




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						:							str							=  {
    """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						:							List[Any]							=  {
    """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						:							List[Any]							=  {
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__A						:							Dict							=  {
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__A						:							Dict							=  {
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

			if has_skip:
						SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.skip_connection.weight"""]
						SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.skip_connection.bias"""]

			return new_checkpoint
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3					,	dim=0   )
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3					,	dim=0   )

			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.norm.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint[f"""{old_prefix}.norm.bias"""]

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

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

			return new_checkpoint
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					torch.load(SCREAMING_SNAKE_CASE					,	map_location="""cpu"""   )
			SCREAMING_SNAKE_CASE           =					{}

			SCREAMING_SNAKE_CASE           =					checkpoint["""time_embed.0.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint["""time_embed.0.bias"""]
			SCREAMING_SNAKE_CASE           =					checkpoint["""time_embed.2.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint["""time_embed.2.bias"""]

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

			SCREAMING_SNAKE_CASE           =					checkpoint["""input_blocks.0.0.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint["""input_blocks.0.0.bias"""]

			SCREAMING_SNAKE_CASE           =					unet_config["""down_block_types"""]
			SCREAMING_SNAKE_CASE           =					unet_config["""layers_per_block"""]
			SCREAMING_SNAKE_CASE           =					unet_config["""attention_head_dim"""]
			SCREAMING_SNAKE_CASE           =					unet_config["""block_out_channels"""]
			SCREAMING_SNAKE_CASE           =					1
			SCREAMING_SNAKE_CASE           =					channels_list[0]

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

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

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

						SCREAMING_SNAKE_CASE           =					current_channels

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

			SCREAMING_SNAKE_CASE           =					0
			SCREAMING_SNAKE_CASE           =					unet_config["""up_block_types"""]

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

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

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

			SCREAMING_SNAKE_CASE           =					checkpoint["""out.0.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint["""out.0.bias"""]
			SCREAMING_SNAKE_CASE           =					checkpoint["""out.2.weight"""]
			SCREAMING_SNAKE_CASE           =					checkpoint["""out.2.bias"""]

			return new_checkpoint


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

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

		__A						:							str							=  os.path.basename(args.unet_path)
		print(f'''Checkpoint: {ckpt_name}''')

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

		if not args.class_cond:
				__A						:							Optional[Any]							=  None

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

		__A						:							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:
				__A						:							str							=  CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__A						:							Tuple							=  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						:							Tuple							=  CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		__A						:							str							=  CMStochasticIterativeScheduler(**scheduler_config)

		__A						:							Any							=  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)












from __future__ import annotations




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Optional[int]       ,     a       :	int = 0			)			->					str:
										SCREAMING_SNAKE_CASE           =					key





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

										return [chr(ord(a			) ^ key			) for ch in content]





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

										return [chr(ord(a			) ^ key			) for ch in content]





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key can be any size
										while key > 255:
													key -= 255

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

										for ch in content:
													ans += chr(ord(a			) ^ key			)

										return ans





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key can be any size
										while key > 255:
													key -= 255

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

										for ch in content:
													ans += chr(ord(a			) ^ key			)

										return ans





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	str       ,     a       :	int = 0			)			->					bool:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										try:
													with open(a			) as fin, open("""encrypt.out"""       ,     """w+"""			) as fout:
																# actual encrypt-process
																for line in fin:
																			fout.write(self.encrypt_string(a       ,     a			)			)

										except OSError:
													return False

										return True







							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int			)			->					bool:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										try:
													with open(a			) as fin, open("""decrypt.out"""       ,     """w+"""			) as fout:
																# actual encrypt-process
																for line in fin:
																			fout.write(self.decrypt_string(a       ,     a			)			)

										except OSError:
													return False

										return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")






