





import argparse
import json

import torch

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







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=1  )				->				Tuple:
					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    (       a ,						a=0  )				->				Optional[int]:
					__A     :						Tuple          =    []
					for old_item in old_list:
										__A     :						Tuple          =    old_item
										__A     :						Optional[int]          =    new_item.replace('block.' ,						'resnets.'  )
										__A     :						str          =    new_item.replace('conv_shorcut' ,						'conv1'  )
										__A     :						Dict          =    new_item.replace('in_shortcut' ,						'conv_shortcut'  )
										__A     :						List[str]          =    new_item.replace('temb_proj' ,						'time_emb_proj'  )

										__A     :						Union[str, Any]          =    shave_segments(a ,						n_shave_prefix_segments=a  )

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

					return mapping







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=0 ,						a=False  )				->				int:
					__A     :						Tuple          =    []
					for old_item in old_list:
										__A     :						List[str]          =    old_item

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

										__A     :						Any          =    new_item.replace('proj_out' ,						'proj_attn'  )
										__A     :						Any          =    new_item.replace('norm' ,						'group_norm'  )

										__A     :						Union[str, Any]          =    shave_segments(a ,						n_shave_prefix_segments=a  )
										mapping.append({'old': old_item, 'new': new_item}  )

					return mapping







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a=None ,						a=None ,						a=None  )				->				Union[str, Any]:
					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():
															__A     :						Union[str, Any]          =    old_checkpoint[path]
															__A     :						List[Any]          =    old_tensor.shape[0] // 3

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

															__A     :						int          =    old_tensor.shape[0] // config.get('num_head_channels' ,						1  ) // 3

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

															__A     :						Union[str, Any]          =    query.reshape(a  ).squeeze()
															__A     :						Optional[int]          =    key.reshape(a  ).squeeze()
															__A     :						Optional[Any]          =    value.reshape(a  ).squeeze()

					for path in paths:
										__A     :						str          =    path['new']

										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

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

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

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







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				int:
					__A     :						Optional[Any]          =    {}

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

					__A     :						Optional[int]          =    checkpoint['norm_out.weight']
					__A     :						List[str]          =    checkpoint['norm_out.bias']

					__A     :						Any          =    checkpoint['conv_in.weight']
					__A     :						Any          =    checkpoint['conv_in.bias']
					__A     :						Dict          =    checkpoint['conv_out.weight']
					__A     :						List[Any]          =    checkpoint['conv_out.bias']

					__A     :						List[str]          =    len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'down' in layer}  )
					__A     :						Union[str, Any]          =    {
					    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a  )
					}

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

										if any('downsample' in layer for layer in down_blocks[i]  ):
															__A     :						List[str]          =    checkpoint[
															    F"""down.{i}.downsample.op.weight"""
															]
															__A     :						str          =    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]  ):
															__A     :						Optional[Any]          =    len(
															    {'.'.join(shave_segments(a ,						2  ).split('.'  )[:2]  ) for layer in down_blocks[i] if 'block' in layer}  )
															__A     :						List[str]          =    {
															    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']  ):
																									__A     :						List[str]          =    renew_resnet_paths(blocks[j]  )
																									assign_to_checkpoint(a ,						a ,						a  )

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

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

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

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

					__A     :						str          =    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  ):
										__A     :						Optional[Any]          =    num_up_blocks - 1 - i

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

										if any('block' in layer for layer in up_blocks[i]  ):
															__A     :						Any          =    len(
															    {'.'.join(shave_segments(a ,						2  ).split('.'  )[:2]  ) for layer in up_blocks[i] if 'block' in layer}  )
															__A     :						Optional[Any]          =    {
															    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  ):
																									__A     :						str          =    {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																									__A     :						Union[str, Any]          =    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]  ):
															__A     :						Any          =    len(
															    {'.'.join(shave_segments(a ,						2  ).split('.'  )[:2]  ) for layer in up_blocks[i] if 'attn' in layer}  )
															__A     :						Dict          =    {
															    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  ):
																									__A     :						str          =    {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																									__A     :						Dict          =    renew_attention_paths(attns[j]  )
																									assign_to_checkpoint(a ,						a ,						a ,						additional_replacements=[replace_indices]  )

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







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Dict:
					__A     :						List[str]          =    {}

					__A     :						List[Any]          =    checkpoint['encoder.norm_out.weight']
					__A     :						Any          =    checkpoint['encoder.norm_out.bias']

					__A     :						Optional[Any]          =    checkpoint['encoder.conv_in.weight']
					__A     :						Optional[Any]          =    checkpoint['encoder.conv_in.bias']
					__A     :						Tuple          =    checkpoint['encoder.conv_out.weight']
					__A     :						Union[str, Any]          =    checkpoint['encoder.conv_out.bias']

					__A     :						List[Any]          =    checkpoint['decoder.norm_out.weight']
					__A     :						List[str]          =    checkpoint['decoder.norm_out.bias']

					__A     :						str          =    checkpoint['decoder.conv_in.weight']
					__A     :						Union[str, Any]          =    checkpoint['decoder.conv_in.bias']
					__A     :						Dict          =    checkpoint['decoder.conv_out.weight']
					__A     :						Optional[int]          =    checkpoint['decoder.conv_out.bias']

					__A     :						List[Any]          =    len({'.'.join(layer.split('.'  )[:3]  ) for layer in checkpoint if 'down' in layer}  )
					__A     :						Optional[int]          =    {
					    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a  )
					}

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

										if any('downsample' in layer for layer in down_blocks[i]  ):
															__A     :						List[str]          =    checkpoint[
															    F"""encoder.down.{i}.downsample.conv.weight"""
															]
															__A     :						Optional[int]          =    checkpoint[
															    F"""encoder.down.{i}.downsample.conv.bias"""
															]

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

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

					__A     :						Union[str, Any]          =    [key for key in checkpoint if 'mid.block_1' in key]
					__A     :						str          =    [key for key in checkpoint if 'mid.block_2' in key]
					__A     :						Optional[Any]          =    [key for key in checkpoint if 'mid.attn_1' in key]

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

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

					__A     :						List[Any]          =    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  ):
										__A     :						Optional[int]          =    num_up_blocks - 1 - i

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

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

					__A     :						Any          =    {k.replace('mid_new_2' ,						'mid_block'  ): v for k, v in new_checkpoint.items()}
					__A     :						Optional[int]          =    checkpoint['quant_conv.weight']
					__A     :						List[str]          =    checkpoint['quant_conv.bias']
					if "quantize.embedding.weight" in checkpoint:
										__A     :						List[Any]          =    checkpoint['quantize.embedding.weight']
					__A     :						int          =    checkpoint['post_quant_conv.weight']
					__A     :						List[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						:       int        =					parser.parse_args()
				UpperCAmelCase						:       Tuple        =					torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								UpperCAmelCase						:       Optional[int]        =					json.loads(f.read())

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

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

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

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

								UpperCAmelCase						:       List[Any]        =					DDPMPipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase						:       str        =					logging.get_logger(__name__)

UpperCAmelCase						:       Optional[Any]        =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Union[str, Any]    =     '''swiftformer'''



				def __init__(      self					, _A=3					, _A=[3, 3, 6, 4]					, _A=[48, 56, 112, 220]					, _A=4					, _A=[True, True, True, True]					, _A="gelu"					, _A=3					, _A=2					, _A=1					, _A=0.0					, _A=True					, _A=1e-5					, _A=1e-5					, **_A					, ):
									super().__init__(**_A					)
									__A     :						List[Any]          =    num_channels
									__A     :						List[Any]          =    depths
									__A     :						int          =    embed_dims
									__A     :						Union[str, Any]          =    mlp_ratio
									__A     :						List[str]          =    downsamples
									__A     :						str          =    hidden_act
									__A     :						Any          =    down_patch_size
									__A     :						Any          =    down_stride
									__A     :						Any          =    down_pad
									__A     :						List[Any]          =    drop_path_rate
									__A     :						str          =    use_layer_scale
									__A     :						Dict          =    layer_scale_init_value
									__A     :						Union[str, Any]          =    batch_norm_eps





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     version.parse('''1.11'''       )



				@property
				def  UpperCAmelCase_		(      self					):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]					)





				@property
				def  UpperCAmelCase_		(      self					):
									return 1e-4




