


"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


snake_case							:	List[str]					      =				{
    '''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							:	Optional[int]					      =				{
    '''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							:	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[Any]					      =				{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

snake_case							:	Optional[Any]					      =				{
    '''num_train_timesteps''': 2_01,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

snake_case							:	Dict					      =				{
    '''num_train_timesteps''': 1_51,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}






def 							__lowercase       (			__lowerCAmelCase							:    List[Any]       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[str]=False       ):
							a__					=					checkpoint[F'{old_prefix}.in_layers.0.weight']
							a__					=					checkpoint[F'{old_prefix}.in_layers.0.bias']
							a__					=					checkpoint[F'{old_prefix}.in_layers.2.weight']
							a__					=					checkpoint[F'{old_prefix}.in_layers.2.bias']
							a__					=					checkpoint[F'{old_prefix}.emb_layers.1.weight']
							a__					=					checkpoint[F'{old_prefix}.emb_layers.1.bias']
							a__					=					checkpoint[F'{old_prefix}.out_layers.0.weight']
							a__					=					checkpoint[F'{old_prefix}.out_layers.0.bias']
							a__					=					checkpoint[F'{old_prefix}.out_layers.3.weight']
							a__					=					checkpoint[F'{old_prefix}.out_layers.3.bias']

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

							return new_checkpoint






def 							__lowercase       (			__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Optional[int]=None       ):
							a__				, a__				, a__					=					checkpoint[F'{old_prefix}.qkv.weight'].chunk(3 ,   dim=0       )
							a__				, a__				, a__					=					checkpoint[F'{old_prefix}.qkv.bias'].chunk(3 ,   dim=0       )

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

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

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

							return new_checkpoint






def 							__lowercase       (			__lowerCAmelCase							:    str ,   __lowerCAmelCase							:    int       ):
							a__					=					torch.load(__lowerCAmelCase ,   map_location='cpu'       )
							a__					=					{}

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

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

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

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

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

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

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

														a__					=					current_channels

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

							a__					=					0
							a__					=					unet_config['up_block_types']

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

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

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

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

							return new_checkpoint


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

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

						snake_case							:	Dict					      =				os.path.basename(args.unet_path)
						print(f"""Checkpoint: {ckpt_name}""")

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

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

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

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

						snake_case							:	Dict					      =				CMStochasticIterativeScheduler(**scheduler_config)

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








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if (direction == 1 and array[indexa] > array[indexa]) or (
							    direction == 0 and array[indexa] < array[indexa]
							):
														a__				, a__					=					array[indexa], array[indexa]






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														for i in range(__lowerCAmelCase ,   low + middle       ):
																					comp_and_swap(__lowerCAmelCase ,   __lowerCAmelCase ,   i + middle ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   __lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														bitonic_sort(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   1       )
														bitonic_sort(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   0       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				input('''Enter numbers separated by a comma:\n''').strip()
						snake_case							:	Optional[int]					      =				[int(item.strip()) for item in user_input.split(''',''')]

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print('''\nSorted array in ascending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print('''Sorted array in descending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')




