



"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__              =							{
    '''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''',
}

lowerCAmelCase__              =							{
    '''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''',
}

lowerCAmelCase__              =							{
    '''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''',
}

lowerCAmelCase__              =							{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

lowerCAmelCase__              =							{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

lowerCAmelCase__              =							{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def 			snake_case_    ( A_   :						List[str]		):


       '''simple docstring'''





       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 			snake_case_    ( A_   :						Union[str, Any],							A_   :						Optional[Any],							A_   :						Tuple,							A_   :						Dict,							A_   :						Optional[Any]=False		):


       '''simple docstring'''





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

       if has_skip:
              _lowerCamelCase		:							Any            =					checkpoint[F'''{old_prefix}.skip_connection.weight''']
              _lowerCamelCase		:							str            =					checkpoint[F'''{old_prefix}.skip_connection.bias''']

       return new_checkpoint






def 			snake_case_    ( A_   :						Optional[int],							A_   :						Tuple,							A_   :						Union[str, Any],							A_   :						Tuple,							A_   :						str=None		):


       '''simple docstring'''





       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							str            =					checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3,							dim=0		)
       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3,							dim=0		)

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

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

       _lowerCamelCase		:							Optional[Any]            =					(
           checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1		).squeeze(-1		)
       )
       _lowerCamelCase		:							List[Any]            =					checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1		).squeeze(-1		)

       return new_checkpoint






def 			snake_case_    ( A_   :						str,							A_   :						Union[str, Any]		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					torch.load(A_,							map_location='''cpu'''		)
       _lowerCamelCase		:							List[str]            =					{}

       _lowerCamelCase		:							str            =					checkpoint['''time_embed.0.weight''']
       _lowerCamelCase		:							Tuple            =					checkpoint['''time_embed.0.bias''']
       _lowerCamelCase		:							str            =					checkpoint['''time_embed.2.weight''']
       _lowerCamelCase		:							List[str]            =					checkpoint['''time_embed.2.bias''']

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

       _lowerCamelCase		:							Any            =					checkpoint['''input_blocks.0.0.weight''']
       _lowerCamelCase		:							Dict            =					checkpoint['''input_blocks.0.0.bias''']

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

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

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

              if i != len(A_		) - 1:
                     _lowerCamelCase		:							Dict            =					F'''down_blocks.{i}.downsamplers.0'''
                     _lowerCamelCase		:							Any            =					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		:							Union[str, Any]            =					'''mid_block.resnets.0'''
       _lowerCamelCase		:							Dict            =					'''middle_block.0'''
       _lowerCamelCase		:							Optional[Any]            =					convert_resnet(A_,							A_,							A_,							A_		)
       _lowerCamelCase		:							List[str]            =					'''mid_block.attentions.0'''
       _lowerCamelCase		:							Dict            =					'''middle_block.1'''
       _lowerCamelCase		:							Union[str, Any]            =					convert_attention(A_,							A_,							A_,							A_,							A_		)
       _lowerCamelCase		:							Tuple            =					'''mid_block.resnets.1'''
       _lowerCamelCase		:							Optional[int]            =					'''middle_block.2'''
       _lowerCamelCase		:							Any            =					convert_resnet(A_,							A_,							A_,							A_		)

       _lowerCamelCase		:							str            =					0
       _lowerCamelCase		:							Any            =					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		:							str            =					F'''up_blocks.{i}.resnets.{j}'''
                            _lowerCamelCase		:							List[str]            =					F'''output_blocks.{current_layer}.0'''
                            _lowerCamelCase		:							Union[str, Any]            =					convert_resnet(A_,							A_,							A_,							A_,							has_skip=A_		)
                            current_layer += 1

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

                     if i != len(A_		) - 1:
                            _lowerCamelCase		:							Any            =					F'''up_blocks.{i}.upsamplers.0'''
                            _lowerCamelCase		:							Union[str, Any]            =					F'''output_blocks.{current_layer-1}.2'''
                            _lowerCamelCase		:							List[str]            =					convert_resnet(A_,							A_,							A_,							A_		)

       _lowerCamelCase		:							str            =					checkpoint['''out.0.weight''']
       _lowerCamelCase		:							Any            =					checkpoint['''out.0.bias''']
       _lowerCamelCase		:							int            =					checkpoint['''out.2.weight''']
       _lowerCamelCase		:							Optional[Any]            =					checkpoint['''out.2.bias''']

       return new_checkpoint


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

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

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

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

   if not args.class_cond:
      lowerCAmelCase__              =							None

   lowerCAmelCase__              =							con_pt_to_diffuser(args.unet_path, unet_config)

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

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

   lowerCAmelCase__              =							CMStochasticIterativeScheduler(**scheduler_config)

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







from __future__ import annotations

def 	_lowerCAmelCase (    A__    ,	A__ = None						):
      lowercase__  =	word_bank or []
      # create a table
      lowercase__  =	len(A__						) + 1

      lowercase__  =	[]
      for _ in range(A__						):
            table.append([]						)
      # seed value
      lowercase__  =	[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(A__						):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(A__						)] == word:
                              lowercase__  =	[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(A__						)] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(A__						)]:
            combination.reverse()

      return table[len(A__						)]


if __name__ == "__main__":
       print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
       print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
       print(
           all_construct(
               "hexagonosaurus",
               ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
           )
       )
