

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a		={
    """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""",
}

a		={
    """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""",
}

a		={
    """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""",
}

a		={
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

a		={
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

a		={
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Optional[int]:
	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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False			)  ->     Tuple:
	__lowerCamelCase				:				List[str]					       = checkpoint[F"{old_prefix}.in_layers.0.weight"]
	__lowerCamelCase				:				Any					       = checkpoint[F"{old_prefix}.in_layers.0.bias"]
	__lowerCamelCase				:				Optional[int]					       = checkpoint[F"{old_prefix}.in_layers.2.weight"]
	__lowerCamelCase				:				Tuple					       = checkpoint[F"{old_prefix}.in_layers.2.bias"]
	__lowerCamelCase				:				Optional[Any]					       = checkpoint[F"{old_prefix}.emb_layers.1.weight"]
	__lowerCamelCase				:				List[Any]					       = checkpoint[F"{old_prefix}.emb_layers.1.bias"]
	__lowerCamelCase				:				List[Any]					       = checkpoint[F"{old_prefix}.out_layers.0.weight"]
	__lowerCamelCase				:				str					       = checkpoint[F"{old_prefix}.out_layers.0.bias"]
	__lowerCamelCase				:				Optional[Any]					       = checkpoint[F"{old_prefix}.out_layers.3.weight"]
	__lowerCamelCase				:				Optional[int]					       = checkpoint[F"{old_prefix}.out_layers.3.bias"]

	if has_skip:
		__lowerCamelCase				:				str					       = checkpoint[F"{old_prefix}.skip_connection.weight"]
		__lowerCamelCase				:				Optional[int]					       = checkpoint[F"{old_prefix}.skip_connection.bias"]

	return new_checkpoint






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None			)  ->     Tuple:
	__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				List[str]					       = checkpoint[F"{old_prefix}.qkv.weight"].chunk(3	, dim=0			)
	__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = checkpoint[F"{old_prefix}.qkv.bias"].chunk(3	, dim=0			)

	__lowerCamelCase				:				int					       = checkpoint[F"{old_prefix}.norm.weight"]
	__lowerCamelCase				:				List[Any]					       = checkpoint[F"{old_prefix}.norm.bias"]

	__lowerCamelCase				:				Dict					       = weight_q.squeeze(-1			).squeeze(-1			)
	__lowerCamelCase				:				int					       = bias_q.squeeze(-1			).squeeze(-1			)
	__lowerCamelCase				:				Optional[Any]					       = weight_k.squeeze(-1			).squeeze(-1			)
	__lowerCamelCase				:				Any					       = bias_k.squeeze(-1			).squeeze(-1			)
	__lowerCamelCase				:				Tuple					       = weight_v.squeeze(-1			).squeeze(-1			)
	__lowerCamelCase				:				Any					       = bias_v.squeeze(-1			).squeeze(-1			)

	__lowerCamelCase				:				Union[str, Any]					       = (
	    checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1			).squeeze(-1			)
	)
	__lowerCamelCase				:				Union[str, Any]					       = checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1			).squeeze(-1			)

	return new_checkpoint






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     List[Any]:
	__lowerCamelCase				:				int					       = torch.load(lowerCamelCase__	, map_location='cpu'			)
	__lowerCamelCase				:				Optional[int]					       = {}

	__lowerCamelCase				:				Dict					       = checkpoint['time_embed.0.weight']
	__lowerCamelCase				:				Optional[Any]					       = checkpoint['time_embed.0.bias']
	__lowerCamelCase				:				Dict					       = checkpoint['time_embed.2.weight']
	__lowerCamelCase				:				int					       = checkpoint['time_embed.2.bias']

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

	__lowerCamelCase				:				str					       = checkpoint['input_blocks.0.0.weight']
	__lowerCamelCase				:				List[Any]					       = checkpoint['input_blocks.0.0.bias']

	__lowerCamelCase				:				Tuple					       = unet_config['down_block_types']
	__lowerCamelCase				:				Optional[Any]					       = unet_config['layers_per_block']
	__lowerCamelCase				:				Any					       = unet_config['attention_head_dim']
	__lowerCamelCase				:				Any					       = unet_config['block_out_channels']
	__lowerCamelCase				:				Union[str, Any]					       = 1
	__lowerCamelCase				:				Tuple					       = channels_list[0]

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

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

		if i != len(lowerCamelCase__			) - 1:
			__lowerCamelCase				:				Tuple					       = F"down_blocks.{i}.downsamplers.0"
			__lowerCamelCase				:				Any					       = F"input_blocks.{current_layer}.0"
			__lowerCamelCase				:				Any					       = convert_resnet(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)
			current_layer += 1

		__lowerCamelCase				:				Union[str, Any]					       = current_channels

	# hardcoded the mid-block for now
	__lowerCamelCase				:				Optional[Any]					       = 'mid_block.resnets.0'
	__lowerCamelCase				:				Any					       = 'middle_block.0'
	__lowerCamelCase				:				Any					       = convert_resnet(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)
	__lowerCamelCase				:				str					       = 'mid_block.attentions.0'
	__lowerCamelCase				:				Union[str, Any]					       = 'middle_block.1'
	__lowerCamelCase				:				str					       = convert_attention(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)
	__lowerCamelCase				:				Optional[Any]					       = 'mid_block.resnets.1'
	__lowerCamelCase				:				Optional[int]					       = 'middle_block.2'
	__lowerCamelCase				:				Union[str, Any]					       = convert_resnet(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)

	__lowerCamelCase				:				str					       = 0
	__lowerCamelCase				:				Union[str, Any]					       = unet_config['up_block_types']

	for i, layer_type in enumerate(lowerCamelCase__			):
		if layer_type == "ResnetUpsampleBlock2D":
			for j in range(layers_per_block + 1			):
				__lowerCamelCase				:				Optional[int]					       = F"up_blocks.{i}.resnets.{j}"
				__lowerCamelCase				:				str					       = F"output_blocks.{current_layer}.0"
				__lowerCamelCase				:				Union[str, Any]					       = convert_resnet(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, has_skip=lowerCamelCase__			)
				current_layer += 1

			if i != len(lowerCamelCase__			) - 1:
				__lowerCamelCase				:				List[str]					       = F"up_blocks.{i}.upsamplers.0"
				__lowerCamelCase				:				str					       = F"output_blocks.{current_layer-1}.1"
				__lowerCamelCase				:				Dict					       = convert_resnet(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)
		elif layer_type == "AttnUpBlock2D":
			for j in range(layers_per_block + 1			):
				__lowerCamelCase				:				Dict					       = F"up_blocks.{i}.resnets.{j}"
				__lowerCamelCase				:				int					       = F"output_blocks.{current_layer}.0"
				__lowerCamelCase				:				Optional[int]					       = convert_resnet(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, has_skip=lowerCamelCase__			)
				__lowerCamelCase				:				List[str]					       = F"up_blocks.{i}.attentions.{j}"
				__lowerCamelCase				:				Dict					       = F"output_blocks.{current_layer}.1"
				__lowerCamelCase				:				Dict					       = convert_attention(
				    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)
				current_layer += 1

			if i != len(lowerCamelCase__			) - 1:
				__lowerCamelCase				:				int					       = F"up_blocks.{i}.upsamplers.0"
				__lowerCamelCase				:				str					       = F"output_blocks.{current_layer-1}.2"
				__lowerCamelCase				:				Optional[int]					       = convert_resnet(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)

	__lowerCamelCase				:				Tuple					       = checkpoint['out.0.weight']
	__lowerCamelCase				:				Dict					       = checkpoint['out.0.bias']
	__lowerCamelCase				:				Optional[int]					       = checkpoint['out.2.weight']
	__lowerCamelCase				:				List[str]					       = checkpoint['out.2.bias']

	return new_checkpoint


if __name__ == "__main__":
					a		=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		=parser.parse_args()
					a		=strabool(args.class_cond)

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

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

					if not args.class_cond:
										a		=None

					a		=con_pt_to_diffuser(args.unet_path, unet_config)

					a		=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		=CD_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
										a		=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		=CT_LSUN_256_SCHEDULER_CONFIG
					else:
										raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

					a		=CMStochasticIterativeScheduler(**scheduler_config)

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








'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


__snake_case							      =     {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     ['''DPTFeatureExtractor''']
		__snake_case							      =     ['''DPTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DPTForDepthEstimation''',
		    '''DPTForSemanticSegmentation''',
		    '''DPTModel''',
		    '''DPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		__snake_case							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)