
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=1 )     ->       Tuple:


		'''simple docstring'''


		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
		else:
				return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=0 )     ->       Dict:


		'''simple docstring'''


		snake_case     :					Tuple     =	[]
		for old_item in old_list:
				snake_case     :					Any     =	old_item
				snake_case     :					Optional[int]     =	new_item.replace('''block.'''   ,					'''resnets.''' )
				snake_case     :					List[str]     =	new_item.replace('''conv_shorcut'''   ,					'''conv1''' )
				snake_case     :					Optional[int]     =	new_item.replace('''in_shortcut'''   ,					'''conv_shortcut''' )
				snake_case     :					Any     =	new_item.replace('''temb_proj'''   ,					'''time_emb_proj''' )

				snake_case     :					Any     =	shave_segments(lowercase__   ,					n_shave_prefix_segments=lowercase__ )

				mapping.append({'''old''': old_item, '''new''': new_item} )

		return mapping




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=0   ,					SCREAMING_SNAKE_CASE__=False )     ->       List[str]:


		'''simple docstring'''


		snake_case     :					Dict     =	[]
		for old_item in old_list:
				snake_case     :					Any     =	old_item

				# In `model.mid`, the layer is called `attn`.
				if not in_mid:
						snake_case     :					Tuple     =	new_item.replace('''attn'''   ,					'''attentions''' )
				snake_case     :					List[Any]     =	new_item.replace('''.k.'''   ,					'''.key.''' )
				snake_case     :					Optional[int]     =	new_item.replace('''.v.'''   ,					'''.value.''' )
				snake_case     :					Union[str, Any]     =	new_item.replace('''.q.'''   ,					'''.query.''' )

				snake_case     :					List[str]     =	new_item.replace('''proj_out'''   ,					'''proj_attn''' )
				snake_case     :					Union[str, Any]     =	new_item.replace('''norm'''   ,					'''group_norm''' )

				snake_case     :					Any     =	shave_segments(lowercase__   ,					n_shave_prefix_segments=lowercase__ )
				mapping.append({'''old''': old_item, '''new''': new_item} )

		return mapping




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None )     ->       int:


		'''simple docstring'''


		assert isinstance(lowercase__   ,					lowercase__ ), "Paths should be a list of dicts containing 'old' and 'new' keys."

		if attention_paths_to_split is not None:
				if config is None:
						raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.''' )

				for path, path_map in attention_paths_to_split.items():
						snake_case     :					List[Any]     =	old_checkpoint[path]
						snake_case     :					int     =	old_tensor.shape[0] // 3

						snake_case     :					Optional[Any]     =	(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

						snake_case     :					Optional[Any]     =	old_tensor.shape[0] // config.get('''num_head_channels'''   ,					1 ) // 3

						snake_case     :					Optional[int]     =	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
						snake_case ,snake_case ,snake_case     :					str     =	old_tensor.split(channels // num_heads   ,					dim=1 )

						snake_case     :					Any     =	query.reshape(lowercase__ ).squeeze()
						snake_case     :					int     =	key.reshape(lowercase__ ).squeeze()
						snake_case     :					List[Any]     =	value.reshape(lowercase__ ).squeeze()

		for path in paths:
				snake_case     :					Tuple     =	path['''new''']

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				snake_case     :					List[Any]     =	new_path.replace('''down.'''   ,					'''down_blocks.''' )
				snake_case     :					str     =	new_path.replace('''up.'''   ,					'''up_blocks.''' )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								snake_case     :					Any     =	new_path.replace(replacement['''old''']   ,					replacement['''new'''] )

				if "attentions" in new_path:
						snake_case     :					Optional[int]     =	old_checkpoint[path['''old''']].squeeze()
				else:
						snake_case     :					str     =	old_checkpoint[path['''old''']]




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       str:


		'''simple docstring'''


		snake_case     :					Tuple     =	{}

		snake_case     :					Tuple     =	checkpoint['''temb.dense.0.weight''']
		snake_case     :					Dict     =	checkpoint['''temb.dense.0.bias''']
		snake_case     :					str     =	checkpoint['''temb.dense.1.weight''']
		snake_case     :					Dict     =	checkpoint['''temb.dense.1.bias''']

		snake_case     :					str     =	checkpoint['''norm_out.weight''']
		snake_case     :					List[str]     =	checkpoint['''norm_out.bias''']

		snake_case     :					List[Any]     =	checkpoint['''conv_in.weight''']
		snake_case     :					Tuple     =	checkpoint['''conv_in.bias''']
		snake_case     :					List[Any]     =	checkpoint['''conv_out.weight''']
		snake_case     :					List[Any]     =	checkpoint['''conv_out.bias''']

		snake_case     :					Dict     =	len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''down''' in layer} )
		snake_case     :					Optional[Any]     =	{
		    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(lowercase__ )
		}

		snake_case     :					Union[str, Any]     =	len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''up''' in layer} )
		snake_case     :					Optional[Any]     =	{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(lowercase__ )}

		for i in range(lowercase__ ):
				snake_case     :					Any     =	(i - 1) // (config['''layers_per_block'''] + 1)

				if any('''downsample''' in layer for layer in down_blocks[i] ):
						snake_case     :					str     =	checkpoint[
						    F'down.{i}.downsample.op.weight'
						]
						snake_case     :					Dict     =	checkpoint[F'down.{i}.downsample.op.bias']
				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

				if any('''block''' in layer for layer in down_blocks[i] ):
						snake_case     :					int     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					2 ).split('''.''' )[:2] ) for layer in down_blocks[i] if '''block''' in layer} )
						snake_case     :					int     =	{
						    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
						    for layer_id in range(lowercase__ )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] ):
										snake_case     :					int     =	renew_resnet_paths(blocks[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__ )

				if any('''attn''' in layer for layer in down_blocks[i] ):
						snake_case     :					int     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					2 ).split('''.''' )[:2] ) for layer in down_blocks[i] if '''attn''' in layer} )
						snake_case     :					List[str]     =	{
						    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
						    for layer_id in range(lowercase__ )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] ):
										snake_case     :					str     =	renew_attention_paths(attns[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__   ,					config=lowercase__ )

		snake_case     :					Tuple     =	[key for key in checkpoint if '''mid.block_1''' in key]
		snake_case     :					List[Any]     =	[key for key in checkpoint if '''mid.block_2''' in key]
		snake_case     :					List[Any]     =	[key for key in checkpoint if '''mid.attn_1''' in key]

		# Mid new 2
		snake_case     :					Dict     =	renew_resnet_paths(lowercase__ )
		assign_to_checkpoint(
		    lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]   ,					)

		snake_case     :					Dict     =	renew_resnet_paths(lowercase__ )
		assign_to_checkpoint(
		    lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]   ,					)

		snake_case     :					List[Any]     =	renew_attention_paths(lowercase__   ,					in_mid=lowercase__ )
		assign_to_checkpoint(
		    lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]   ,					)

		for i in range(lowercase__ ):
				snake_case     :					int     =	num_up_blocks - 1 - i

				if any('''upsample''' in layer for layer in up_blocks[i] ):
						snake_case     :					int     =	checkpoint[
						    F'up.{i}.upsample.conv.weight'
						]
						snake_case     :					List[Any]     =	checkpoint[F'up.{i}.upsample.conv.bias']

				if any('''block''' in layer for layer in up_blocks[i] ):
						snake_case     :					int     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					2 ).split('''.''' )[:2] ) for layer in up_blocks[i] if '''block''' in layer} )
						snake_case     :					int     =	{
						    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(lowercase__ )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] + 1 ):
										snake_case     :					Tuple     =	{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
										snake_case     :					Dict     =	renew_resnet_paths(blocks[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[replace_indices] )

				if any('''attn''' in layer for layer in up_blocks[i] ):
						snake_case     :					Optional[int]     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					2 ).split('''.''' )[:2] ) for layer in up_blocks[i] if '''attn''' in layer} )
						snake_case     :					List[Any]     =	{
						    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(lowercase__ )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] + 1 ):
										snake_case     :					Optional[Any]     =	{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
										snake_case     :					int     =	renew_attention_paths(attns[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[replace_indices] )

		snake_case     :					Dict     =	{k.replace('''mid_new_2'''   ,					'''mid_block''' ): v for k, v in new_checkpoint.items()}
		return new_checkpoint




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


		snake_case     :					str     =	{}

		snake_case     :					Dict     =	checkpoint['''encoder.norm_out.weight''']
		snake_case     :					int     =	checkpoint['''encoder.norm_out.bias''']

		snake_case     :					Any     =	checkpoint['''encoder.conv_in.weight''']
		snake_case     :					List[str]     =	checkpoint['''encoder.conv_in.bias''']
		snake_case     :					str     =	checkpoint['''encoder.conv_out.weight''']
		snake_case     :					Dict     =	checkpoint['''encoder.conv_out.bias''']

		snake_case     :					Tuple     =	checkpoint['''decoder.norm_out.weight''']
		snake_case     :					List[str]     =	checkpoint['''decoder.norm_out.bias''']

		snake_case     :					Optional[Any]     =	checkpoint['''decoder.conv_in.weight''']
		snake_case     :					Optional[int]     =	checkpoint['''decoder.conv_in.bias''']
		snake_case     :					List[str]     =	checkpoint['''decoder.conv_out.weight''']
		snake_case     :					Optional[Any]     =	checkpoint['''decoder.conv_out.bias''']

		snake_case     :					Tuple     =	len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in checkpoint if '''down''' in layer} )
		snake_case     :					Union[str, Any]     =	{
		    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(lowercase__ )
		}

		snake_case     :					Tuple     =	len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in checkpoint if '''up''' in layer} )
		snake_case     :					int     =	{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(lowercase__ )}

		for i in range(lowercase__ ):
				snake_case     :					Tuple     =	(i - 1) // (config['''layers_per_block'''] + 1)

				if any('''downsample''' in layer for layer in down_blocks[i] ):
						snake_case     :					str     =	checkpoint[
						    F'encoder.down.{i}.downsample.conv.weight'
						]
						snake_case     :					Any     =	checkpoint[
						    F'encoder.down.{i}.downsample.conv.bias'
						]

				if any('''block''' in layer for layer in down_blocks[i] ):
						snake_case     :					int     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					3 ).split('''.''' )[:3] ) for layer in down_blocks[i] if '''block''' in layer} )
						snake_case     :					Optional[Any]     =	{
						    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
						    for layer_id in range(lowercase__ )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] ):
										snake_case     :					Optional[Any]     =	renew_resnet_paths(blocks[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__ )

				if any('''attn''' in layer for layer in down_blocks[i] ):
						snake_case     :					Tuple     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					3 ).split('''.''' )[:3] ) for layer in down_blocks[i] if '''attn''' in layer} )
						snake_case     :					int     =	{
						    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
						    for layer_id in range(lowercase__ )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] ):
										snake_case     :					Any     =	renew_attention_paths(attns[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__   ,					config=lowercase__ )

		snake_case     :					Dict     =	[key for key in checkpoint if '''mid.block_1''' in key]
		snake_case     :					int     =	[key for key in checkpoint if '''mid.block_2''' in key]
		snake_case     :					List[str]     =	[key for key in checkpoint if '''mid.attn_1''' in key]

		# Mid new 2
		snake_case     :					str     =	renew_resnet_paths(lowercase__ )
		assign_to_checkpoint(
		    lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]   ,					)

		snake_case     :					str     =	renew_resnet_paths(lowercase__ )
		assign_to_checkpoint(
		    lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]   ,					)

		snake_case     :					str     =	renew_attention_paths(lowercase__   ,					in_mid=lowercase__ )
		assign_to_checkpoint(
		    lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]   ,					)

		for i in range(lowercase__ ):
				snake_case     :					List[Any]     =	num_up_blocks - 1 - i

				if any('''upsample''' in layer for layer in up_blocks[i] ):
						snake_case     :					List[Any]     =	checkpoint[
						    F'decoder.up.{i}.upsample.conv.weight'
						]
						snake_case     :					Any     =	checkpoint[
						    F'decoder.up.{i}.upsample.conv.bias'
						]

				if any('''block''' in layer for layer in up_blocks[i] ):
						snake_case     :					Tuple     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					3 ).split('''.''' )[:3] ) for layer in up_blocks[i] if '''block''' in layer} )
						snake_case     :					Any     =	{
						    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(lowercase__ )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] + 1 ):
										snake_case     :					Optional[int]     =	{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
										snake_case     :					Optional[Any]     =	renew_resnet_paths(blocks[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[replace_indices] )

				if any('''attn''' in layer for layer in up_blocks[i] ):
						snake_case     :					Optional[Any]     =	len(
						    {'''.'''.join(shave_segments(lowercase__   ,					3 ).split('''.''' )[:3] ) for layer in up_blocks[i] if '''attn''' in layer} )
						snake_case     :					Tuple     =	{
						    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(lowercase__ )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] + 1 ):
										snake_case     :					List[Any]     =	{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
										snake_case     :					Union[str, Any]     =	renew_attention_paths(attns[j] )
										assign_to_checkpoint(lowercase__   ,					lowercase__   ,					lowercase__   ,					additional_replacements=[replace_indices] )

		snake_case     :					Tuple     =	{k.replace('''mid_new_2'''   ,					'''mid_block''' ): v for k, v in new_checkpoint.items()}
		snake_case     :					Dict     =	checkpoint['''quant_conv.weight''']
		snake_case     :					str     =	checkpoint['''quant_conv.bias''']
		if "quantize.embedding.weight" in checkpoint:
				snake_case     :					List[str]     =	checkpoint['''quantize.embedding.weight''']
		snake_case     :					Optional[Any]     =	checkpoint['''post_quant_conv.weight''']
		snake_case     :					int     =	checkpoint['''post_quant_conv.bias''']

		return new_checkpoint


if __name__ == "__main__":
						lowercase__						=			argparse.ArgumentParser()

						parser.add_argument(
						    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
						)

						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help="The config json file corresponding to the architecture.",
						)

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						lowercase__						=			parser.parse_args()
						lowercase__						=			torch.load(args.checkpoint_path)

						with open(args.config_file) as f:
												lowercase__						=			json.loads(f.read())

						# unet case
						lowercase__						=			{key.split(".")[0] for key in checkpoint.keys()}
						if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
												lowercase__						=			convert_vq_autoenc_checkpoint(checkpoint, config)
						else:
												lowercase__						=			convert_ddpm_checkpoint(checkpoint, config)

						if "ddpm" in config:
												del config["ddpm"]

						if config["_class_name"] == "VQModel":
												lowercase__						=			VQModel(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						elif config["_class_name"] == "AutoencoderKL":
												lowercase__						=			AutoencoderKL(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						else:
												lowercase__						=			UNetaDModel(**config)
												model.load_state_dict(converted_checkpoint)

												lowercase__						=			DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

												lowercase__						=			DDPMPipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)




'''simple docstring'''
from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase__						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


lowercase__						=			"\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"






class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: UNetaDModel     ,  UpperCamelCase__							: CMStochasticIterativeScheduler			)	-> None:






				"""simple docstring"""







				super().__init__()

				self.register_modules(
				    unet=UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  )

				snake_case     :					str     =	None



		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: str=0			)	-> int:






				"""simple docstring"""







				if is_accelerate_available() and is_accelerate_version('''>='''     ,  '''0.14.0'''			):
						from accelerate import cpu_offload
				else:
						raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''			)

				snake_case     :					Optional[Any]     =	torch.device(f'cuda:{gpu_id}'			)

				if self.device.type != "cpu":
						self.to('''cpu'''     ,  silence_dtype_warnings=UpperCamelCase__			)
						torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

				for cpu_offloaded_model in [self.unet]:
						cpu_offload(UpperCamelCase__     ,  UpperCamelCase__			)

				if self.safety_checker is not None:
						cpu_offload(self.safety_checker     ,  execution_device=UpperCamelCase__     ,  offload_buffers=UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: int=0			)	-> Dict:






				"""simple docstring"""







				if is_accelerate_available() and is_accelerate_version('''>='''     ,  '''0.17.0.dev0'''			):
						from accelerate import cpu_offload_with_hook
				else:
						raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''			)

				snake_case     :					Union[str, Any]     =	torch.device(f'cuda:{gpu_id}'			)

				if self.device.type != "cpu":
						self.to('''cpu'''     ,  silence_dtype_warnings=UpperCamelCase__			)
						torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

				snake_case     :					List[Any]     =	None
				for cpu_offloaded_model in [self.unet]:
						snake_case ,snake_case     :					Union[str, Any]     =	cpu_offload_with_hook(UpperCamelCase__     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)

				if self.safety_checker is not None:
						snake_case ,snake_case     :					Optional[Any]     =	cpu_offload_with_hook(self.safety_checker     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)

				# We'll offload the last model manually.
				snake_case     :					Optional[Any]     =	hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 			lowerCAmelCase							(			self							: int			)	-> List[str]:






				"""simple docstring"""







				if not hasattr(self.unet     ,  '''_hf_hook'''			):
						return self.device
				for module in self.unet.modules():
						if (
						    hasattr(UpperCamelCase__     ,  '''_hf_hook'''			)
						    and hasattr(module._hf_hook     ,  '''execution_device'''			)
						    and module._hf_hook.execution_device is not None
						):
								return torch.device(module._hf_hook.execution_device			)
				return self.device



		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Optional[int]=None			)	-> Tuple:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	(batch_size, num_channels, height, width)
				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and len(UpperCamelCase__			) != batch_size:
						raise ValueError(
						    f'You have passed a list of generators of length {len(UpperCamelCase__			)}, but requested an effective batch'
						    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'			)

				if latents is None:
						snake_case     :					List[Any]     =	randn_tensor(UpperCamelCase__     ,  generator=UpperCamelCase__     ,  device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)
				else:
						snake_case     :					Tuple     =	latents.to(device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)

				# scale the initial noise by the standard deviation required by the scheduler
				snake_case     :					str     =	latents * self.scheduler.init_noise_sigma
				return latents



		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: torch.FloatTensor     ,  UpperCamelCase__							: str = "pil"			)	-> Union[str, Any]:






				"""simple docstring"""







				if output_type not in ["pt", "np", "pil"]:
						raise ValueError(
						    f'output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'			)

				# Equivalent to diffusers.VaeImageProcessor.denormalize
				snake_case     :					Any     =	(sample / 2 + 0.5).clamp(0     ,  1			)
				if output_type == "pt":
						return sample

				# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
				snake_case     :					Optional[int]     =	sample.cpu().permute(0     ,  2     ,  3     ,  1			).numpy()
				if output_type == "np":
						return sample

				# Output_type must be 'pil'
				snake_case     :					Tuple     =	self.numpy_to_pil(UpperCamelCase__			)
				return sample



		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: List[str]=None			)	-> List[str]:






				"""simple docstring"""







				if self.unet.config.num_class_embeds is not None:
						if isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
								snake_case     :					str     =	torch.tensor(UpperCamelCase__     ,  dtype=torch.int			)
						elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
								assert batch_size == 1, "Batch size must be 1 if classes is an int"
								snake_case     :					Dict     =	torch.tensor([class_labels]     ,  dtype=torch.int			)
						elif class_labels is None:
								# Randomly generate batch_size class labels
								# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
								snake_case     :					List[Any]     =	torch.randint(0     ,  self.unet.config.num_class_embeds     ,  size=(batch_size,)			)
						snake_case     :					Dict     =	class_labels.to(UpperCamelCase__			)
				else:
						snake_case     :					int     =	None
				return class_labels



		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Union[str, Any]			)	-> Union[str, Any]:






				"""simple docstring"""







				if num_inference_steps is None and timesteps is None:
						raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''			)

				if num_inference_steps is not None and timesteps is not None:
						logger.warning(
						    f'Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'
						    ''' `timesteps` will be used over `num_inference_steps`.'''			)

				if latents is not None:
						snake_case     :					List[Any]     =	(batch_size, 3, img_size, img_size)
						if latents.shape != expected_shape:
								raise ValueError(f'The shape of latents is {latents.shape} but is expected to be {expected_shape}.'			)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(UpperCamelCase__     ,  UpperCamelCase__			) or callback_steps <= 0)
				):
						raise ValueError(
						    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
						    f' {type(UpperCamelCase__			)}.'			)



		@torch.no_grad()
		@replace_example_docstring(UpperCamelCase__			)
		def __call__(			self							: Optional[int]     ,  UpperCamelCase__							: int = 1     ,  UpperCamelCase__							: Optional[Union[torch.Tensor, List[int], int]] = None     ,  UpperCamelCase__							: int = 1     ,  UpperCamelCase__							: List[int] = None     ,  UpperCamelCase__							: Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  UpperCamelCase__							: Optional[torch.FloatTensor] = None     ,  UpperCamelCase__							: Optional[str] = "pil"     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,  UpperCamelCase__							: int = 1     ,  )	-> str:






				"""simple docstring"""







				snake_case     :					Optional[int]     =	self.unet.config.sample_size
				snake_case     :					List[Any]     =	self._execution_device

				# 1. Check inputs
				self.check_inputs(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)

				# 2. Prepare image latents
				# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
				snake_case     :					List[str]     =	self.prepare_latents(
				    batch_size=UpperCamelCase__     ,  num_channels=self.unet.config.in_channels     ,  height=UpperCamelCase__     ,  width=UpperCamelCase__     ,  dtype=self.unet.dtype     ,  device=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  latents=UpperCamelCase__     ,  )

				# 3. Handle class_labels for class-conditional models
				snake_case     :					List[str]     =	self.prepare_class_labels(UpperCamelCase__     ,  UpperCamelCase__     ,  class_labels=UpperCamelCase__			)

				# 4. Prepare timesteps
				if timesteps is not None:
						self.scheduler.set_timesteps(timesteps=UpperCamelCase__     ,  device=UpperCamelCase__			)
						snake_case     :					Union[str, Any]     =	self.scheduler.timesteps
						snake_case     :					Optional[int]     =	len(UpperCamelCase__			)
				else:
						self.scheduler.set_timesteps(UpperCamelCase__			)
						snake_case     :					List[str]     =	self.scheduler.timesteps

				# 5. Denoising loop
				# Multistep sampling: implements Algorithm 1 in the paper
				with self.progress_bar(total=UpperCamelCase__			) as progress_bar:
						for i, t in enumerate(UpperCamelCase__			):
								snake_case     :					Any     =	self.scheduler.scale_model_input(UpperCamelCase__     ,  UpperCamelCase__			)
								snake_case     :					int     =	self.unet(UpperCamelCase__     ,  UpperCamelCase__     ,  class_labels=UpperCamelCase__     ,  return_dict=UpperCamelCase__			)[0]

								snake_case     :					Dict     =	self.scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  generator=UpperCamelCase__			)[0]

								# call the callback, if provided
								progress_bar.update()
								if callback is not None and i % callback_steps == 0:
										callback(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)

        # 6. Post-process image sample
				snake_case     :					Any     =	self.postprocess_image(UpperCamelCase__     ,  output_type=UpperCamelCase__			)

				# Offload last model to CPU
				if hasattr(self     ,  '''final_offload_hook'''			) and self.final_offload_hook is not None:
						self.final_offload_hook.offload()

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=UpperCamelCase__			)


