
'''simple docstring'''

import argparse
import json

import torch

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







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=1	) ->      int:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('''.'''	)[n_shave_prefix_segments:]	)
		else:
				return ".".join(path.split('''.'''	)[:n_shave_prefix_segments]	)







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=0	) ->      Dict:
		_UpperCamelCase       : Optional[int]    =      []
		for old_item in old_list:
				_UpperCamelCase       : str    =      old_item
				_UpperCamelCase       : Union[str, Any]    =      new_item.replace('''block.'''			,'''resnets.'''	)
				_UpperCamelCase       : Union[str, Any]    =      new_item.replace('''conv_shorcut'''			,'''conv1'''	)
				_UpperCamelCase       : Optional[int]    =      new_item.replace('''in_shortcut'''			,'''conv_shortcut'''	)
				_UpperCamelCase       : Tuple    =      new_item.replace('''temb_proj'''			,'''time_emb_proj'''	)

				_UpperCamelCase       : Union[str, Any]    =      shave_segments(UpperCamelCase			,n_shave_prefix_segments=UpperCamelCase	)

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

		return mapping







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=0			,UpperCamelCase=False	) ->      str:
		_UpperCamelCase       : Union[str, Any]    =      []
		for old_item in old_list:
				_UpperCamelCase       : List[str]    =      old_item

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

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

				_UpperCamelCase       : List[Any]    =      shave_segments(UpperCamelCase			,n_shave_prefix_segments=UpperCamelCase	)
				mapping.append({'''old''': old_item, '''new''': new_item}	)

		return mapping







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase=None			,UpperCamelCase=None			,UpperCamelCase=None	) ->      List[str]:
		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():
						_UpperCamelCase       : int    =      old_checkpoint[path]
						_UpperCamelCase       : Any    =      old_tensor.shape[0] // 3

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

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

						_UpperCamelCase       : Union[str, Any]    =      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
						_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase       : Dict    =      old_tensor.split(channels // num_heads			,dim=1	)

						_UpperCamelCase       : Union[str, Any]    =      query.reshape(UpperCamelCase	).squeeze()
						_UpperCamelCase       : Any    =      key.reshape(UpperCamelCase	).squeeze()
						_UpperCamelCase       : Union[str, Any]    =      value.reshape(UpperCamelCase	).squeeze()

		for path in paths:
				_UpperCamelCase       : Any    =      path['''new''']

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				_UpperCamelCase       : int    =      new_path.replace('''down.'''			,'''down_blocks.'''	)
				_UpperCamelCase       : Dict    =      new_path.replace('''up.'''			,'''up_blocks.'''	)

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

				if "attentions" in new_path:
						_UpperCamelCase       : Union[str, Any]    =      old_checkpoint[path['''old''']].squeeze()
				else:
						_UpperCamelCase       : Union[str, Any]    =      old_checkpoint[path['''old''']]







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      List[str]:
		_UpperCamelCase       : Union[str, Any]    =      {}

		_UpperCamelCase       : Any    =      checkpoint['''temb.dense.0.weight''']
		_UpperCamelCase       : Any    =      checkpoint['''temb.dense.0.bias''']
		_UpperCamelCase       : int    =      checkpoint['''temb.dense.1.weight''']
		_UpperCamelCase       : Optional[int]    =      checkpoint['''temb.dense.1.bias''']

		_UpperCamelCase       : List[Any]    =      checkpoint['''norm_out.weight''']
		_UpperCamelCase       : Optional[Any]    =      checkpoint['''norm_out.bias''']

		_UpperCamelCase       : List[str]    =      checkpoint['''conv_in.weight''']
		_UpperCamelCase       : Optional[int]    =      checkpoint['''conv_in.bias''']
		_UpperCamelCase       : int    =      checkpoint['''conv_out.weight''']
		_UpperCamelCase       : Tuple    =      checkpoint['''conv_out.bias''']

		_UpperCamelCase       : str    =      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''down''' in layer}	)
		_UpperCamelCase       : List[str]    =      {
		    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
		}

		_UpperCamelCase       : Optional[Any]    =      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''up''' in layer}	)
		_UpperCamelCase       : str    =      {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	):
				_UpperCamelCase       : Dict    =      (i - 1) // (config['''layers_per_block'''] + 1)

				if any('''downsample''' in layer for layer in down_blocks[i]	):
						_UpperCamelCase       : Tuple    =      checkpoint[
						    f'''down.{i}.downsample.op.weight'''
						]
						_UpperCamelCase       : Union[str, Any]    =      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]	):
						_UpperCamelCase       : Optional[Any]    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''block''' in layer}	)
						_UpperCamelCase       : Any    =      {
						    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''']	):
										_UpperCamelCase       : Any    =      renew_resnet_paths(blocks[j]	)
										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase	)

				if any('''attn''' in layer for layer in down_blocks[i]	):
						_UpperCamelCase       : Any    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''attn''' in layer}	)
						_UpperCamelCase       : Any    =      {
						    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''']	):
										_UpperCamelCase       : str    =      renew_attention_paths(attns[j]	)
										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,config=UpperCamelCase	)

		_UpperCamelCase       : Optional[int]    =      [key for key in checkpoint if '''mid.block_1''' in key]
		_UpperCamelCase       : Dict    =      [key for key in checkpoint if '''mid.block_2''' in key]
		_UpperCamelCase       : Any    =      [key for key in checkpoint if '''mid.attn_1''' in key]

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

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

		_UpperCamelCase       : List[str]    =      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	):
				_UpperCamelCase       : str    =      num_up_blocks - 1 - i

				if any('''upsample''' in layer for layer in up_blocks[i]	):
						_UpperCamelCase       : Optional[int]    =      checkpoint[
						    f'''up.{i}.upsample.conv.weight'''
						]
						_UpperCamelCase       : List[str]    =      checkpoint[f'''up.{i}.upsample.conv.bias''']

				if any('''block''' in layer for layer in up_blocks[i]	):
						_UpperCamelCase       : Dict    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''block''' in layer}	)
						_UpperCamelCase       : Optional[Any]    =      {
						    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	):
										_UpperCamelCase       : str    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
										_UpperCamelCase       : List[Any]    =      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]	):
						_UpperCamelCase       : Union[str, Any]    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''attn''' in layer}	)
						_UpperCamelCase       : Tuple    =      {
						    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	):
										_UpperCamelCase       : int    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
										_UpperCamelCase       : Optional[int]    =      renew_attention_paths(attns[j]	)
										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[replace_indices]	)

		_UpperCamelCase       : List[Any]    =      {k.replace('''mid_new_2'''			,'''mid_block'''	): v for k, v in new_checkpoint.items()}
		return new_checkpoint







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      Tuple:
		_UpperCamelCase       : Tuple    =      {}

		_UpperCamelCase       : Tuple    =      checkpoint['''encoder.norm_out.weight''']
		_UpperCamelCase       : str    =      checkpoint['''encoder.norm_out.bias''']

		_UpperCamelCase       : Optional[Any]    =      checkpoint['''encoder.conv_in.weight''']
		_UpperCamelCase       : Any    =      checkpoint['''encoder.conv_in.bias''']
		_UpperCamelCase       : Any    =      checkpoint['''encoder.conv_out.weight''']
		_UpperCamelCase       : List[str]    =      checkpoint['''encoder.conv_out.bias''']

		_UpperCamelCase       : str    =      checkpoint['''decoder.norm_out.weight''']
		_UpperCamelCase       : Dict    =      checkpoint['''decoder.norm_out.bias''']

		_UpperCamelCase       : Any    =      checkpoint['''decoder.conv_in.weight''']
		_UpperCamelCase       : int    =      checkpoint['''decoder.conv_in.bias''']
		_UpperCamelCase       : Dict    =      checkpoint['''decoder.conv_out.weight''']
		_UpperCamelCase       : str    =      checkpoint['''decoder.conv_out.bias''']

		_UpperCamelCase       : str    =      len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''down''' in layer}	)
		_UpperCamelCase       : List[str]    =      {
		    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
		}

		_UpperCamelCase       : int    =      len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''up''' in layer}	)
		_UpperCamelCase       : int    =      {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	):
				_UpperCamelCase       : Union[str, Any]    =      (i - 1) // (config['''layers_per_block'''] + 1)

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

				if any('''block''' in layer for layer in down_blocks[i]	):
						_UpperCamelCase       : int    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''block''' in layer}	)
						_UpperCamelCase       : str    =      {
						    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''']	):
										_UpperCamelCase       : Optional[int]    =      renew_resnet_paths(blocks[j]	)
										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase	)

				if any('''attn''' in layer for layer in down_blocks[i]	):
						_UpperCamelCase       : List[Any]    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''attn''' in layer}	)
						_UpperCamelCase       : Any    =      {
						    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''']	):
										_UpperCamelCase       : List[str]    =      renew_attention_paths(attns[j]	)
										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,config=UpperCamelCase	)

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

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

		_UpperCamelCase       : List[Any]    =      renew_resnet_paths(UpperCamelCase	)
		assign_to_checkpoint(
		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]			,)

		_UpperCamelCase       : Union[str, Any]    =      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	):
				_UpperCamelCase       : Tuple    =      num_up_blocks - 1 - i

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

				if any('''block''' in layer for layer in up_blocks[i]	):
						_UpperCamelCase       : Tuple    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''block''' in layer}	)
						_UpperCamelCase       : Union[str, Any]    =      {
						    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	):
										_UpperCamelCase       : List[Any]    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
										_UpperCamelCase       : int    =      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]	):
						_UpperCamelCase       : List[str]    =      len(
						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''attn''' in layer}	)
						_UpperCamelCase       : int    =      {
						    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	):
										_UpperCamelCase       : Any    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
										_UpperCamelCase       : Any    =      renew_attention_paths(attns[j]	)
										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[replace_indices]	)

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

		return new_checkpoint


if __name__ == "__main__":
	_UpperCAmelCase   :      List[str] 						=  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.""")

	_UpperCAmelCase   :      Optional[Any] 						=  parser.parse_args()
	_UpperCAmelCase   :      Optional[int] 						=  torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		_UpperCAmelCase   :      Union[str, Any] 						=  json.loads(f.read())

	# unet case
	_UpperCAmelCase   :      Optional[int] 						=  {key.split(""".""")[0] for key in checkpoint.keys()}
	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
		_UpperCAmelCase   :      Union[str, Any] 						=  convert_vq_autoenc_checkpoint(checkpoint, config)
	else:
		_UpperCAmelCase   :      Tuple 						=  convert_ddpm_checkpoint(checkpoint, config)

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

	if config["_class_name"] == "VQModel":
		_UpperCAmelCase   :      int 						=  VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		_UpperCAmelCase   :      List[Any] 						=  AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		_UpperCAmelCase   :      Tuple 						=  UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		_UpperCAmelCase   :      Union[str, Any] 						=  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		_UpperCAmelCase   :      Dict 						=  DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)







'''simple docstring'''

from typing import TYPE_CHECKING

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


_UpperCAmelCase   :      int 						=  {
    """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:
	_UpperCAmelCase   :      int 						=  [
	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTSanJapaneseForConditionalGeneration""",
	    """GPTSanJapaneseModel""",
	    """GPTSanJapanesePreTrainedModel""",
	]
	_UpperCAmelCase   :      str 						=  [
	    """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

	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





