import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__a  :     List[str]       =  {
    "sample_size": 3_2,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [3_2, 6_4],
    "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",
}

__a  :     Tuple       =  {
    "sample_size": 6_4,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    "attention_head_dim": 6_4,
    "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",
}

__a  :     Dict       =  {
    "sample_size": 2_5_6,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    "attention_head_dim": 6_4,
    "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",
}

__a  :     Optional[Any]       =  {
    "num_train_timesteps": 4_0,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__a  :     List[str]       =  {
    "num_train_timesteps": 2_0_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__a  :     Optional[Any]       =  {
    "num_train_timesteps": 1_5_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 				UpperCAmelCase						(    lowercase							):







				"""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 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase=False							):







				"""simple docstring"""



				__lowercase 			=							checkpoint[F"{old_prefix}.in_layers.0.weight"]
				__lowercase 			=							checkpoint[F"{old_prefix}.in_layers.0.bias"]
				__lowercase 			=							checkpoint[F"{old_prefix}.in_layers.2.weight"]
				__lowercase 			=							checkpoint[F"{old_prefix}.in_layers.2.bias"]
				__lowercase 			=							checkpoint[F"{old_prefix}.emb_layers.1.weight"]
				__lowercase 			=							checkpoint[F"{old_prefix}.emb_layers.1.bias"]
				__lowercase 			=							checkpoint[F"{old_prefix}.out_layers.0.weight"]
				__lowercase 			=							checkpoint[F"{old_prefix}.out_layers.0.bias"]
				__lowercase 			=							checkpoint[F"{old_prefix}.out_layers.3.weight"]
				__lowercase 			=							checkpoint[F"{old_prefix}.out_layers.3.bias"]

				if has_skip:
								__lowercase 			=							checkpoint[F"{old_prefix}.skip_connection.weight"]
								__lowercase 			=							checkpoint[F"{old_prefix}.skip_connection.bias"]

				return new_checkpoint





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase=None							):







				"""simple docstring"""



				__lowercase			,     __lowercase			,     __lowercase 			=							checkpoint[F"{old_prefix}.qkv.weight"].chunk(3							,			dim=0							)
				__lowercase			,     __lowercase			,     __lowercase 			=							checkpoint[F"{old_prefix}.qkv.bias"].chunk(3							,			dim=0							)

				__lowercase 			=							checkpoint[F"{old_prefix}.norm.weight"]
				__lowercase 			=							checkpoint[F"{old_prefix}.norm.bias"]

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

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

				return new_checkpoint





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							torch.load(_UpperCamelCase							,			map_location='''cpu'''							)
				__lowercase 			=							{}

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

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

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

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

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

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

								if i != len(_UpperCamelCase							) - 1:
												__lowercase 			=							F"down_blocks.{i}.downsamplers.0"
												__lowercase 			=							F"input_blocks.{current_layer}.0"
												__lowercase 			=							convert_resnet(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							)
												current_layer += 1

								__lowercase 			=							current_channels

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

				__lowercase 			=							0
				__lowercase 			=							unet_config['''up_block_types''']

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

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

												if i != len(_UpperCamelCase							) - 1:
																__lowercase 			=							F"up_blocks.{i}.upsamplers.0"
																__lowercase 			=							F"output_blocks.{current_layer-1}.2"
																__lowercase 			=							convert_resnet(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							)

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

				return new_checkpoint


if __name__ == "__main__":
		__a  :     Dict       =  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.""")

		__a  :     Optional[Any]       =  parser.parse_args()
		__a  :     int       =  strabool(args.class_cond)

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

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

		if not args.class_cond:
				__a  :     Union[str, Any]       =  None

		__a  :     int       =  con_pt_to_diffuser(args.unet_path, unet_config)

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

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

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



"""simple docstring"""


from __future__ import annotations


def _lowerCamelCase	(		_UpperCamelCase = 4      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			abs(_UpperCamelCase      ) or 4
			return [[1 + x + y * row_size for x in range(_UpperCamelCase      )] for y in range(_UpperCamelCase      )]


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_row(transpose(_UpperCamelCase      )      )
			# OR.. transpose(reverse_column(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_row(reverse_column(_UpperCamelCase      )      )
			# OR.. reverse_column(reverse_row(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_column(transpose(_UpperCamelCase      )      )
			# OR.. transpose(reverse_row(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[list(_UpperCamelCase      ) for x in zip(*_UpperCamelCase      )]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			matrix[::-1]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[x[::-1] for x in matrix]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			for i in matrix:
						print(*_UpperCamelCase      )


if __name__ == "__main__":
	A     :   Dict			    =			make_matrix()
	print("\norigin:\n")
	print_matrix(matrix)
	print("\nrotate 90 counterclockwise:\n")
	print_matrix(rotate_aa(matrix))

	A     :   List[str]			    =			make_matrix()
	print("\norigin:\n")
	print_matrix(matrix)
	print("\nrotate 180:\n")
	print_matrix(rotate_aaa(matrix))

	A     :   str			    =			make_matrix()
	print("\norigin:\n")
	print_matrix(matrix)
	print("\nrotate 270 counterclockwise:\n")
	print_matrix(rotate_aaa(matrix))




