






"""simple docstring"""





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_SCREAMING_SNAKE_CASE					:				Tuple					    =						{
    '''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					:				str					    =						{
    '''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					:				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''',
}

_SCREAMING_SNAKE_CASE					:				List[str]					    =						{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_SCREAMING_SNAKE_CASE					:				List[Any]					    =						{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				int:
  if isinstance(_lowerCamelCase		,				_lowerCamelCase ):
    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__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		List[Any]=False )      ->				List[Any]:
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.in_layers.0.weight''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.in_layers.0.bias''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.in_layers.2.weight''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.in_layers.2.bias''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.out_layers.0.weight''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.out_layers.0.bias''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.out_layers.3.weight''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.out_layers.3.bias''']

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

  return new_checkpoint







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		int=None )      ->				Union[str, Any]:
  lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3		,				dim=0 )
  lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3		,				dim=0 )

  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.norm.weight''']
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.norm.bias''']

  lowerCamelCase_         =						weight_q.squeeze(-1 ).squeeze(-1 )
  lowerCamelCase_         =						bias_q.squeeze(-1 ).squeeze(-1 )
  lowerCamelCase_         =						weight_k.squeeze(-1 ).squeeze(-1 )
  lowerCamelCase_         =						bias_k.squeeze(-1 ).squeeze(-1 )
  lowerCamelCase_         =						weight_v.squeeze(-1 ).squeeze(-1 )
  lowerCamelCase_         =						bias_v.squeeze(-1 ).squeeze(-1 )

  lowerCamelCase_         =						(
      checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
  )
  lowerCamelCase_         =						checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )

  return new_checkpoint







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Optional[int] )      ->				str:
  lowerCamelCase_         =						torch.load(_lowerCamelCase		,				map_location='cpu' )
  lowerCamelCase_         =						{}

  lowerCamelCase_         =						checkpoint['time_embed.0.weight']
  lowerCamelCase_         =						checkpoint['time_embed.0.bias']
  lowerCamelCase_         =						checkpoint['time_embed.2.weight']
  lowerCamelCase_         =						checkpoint['time_embed.2.bias']

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

  lowerCamelCase_         =						checkpoint['input_blocks.0.0.weight']
  lowerCamelCase_         =						checkpoint['input_blocks.0.0.bias']

  lowerCamelCase_         =						unet_config['down_block_types']
  lowerCamelCase_         =						unet_config['layers_per_block']
  lowerCamelCase_         =						unet_config['attention_head_dim']
  lowerCamelCase_         =						unet_config['block_out_channels']
  lowerCamelCase_         =						1
  lowerCamelCase_         =						channels_list[0]

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

    elif layer_type == "AttnDownBlock2D":
      for j in range(_lowerCamelCase ):
        lowerCamelCase_         =						F'''down_blocks.{i}.resnets.{j}'''
        lowerCamelCase_         =						F'''input_blocks.{current_layer}.0'''
        lowerCamelCase_         =						True if j == 0 and downsample_block_has_skip else False
        lowerCamelCase_         =						convert_resnet(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				has_skip=_lowerCamelCase )
        lowerCamelCase_         =						F'''down_blocks.{i}.attentions.{j}'''
        lowerCamelCase_         =						F'''input_blocks.{current_layer}.1'''
        lowerCamelCase_         =						convert_attention(
            _lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
        current_layer += 1

    if i != len(_lowerCamelCase ) - 1:
      lowerCamelCase_         =						F'''down_blocks.{i}.downsamplers.0'''
      lowerCamelCase_         =						F'''input_blocks.{current_layer}.0'''
      lowerCamelCase_         =						convert_resnet(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      current_layer += 1

    lowerCamelCase_         =						current_channels

  # hardcoded the mid-block for now
  lowerCamelCase_         =						'mid_block.resnets.0'
  lowerCamelCase_         =						'middle_block.0'
  lowerCamelCase_         =						convert_resnet(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
  lowerCamelCase_         =						'mid_block.attentions.0'
  lowerCamelCase_         =						'middle_block.1'
  lowerCamelCase_         =						convert_attention(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
  lowerCamelCase_         =						'mid_block.resnets.1'
  lowerCamelCase_         =						'middle_block.2'
  lowerCamelCase_         =						convert_resnet(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )

  lowerCamelCase_         =						0
  lowerCamelCase_         =						unet_config['up_block_types']

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

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

      if i != len(_lowerCamelCase ) - 1:
        lowerCamelCase_         =						F'''up_blocks.{i}.upsamplers.0'''
        lowerCamelCase_         =						F'''output_blocks.{current_layer-1}.2'''
        lowerCamelCase_         =						convert_resnet(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )

  lowerCamelCase_         =						checkpoint['out.0.weight']
  lowerCamelCase_         =						checkpoint['out.0.bias']
  lowerCamelCase_         =						checkpoint['out.2.weight']
  lowerCamelCase_         =						checkpoint['out.2.bias']

  return new_checkpoint


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Union[str, 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.''')

  _SCREAMING_SNAKE_CASE					:				Any					    =						parser.parse_args()
  _SCREAMING_SNAKE_CASE					:				str					    =						strabool(args.class_cond)

  _SCREAMING_SNAKE_CASE					:				Dict					    =						os.path.basename(args.unet_path)
  print(F'''Checkpoint: {ckpt_name}''')

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

  if not args.class_cond:
    _SCREAMING_SNAKE_CASE					:				int					    =						None

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

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

  _SCREAMING_SNAKE_CASE					:				Dict					    =						CMStochasticIterativeScheduler(**scheduler_config)

  _SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)












"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




