





import argparse
import json

import torch

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






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str    ,      UpperCamelCase__:							List[str]=1			):
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
		else:
				return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Tuple    ,      UpperCamelCase__:							int=0			):
		SCREAMING_SNAKE_CASE__								=					[]
		for old_item in old_list:
				SCREAMING_SNAKE_CASE__								=					old_item
				SCREAMING_SNAKE_CASE__								=					new_item.replace("""block."""    ,      """resnets."""			)
				SCREAMING_SNAKE_CASE__								=					new_item.replace("""conv_shorcut"""    ,      """conv1"""			)
				SCREAMING_SNAKE_CASE__								=					new_item.replace("""in_shortcut"""    ,      """conv_shortcut"""			)
				SCREAMING_SNAKE_CASE__								=					new_item.replace("""temb_proj"""    ,      """time_emb_proj"""			)

				SCREAMING_SNAKE_CASE__								=					shave_segments(UpperCamelCase__    ,      n_shave_prefix_segments=UpperCamelCase__			)

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

		return mapping






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							List[Any]=0    ,      UpperCamelCase__:							Optional[Any]=False			):
		SCREAMING_SNAKE_CASE__								=					[]
		for old_item in old_list:
				SCREAMING_SNAKE_CASE__								=					old_item

				# In `model.mid`, the layer is called `attn`.
				if not in_mid:
						SCREAMING_SNAKE_CASE__								=					new_item.replace("""attn"""    ,      """attentions"""			)
				SCREAMING_SNAKE_CASE__								=					new_item.replace(""".k."""    ,      """.key."""			)
				SCREAMING_SNAKE_CASE__								=					new_item.replace(""".v."""    ,      """.value."""			)
				SCREAMING_SNAKE_CASE__								=					new_item.replace(""".q."""    ,      """.query."""			)

				SCREAMING_SNAKE_CASE__								=					new_item.replace("""proj_out"""    ,      """proj_attn"""			)
				SCREAMING_SNAKE_CASE__								=					new_item.replace("""norm"""    ,      """group_norm"""			)

				SCREAMING_SNAKE_CASE__								=					shave_segments(UpperCamelCase__    ,      n_shave_prefix_segments=UpperCamelCase__			)
				mapping.append({"""old""": old_item, """new""": new_item}			)

		return mapping






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							Union[str, Any]=None    ,      UpperCamelCase__:							List[Any]=None    ,      UpperCamelCase__:							int=None			):
		assert isinstance(UpperCamelCase__    ,      UpperCamelCase__			), "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():
						SCREAMING_SNAKE_CASE__								=					old_checkpoint[path]
						SCREAMING_SNAKE_CASE__								=					old_tensor.shape[0] // 3

						SCREAMING_SNAKE_CASE__								=					(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

						SCREAMING_SNAKE_CASE__								=					old_tensor.shape[0] // config.get("""num_head_channels"""    ,      1			) // 3

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

						SCREAMING_SNAKE_CASE__								=					query.reshape(UpperCamelCase__			).squeeze()
						SCREAMING_SNAKE_CASE__								=					key.reshape(UpperCamelCase__			).squeeze()
						SCREAMING_SNAKE_CASE__								=					value.reshape(UpperCamelCase__			).squeeze()

		for path in paths:
				SCREAMING_SNAKE_CASE__								=					path["""new"""]

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				SCREAMING_SNAKE_CASE__								=					new_path.replace("""down."""    ,      """down_blocks."""			)
				SCREAMING_SNAKE_CASE__								=					new_path.replace("""up."""    ,      """up_blocks."""			)

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

				if "attentions" in new_path:
						SCREAMING_SNAKE_CASE__								=					old_checkpoint[path["""old"""]].squeeze()
				else:
						SCREAMING_SNAKE_CASE__								=					old_checkpoint[path["""old"""]]






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							Tuple			):
		SCREAMING_SNAKE_CASE__								=					{}

		SCREAMING_SNAKE_CASE__								=					checkpoint["""temb.dense.0.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""temb.dense.0.bias"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""temb.dense.1.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""temb.dense.1.bias"""]

		SCREAMING_SNAKE_CASE__								=					checkpoint["""norm_out.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""norm_out.bias"""]

		SCREAMING_SNAKE_CASE__								=					checkpoint["""conv_in.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""conv_in.bias"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""conv_out.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""conv_out.bias"""]

		SCREAMING_SNAKE_CASE__								=					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """down""" in layer}			)
		SCREAMING_SNAKE_CASE__								=					{
		    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)
		}

		SCREAMING_SNAKE_CASE__								=					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """up""" in layer}			)
		SCREAMING_SNAKE_CASE__								=					{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)}

		for i in range(UpperCamelCase__			):
				SCREAMING_SNAKE_CASE__								=					(i - 1) // (config["""layers_per_block"""] + 1)

				if any("""downsample""" in layer for layer in down_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					checkpoint[
						    f'''down.{i}.downsample.op.weight'''
						]
						SCREAMING_SNAKE_CASE__								=					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]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """block""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
						    for layer_id in range(UpperCamelCase__			)
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""]			):
										SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(blocks[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__			)

				if any("""attn""" in layer for layer in down_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """attn""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
						    for layer_id in range(UpperCamelCase__			)
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""]			):
										SCREAMING_SNAKE_CASE__								=					renew_attention_paths(attns[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      config=UpperCamelCase__			)

		SCREAMING_SNAKE_CASE__								=					[key for key in checkpoint if """mid.block_1""" in key]
		SCREAMING_SNAKE_CASE__								=					[key for key in checkpoint if """mid.block_2""" in key]
		SCREAMING_SNAKE_CASE__								=					[key for key in checkpoint if """mid.attn_1""" in key]

		# Mid new 2
		SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(UpperCamelCase__			)
		assign_to_checkpoint(
		    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]    ,      )

		SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(UpperCamelCase__			)
		assign_to_checkpoint(
		    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]    ,      )

		SCREAMING_SNAKE_CASE__								=					renew_attention_paths(UpperCamelCase__    ,      in_mid=UpperCamelCase__			)
		assign_to_checkpoint(
		    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]    ,      )

		for i in range(UpperCamelCase__			):
				SCREAMING_SNAKE_CASE__								=					num_up_blocks - 1 - i

				if any("""upsample""" in layer for layer in up_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					checkpoint[
						    f'''up.{i}.upsample.conv.weight'''
						]
						SCREAMING_SNAKE_CASE__								=					checkpoint[f'''up.{i}.upsample.conv.bias''']

				if any("""block""" in layer for layer in up_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """block""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""] + 1			):
										SCREAMING_SNAKE_CASE__								=					{"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
										SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(blocks[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[replace_indices]			)

				if any("""attn""" in layer for layer in up_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """attn""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""] + 1			):
										SCREAMING_SNAKE_CASE__								=					{"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
										SCREAMING_SNAKE_CASE__								=					renew_attention_paths(attns[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[replace_indices]			)

		SCREAMING_SNAKE_CASE__								=					{k.replace("""mid_new_2"""    ,      """mid_block"""			): v for k, v in new_checkpoint.items()}
		return new_checkpoint






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Any    ,      UpperCamelCase__:							int			):
		SCREAMING_SNAKE_CASE__								=					{}

		SCREAMING_SNAKE_CASE__								=					checkpoint["""encoder.norm_out.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""encoder.norm_out.bias"""]

		SCREAMING_SNAKE_CASE__								=					checkpoint["""encoder.conv_in.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""encoder.conv_in.bias"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""encoder.conv_out.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""encoder.conv_out.bias"""]

		SCREAMING_SNAKE_CASE__								=					checkpoint["""decoder.norm_out.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""decoder.norm_out.bias"""]

		SCREAMING_SNAKE_CASE__								=					checkpoint["""decoder.conv_in.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""decoder.conv_in.bias"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""decoder.conv_out.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""decoder.conv_out.bias"""]

		SCREAMING_SNAKE_CASE__								=					len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """down""" in layer}			)
		SCREAMING_SNAKE_CASE__								=					{
		    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)
		}

		SCREAMING_SNAKE_CASE__								=					len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """up""" in layer}			)
		SCREAMING_SNAKE_CASE__								=					{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)}

		for i in range(UpperCamelCase__			):
				SCREAMING_SNAKE_CASE__								=					(i - 1) // (config["""layers_per_block"""] + 1)

				if any("""downsample""" in layer for layer in down_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					checkpoint[
						    f'''encoder.down.{i}.downsample.conv.weight'''
						]
						SCREAMING_SNAKE_CASE__								=					checkpoint[
						    f'''encoder.down.{i}.downsample.conv.bias'''
						]

				if any("""block""" in layer for layer in down_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """block""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
						    for layer_id in range(UpperCamelCase__			)
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""]			):
										SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(blocks[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__			)

				if any("""attn""" in layer for layer in down_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """attn""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
						    for layer_id in range(UpperCamelCase__			)
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""]			):
										SCREAMING_SNAKE_CASE__								=					renew_attention_paths(attns[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      config=UpperCamelCase__			)

		SCREAMING_SNAKE_CASE__								=					[key for key in checkpoint if """mid.block_1""" in key]
		SCREAMING_SNAKE_CASE__								=					[key for key in checkpoint if """mid.block_2""" in key]
		SCREAMING_SNAKE_CASE__								=					[key for key in checkpoint if """mid.attn_1""" in key]

		# Mid new 2
		SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(UpperCamelCase__			)
		assign_to_checkpoint(
		    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]    ,      )

		SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(UpperCamelCase__			)
		assign_to_checkpoint(
		    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]    ,      )

		SCREAMING_SNAKE_CASE__								=					renew_attention_paths(UpperCamelCase__    ,      in_mid=UpperCamelCase__			)
		assign_to_checkpoint(
		    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]    ,      )

		for i in range(UpperCamelCase__			):
				SCREAMING_SNAKE_CASE__								=					num_up_blocks - 1 - i

				if any("""upsample""" in layer for layer in up_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					checkpoint[
						    f'''decoder.up.{i}.upsample.conv.weight'''
						]
						SCREAMING_SNAKE_CASE__								=					checkpoint[
						    f'''decoder.up.{i}.upsample.conv.bias'''
						]

				if any("""block""" in layer for layer in up_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """block""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""] + 1			):
										SCREAMING_SNAKE_CASE__								=					{"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
										SCREAMING_SNAKE_CASE__								=					renew_resnet_paths(blocks[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[replace_indices]			)

				if any("""attn""" in layer for layer in up_blocks[i]			):
						SCREAMING_SNAKE_CASE__								=					len(
						    {""".""".join(shave_segments(UpperCamelCase__    ,      3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """attn""" in layer}			)
						SCREAMING_SNAKE_CASE__								=					{
						    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(UpperCamelCase__			)
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""] + 1			):
										SCREAMING_SNAKE_CASE__								=					{"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
										SCREAMING_SNAKE_CASE__								=					renew_attention_paths(attns[j]			)
										assign_to_checkpoint(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      additional_replacements=[replace_indices]			)

		SCREAMING_SNAKE_CASE__								=					{k.replace("""mid_new_2"""    ,      """mid_block"""			): v for k, v in new_checkpoint.items()}
		SCREAMING_SNAKE_CASE__								=					checkpoint["""quant_conv.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""quant_conv.bias"""]
		if "quantize.embedding.weight" in checkpoint:
				SCREAMING_SNAKE_CASE__								=					checkpoint["""quantize.embedding.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""post_quant_conv.weight"""]
		SCREAMING_SNAKE_CASE__								=					checkpoint["""post_quant_conv.bias"""]

		return new_checkpoint


if __name__ == "__main__":
	_lowerCamelCase       =	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.')

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

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

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

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

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

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase       =	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase       =	[
	    'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTSanJapaneseForConditionalGeneration',
	    'GPTSanJapaneseModel',
	    'GPTSanJapanesePreTrainedModel',
	]
	_lowerCamelCase       =	[
	    'GPTSanJapaneseTokenizer',
	]


if TYPE_CHECKING:
	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptsan_japanese import (
		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTSanJapaneseForConditionalGeneration,
		    GPTSanJapaneseModel,
		    GPTSanJapanesePreTrainedModel,
		)
		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
	import sys

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