

      a						:int 							=       0
      a						:Any 							=       unet_config['''up_block_types''']

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

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

                  if i != len(UpperCAmelCase_						) - 1:
                        a						:Optional[int] 							=       F'''up_blocks.{i}.upsamplers.0'''
                        a						:Optional[Any] 							=       F'''output_blocks.{current_layer-1}.2'''
                        a						:Optional[int] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

      a						:Optional[Any] 							=       checkpoint['''out.0.weight''']
      a						:List[Any] 							=       checkpoint['''out.0.bias''']
      a						:Tuple 							=       checkpoint['''out.2.weight''']
      a						:List[str] 							=       checkpoint['''out.2.bias''']

      return new_checkpoint


if __name__ == "__main__":
     snake_case				:    List[Any]       				=     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.''')

     snake_case				:    Union[str, Any]       				=     parser.parse_args()
     snake_case				:    int       				=     strabool(args.class_cond)

     snake_case				:    Optional[Any]       				=     os.path.basename(args.unet_path)
     print(F"""Checkpoint: {ckpt_name}""")

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

     if not args.class_cond:
          snake_case				:    Optional[Any]       				=     None

     snake_case				:    Optional[int]       				=     con_pt_to_diffuser(args.unet_path, unet_config)

     snake_case				:    Tuple       				=     UNetaDModel(**unet_config)
     image_unet.load_state_dict(converted_unet_ckpt)

     # Get scheduler config
     if "cd" in ckpt_name or "test" in ckpt_name:
          snake_case				:    Union[str, Any]       				=     CD_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
          snake_case				:    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)):
          snake_case				:    Optional[Any]       				=     CT_LSUN_256_SCHEDULER_CONFIG
     else:
          raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

     snake_case				:    Optional[int]       				=     CMStochasticIterativeScheduler(**scheduler_config)

     snake_case				:    Any       				=     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
     consistency_model.save_pretrained(args.dump_path)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






