






import argparse
import json

import torch

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






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							Any=1  )     ->						str:
		"""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      (			__magic_name__  :							str					,					__magic_name__  :							Any=0  )     ->						str:
		"""simple docstring"""




		lowercase__      =	[]
		for old_item in old_list:
				lowercase__      =	old_item
				lowercase__      =	new_item.replace("""block."""					,					"""resnets."""  )
				lowercase__      =	new_item.replace("""conv_shorcut"""					,					"""conv1"""  )
				lowercase__      =	new_item.replace("""in_shortcut"""					,					"""conv_shortcut"""  )
				lowercase__      =	new_item.replace("""temb_proj"""					,					"""time_emb_proj"""  )

				lowercase__      =	shave_segments(__A					,					n_shave_prefix_segments=__A  )

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

		return mapping






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Tuple=0					,					__magic_name__  :							Optional[Any]=False  )     ->						Tuple:
		"""simple docstring"""




		lowercase__      =	[]
		for old_item in old_list:
				lowercase__      =	old_item

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

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

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

		return mapping






def 				UpperCamelCase      (			__magic_name__  :							List[str]					,					__magic_name__  :							int					,					__magic_name__  :							List[str]					,					__magic_name__  :							str=None					,					__magic_name__  :							Dict=None					,					__magic_name__  :							List[Any]=None  )     ->						Tuple:
		"""simple docstring"""




		assert isinstance(__A					,					__A  ), "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():
						lowercase__      =	old_checkpoint[path]
						lowercase__      =	old_tensor.shape[0] // 3

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

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

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

						lowercase__      =	query.reshape(__A  ).squeeze()
						lowercase__      =	key.reshape(__A  ).squeeze()
						lowercase__      =	value.reshape(__A  ).squeeze()

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

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

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

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

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






def 				UpperCamelCase      (			__magic_name__  :							Optional[int]					,					__magic_name__  :							List[Any]  )     ->						int:
		"""simple docstring"""




		lowercase__      =	{}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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






def 				UpperCamelCase      (			__magic_name__  :							Dict					,					__magic_name__  :							Optional[Any]  )     ->						Dict:
		"""simple docstring"""




		lowercase__      =	{}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

		return new_checkpoint


if __name__ == "__main__":
	A :	Dict												=     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.')

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

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

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

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

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

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

		A :	str												=     DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A :	Optional[Any]												=     logging.get_logger(__name__)

A :	Union[str, Any]												=     {
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Dict   ,      _UpperCAmelCase					:		List[str]=256   ,      _UpperCAmelCase					:		Tuple=1024   ,      _UpperCAmelCase					:		List[str]=16   ,      _UpperCAmelCase					:		Optional[Any]=16   ,      _UpperCAmelCase					:		Union[str, Any]=4   ,      _UpperCAmelCase					:		str="gelu"   ,      _UpperCAmelCase					:		List[str]=1E-6   ,      **_UpperCAmelCase					:		List[Any]   ,      )		->     Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	hidden_size
							lowercase__      =	image_size
							lowercase__      =	patch_size
							lowercase__      =	image_size // patch_size
							lowercase__      =	mask_input_channels
							lowercase__      =	num_point_embeddings
							lowercase__      =	hidden_act
							lowercase__      =	layer_norm_eps






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Optional[int]=256   ,      _UpperCAmelCase					:		int="relu"   ,      _UpperCAmelCase					:		Union[str, Any]=2048   ,      _UpperCAmelCase					:		int=2   ,      _UpperCAmelCase					:		Optional[Any]=8   ,      _UpperCAmelCase					:		Any=2   ,      _UpperCAmelCase					:		Tuple=3   ,      _UpperCAmelCase					:		List[str]=3   ,      _UpperCAmelCase					:		List[str]=256   ,      _UpperCAmelCase					:		Tuple=1E-6   ,      **_UpperCAmelCase					:		int   ,      )		->     Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	hidden_size
							lowercase__      =	hidden_act
							lowercase__      =	mlp_dim
							lowercase__      =	num_hidden_layers
							lowercase__      =	num_attention_heads
							lowercase__      =	attention_downsample_rate
							lowercase__      =	num_multimask_outputs
							lowercase__      =	iou_head_depth
							lowercase__      =	iou_head_hidden_dim
							lowercase__      =	layer_norm_eps






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      _UpperCAmelCase					:		List[Any]=768   ,      _UpperCAmelCase					:		Union[str, Any]=256   ,      _UpperCAmelCase					:		Union[str, Any]=12   ,      _UpperCAmelCase					:		Optional[Any]=12   ,      _UpperCAmelCase					:		Tuple=3   ,      _UpperCAmelCase					:		Optional[int]=1024   ,      _UpperCAmelCase					:		str=16   ,      _UpperCAmelCase					:		Tuple="gelu"   ,      _UpperCAmelCase					:		Any=1E-0_6   ,      _UpperCAmelCase					:		Any=0.0   ,      _UpperCAmelCase					:		str=1E-1_0   ,      _UpperCAmelCase					:		Tuple=True   ,      _UpperCAmelCase					:		Tuple=4.0   ,      _UpperCAmelCase					:		Optional[Any]=True   ,      _UpperCAmelCase					:		Optional[int]=True   ,      _UpperCAmelCase					:		Optional[Any]=14   ,      _UpperCAmelCase					:		Optional[int]=[2, 5, 8, 11]   ,      _UpperCAmelCase					:		List[Any]=128   ,      _UpperCAmelCase					:		int=None   ,      **_UpperCAmelCase					:		int   ,      )		->     Dict:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)

							lowercase__      =	hidden_size
							lowercase__      =	output_channels
							lowercase__      =	num_hidden_layers
							lowercase__      =	num_attention_heads
							lowercase__      =	num_channels
							lowercase__      =	image_size
							lowercase__      =	patch_size
							lowercase__      =	hidden_act
							lowercase__      =	layer_norm_eps
							lowercase__      =	attention_dropout
							lowercase__      =	initializer_range
							lowercase__      =	qkv_bias
							lowercase__      =	mlp_ratio
							lowercase__      =	use_abs_pos
							lowercase__      =	use_rel_pos
							lowercase__      =	window_size
							lowercase__      =	global_attn_indexes
							lowercase__      =	num_pos_feats
							lowercase__      =	int(hidden_size * mlp_ratio	) if mlp_dim is None else mlp_dim






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				'''sam'''
					A__    =				True

					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Any=None   ,      _UpperCAmelCase					:		List[Any]=None   ,      _UpperCAmelCase					:		List[Any]=None   ,      _UpperCAmelCase					:		List[Any]=0.02   ,      **_UpperCAmelCase					:		Optional[Any]   ,      )		->     Dict:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	vision_config if vision_config is not None else {}
							lowercase__      =	prompt_encoder_config if prompt_encoder_config is not None else {}
							lowercase__      =	mask_decoder_config if mask_decoder_config is not None else {}

							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	vision_config.to_dict()
							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	prompt_encoder_config.to_dict()
							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	mask_decoder_config.to_dict()

							lowercase__      =	SamVisionConfig(**_UpperCAmelCase	)
							lowercase__      =	SamPromptEncoderConfig(**_UpperCAmelCase	)
							lowercase__      =	SamMaskDecoderConfig(**_UpperCAmelCase	)
							lowercase__      =	initializer_range


					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Tuple:




							"""simple docstring"""


							lowercase__      =	copy.deepcopy(self.__dict__	)
							lowercase__      =	self.vision_config.to_dict()
							lowercase__      =	self.prompt_encoder_config.to_dict()
							lowercase__      =	self.mask_decoder_config.to_dict()
							lowercase__      =	self.__class__.model_type
							return output

