






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCAmelCase__      			=						{
    '''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''',
}

UpperCAmelCase__      			=						{
    '''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''',
}

UpperCAmelCase__      			=						{
    '''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''',
}

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

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

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







def     a_   (__A      )				->						Dict:



					"""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     a_   (__A				,       __A				,       __A				,       __A				,       __A=False      )				->						Dict:



					"""simple docstring"""
					__a					:						List[str]      =					checkpoint[f'{old_prefix}.in_layers.0.weight']
					__a					:						Optional[int]      =					checkpoint[f'{old_prefix}.in_layers.0.bias']
					__a					:						str      =					checkpoint[f'{old_prefix}.in_layers.2.weight']
					__a					:						List[Any]      =					checkpoint[f'{old_prefix}.in_layers.2.bias']
					__a					:						Tuple      =					checkpoint[f'{old_prefix}.emb_layers.1.weight']
					__a					:						Union[str, Any]      =					checkpoint[f'{old_prefix}.emb_layers.1.bias']
					__a					:						Union[str, Any]      =					checkpoint[f'{old_prefix}.out_layers.0.weight']
					__a					:						Optional[Any]      =					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					:						Optional[Any]      =					checkpoint[f'{old_prefix}.skip_connection.weight']
										__a					:						Optional[int]      =					checkpoint[f'{old_prefix}.skip_connection.bias']

					return new_checkpoint







def     a_   (__A				,       __A				,       __A				,       __A				,       __A=None      )				->						Optional[int]:



					"""simple docstring"""
					__a      ,			__a      ,			__a					:						Optional[int]      =					checkpoint[f'{old_prefix}.qkv.weight'].chunk(3				,       dim=0      )
					__a      ,			__a      ,			__a					:						Tuple      =					checkpoint[f'{old_prefix}.qkv.bias'].chunk(3				,       dim=0      )

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

					__a					:						str      =					weight_q.squeeze(-1      ).squeeze(-1      )
					__a					:						int      =					bias_q.squeeze(-1      ).squeeze(-1      )
					__a					:						Optional[Any]      =					weight_k.squeeze(-1      ).squeeze(-1      )
					__a					:						Optional[Any]      =					bias_k.squeeze(-1      ).squeeze(-1      )
					__a					:						List[str]      =					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					:						Union[str, Any]      =					checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1      ).squeeze(-1      )

					return new_checkpoint







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Tuple      =					torch.load(__A				,       map_location="cpu"      )
					__a					:						Any      =					{}

					__a					:						List[str]      =					checkpoint["time_embed.0.weight"]
					__a					:						Optional[int]      =					checkpoint["time_embed.0.bias"]
					__a					:						List[str]      =					checkpoint["time_embed.2.weight"]
					__a					:						List[str]      =					checkpoint["time_embed.2.bias"]

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

					__a					:						str      =					checkpoint["input_blocks.0.0.weight"]
					__a					:						List[Any]      =					checkpoint["input_blocks.0.0.bias"]

					__a					:						Any      =					unet_config["down_block_types"]
					__a					:						str      =					unet_config["layers_per_block"]
					__a					:						str      =					unet_config["attention_head_dim"]
					__a					:						List[Any]      =					unet_config["block_out_channels"]
					__a					:						Any      =					1
					__a					:						str      =					channels_list[0]

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

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

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

										__a					:						Optional[Any]      =					current_channels

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

					__a					:						Optional[Any]      =					0
					__a					:						Optional[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					:						Optional[Any]      =					f'up_blocks.{i}.resnets.{j}'
																				__a					:						int      =					f'output_blocks.{current_layer}.0'
																				__a					:						str      =					convert_resnet(__A				,       __A				,       __A				,       __A				,       has_skip=__A      )
																				current_layer += 1

															if i != len(__A      ) - 1:
																				__a					:						Tuple      =					f'up_blocks.{i}.upsamplers.0'
																				__a					:						List[Any]      =					f'output_blocks.{current_layer-1}.1'
																				__a					:						Union[str, Any]      =					convert_resnet(__A				,       __A				,       __A				,       __A      )
										elif layer_type == "AttnUpBlock2D":
															for j in range(layers_per_block + 1      ):
																				__a					:						Optional[int]      =					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      )
																				__a					:						int      =					f'up_blocks.{i}.attentions.{j}'
																				__a					:						List[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					:						str      =					f'output_blocks.{current_layer-1}.2'
																				__a					:						Optional[int]      =					convert_resnet(__A				,       __A				,       __A				,       __A      )

					__a					:						Tuple      =					checkpoint["out.0.weight"]
					__a					:						str      =					checkpoint["out.0.bias"]
					__a					:						Union[str, Any]      =					checkpoint["out.2.weight"]
					__a					:						Dict      =					checkpoint["out.2.bias"]

					return new_checkpoint


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

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

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

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

			if not args.class_cond:
						UpperCAmelCase__      			=						None

			UpperCAmelCase__      			=						con_pt_to_diffuser(args.unet_path, unet_config)

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

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

			UpperCAmelCase__      			=						CMStochasticIterativeScheduler(**scheduler_config)

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













from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





