






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase_		  =						{
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_		  =						["""DetaImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_		  =						[
		    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """DetaForObjectDetection""",
		    """DetaModel""",
		    """DetaPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_deta import DetaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deta import (
				    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DetaForObjectDetection,
				    DetaModel,
				    DetaPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase_		  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def   lowerCamelCase      (		a_				,		a_=1			)			->    str:
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split('.'			)[n_shave_prefix_segments:]			)
							else:
														return ".".join(path.split('.'			)[:n_shave_prefix_segments]			)
def   lowerCamelCase      (		a_				,		a_=0			)			->    List[Any]:
							lowerCAmelCase_       		=  []
							for old_item in old_list:
														lowerCAmelCase_       		=  old_item
														lowerCAmelCase_       		=  new_item.replace('block.'				,		'resnets.'			)
														lowerCAmelCase_       		=  new_item.replace('conv_shorcut'				,		'conv1'			)
														lowerCAmelCase_       		=  new_item.replace('in_shortcut'				,		'conv_shortcut'			)
														lowerCAmelCase_       		=  new_item.replace('temb_proj'				,		'time_emb_proj'			)

														lowerCAmelCase_       		=  shave_segments(a_				,		n_shave_prefix_segments=a_			)

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

							return mapping
def   lowerCamelCase      (		a_				,		a_=0				,		a_=False			)			->    Dict:
							lowerCAmelCase_       		=  []
							for old_item in old_list:
														lowerCAmelCase_       		=  old_item

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

														lowerCAmelCase_       		=  new_item.replace('proj_out'				,		'proj_attn'			)
														lowerCAmelCase_       		=  new_item.replace('norm'				,		'group_norm'			)

														lowerCAmelCase_       		=  shave_segments(a_				,		n_shave_prefix_segments=a_			)
														mapping.append({'old': old_item, 'new': new_item}			)

							return mapping
def   lowerCamelCase      (		a_				,		a_				,		a_				,		a_=None				,		a_=None				,		a_=None			)			->    Tuple:
							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():
																					lowerCAmelCase_       		=  old_checkpoint[path]
																					lowerCAmelCase_       		=  old_tensor.shape[0] // 3

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

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

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

																					lowerCAmelCase_       		=  query.reshape(a_			).squeeze()
																					lowerCAmelCase_       		=  key.reshape(a_			).squeeze()
																					lowerCAmelCase_       		=  value.reshape(a_			).squeeze()

							for path in paths:
														lowerCAmelCase_       		=  path['new']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														lowerCAmelCase_       		=  new_path.replace('down.'				,		'down_blocks.'			)
														lowerCAmelCase_       		=  new_path.replace('up.'				,		'up_blocks.'			)

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

														if "attentions" in new_path:
																					lowerCAmelCase_       		=  old_checkpoint[path['old']].squeeze()
														else:
																					lowerCAmelCase_       		=  old_checkpoint[path['old']]
def   lowerCamelCase      (		a_				,		a_			)			->    List[str]:
							lowerCAmelCase_       		=  {}

							lowerCAmelCase_       		=  checkpoint['temb.dense.0.weight']
							lowerCAmelCase_       		=  checkpoint['temb.dense.0.bias']
							lowerCAmelCase_       		=  checkpoint['temb.dense.1.weight']
							lowerCAmelCase_       		=  checkpoint['temb.dense.1.bias']

							lowerCAmelCase_       		=  checkpoint['norm_out.weight']
							lowerCAmelCase_       		=  checkpoint['norm_out.bias']

							lowerCAmelCase_       		=  checkpoint['conv_in.weight']
							lowerCAmelCase_       		=  checkpoint['conv_in.bias']
							lowerCAmelCase_       		=  checkpoint['conv_out.weight']
							lowerCAmelCase_       		=  checkpoint['conv_out.bias']

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'down' in layer}			)
							lowerCAmelCase_       		=  {
							    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_			)
							}

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'up' in layer}			)
							lowerCAmelCase_       		=  {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_			):
														lowerCAmelCase_       		=  (i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]			):
																					lowerCAmelCase_       		=  checkpoint[
																					    F'''down.{i}.downsample.op.weight'''
																					]
																					lowerCAmelCase_       		=  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]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'block' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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']			):
																																			lowerCAmelCase_       		=  renew_resnet_paths(blocks[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_			)

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

							lowerCAmelCase_       		=  [key for key in checkpoint if 'mid.block_1' in key]
							lowerCAmelCase_       		=  [key for key in checkpoint if 'mid.block_2' in key]
							lowerCAmelCase_       		=  [key for key in checkpoint if 'mid.attn_1' in key]

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

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

							lowerCAmelCase_       		=  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_			):
														lowerCAmelCase_       		=  num_up_blocks - 1 - i

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

														if any('block' in layer for layer in up_blocks[i]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'block' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  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]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'attn' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  renew_attention_paths(attns[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_				,		additional_replacements=[replace_indices]			)

							lowerCAmelCase_       		=  {k.replace('mid_new_2'				,		'mid_block'			): v for k, v in new_checkpoint.items()}
							return new_checkpoint
def   lowerCamelCase      (		a_				,		a_			)			->    Any:
							lowerCAmelCase_       		=  {}

							lowerCAmelCase_       		=  checkpoint['encoder.norm_out.weight']
							lowerCAmelCase_       		=  checkpoint['encoder.norm_out.bias']

							lowerCAmelCase_       		=  checkpoint['encoder.conv_in.weight']
							lowerCAmelCase_       		=  checkpoint['encoder.conv_in.bias']
							lowerCAmelCase_       		=  checkpoint['encoder.conv_out.weight']
							lowerCAmelCase_       		=  checkpoint['encoder.conv_out.bias']

							lowerCAmelCase_       		=  checkpoint['decoder.norm_out.weight']
							lowerCAmelCase_       		=  checkpoint['decoder.norm_out.bias']

							lowerCAmelCase_       		=  checkpoint['decoder.conv_in.weight']
							lowerCAmelCase_       		=  checkpoint['decoder.conv_in.bias']
							lowerCAmelCase_       		=  checkpoint['decoder.conv_out.weight']
							lowerCAmelCase_       		=  checkpoint['decoder.conv_out.bias']

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'down' in layer}			)
							lowerCAmelCase_       		=  {
							    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_			)
							}

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'up' in layer}			)
							lowerCAmelCase_       		=  {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_			):
														lowerCAmelCase_       		=  (i - 1) // (config['layers_per_block'] + 1)

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

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

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

							lowerCAmelCase_       		=  [key for key in checkpoint if 'mid.block_1' in key]
							lowerCAmelCase_       		=  [key for key in checkpoint if 'mid.block_2' in key]
							lowerCAmelCase_       		=  [key for key in checkpoint if 'mid.attn_1' in key]

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

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

							lowerCAmelCase_       		=  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_			):
														lowerCAmelCase_       		=  num_up_blocks - 1 - i

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

														if any('block' in layer for layer in up_blocks[i]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'block' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  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]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'attn' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  renew_attention_paths(attns[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_				,		additional_replacements=[replace_indices]			)

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



