




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE__     :     int		    =       {
    "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",
}

SCREAMING_SNAKE_CASE__     :     Dict		    =       {
    "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",
}

SCREAMING_SNAKE_CASE__     :     List[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",
}

SCREAMING_SNAKE_CASE__     :     Dict		    =       {
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

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

SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       {
    "num_train_timesteps": 1_51,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							Any:


			'''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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=None )			->							Tuple:


			'''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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							List[Any]:


			'''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__":
				SCREAMING_SNAKE_CASE__     :     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__     :     int		    =       parser.parse_args()
				SCREAMING_SNAKE_CASE__     :     int		    =       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__     :     Union[str, Any]		    =       IMAGENET_64_UNET_CONFIG
				elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
								SCREAMING_SNAKE_CASE__     :     List[str]		    =       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__     :     str		    =       None

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

				SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       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__     :     Any		    =       CD_SCHEDULER_CONFIG
				elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
								SCREAMING_SNAKE_CASE__     :     int		    =       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__     :     str		    =       CT_LSUN_256_SCHEDULER_CONFIG
				else:
								raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       CMStochasticIterativeScheduler(**scheduler_config)

				SCREAMING_SNAKE_CASE__     :     Tuple		    =       ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
				consistency_model.save_pretrained(args.dump_path)






def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if index == r:
						for j in range(SCREAMING_SNAKE_CASE ):
									print(data[j]       ,    end=' ' )
						print(' ' )
						return
			#  When no more elements are there to put in data[]
			if i >= n:
						return
			# current is included, put next at next location
			SCREAMING_SNAKE_CASE_								=						arr[i]
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    0       ,    SCREAMING_SNAKE_CASE       ,    0 )


if __name__ == "__main__":
				# Driver code to check the function above
				SCREAMING_SNAKE_CASE__     :     Dict		    =       [10, 20, 30, 40, 50]
				print_combination(arr, len(arr), 3)
				# This code is contributed by Ambuj sahu
