


import argparse
import json

import torch

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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_=1):
	'''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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_=0):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       []
	for old_item in old_list:
		lowerCamelCase_			:							List[str]      =       old_item
		lowerCamelCase_			:							List[str]      =       new_item.replace("block."      ,					"resnets.")
		lowerCamelCase_			:							Optional[int]      =       new_item.replace("conv_shorcut"      ,					"conv1")
		lowerCamelCase_			:							Tuple      =       new_item.replace("in_shortcut"      ,					"conv_shortcut")
		lowerCamelCase_			:							Optional[int]      =       new_item.replace("temb_proj"      ,					"time_emb_proj")

		lowerCamelCase_			:							Union[str, Any]      =       shave_segments(lowerCAmelCase_      ,					n_shave_prefix_segments=lowerCAmelCase_)

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

	return mapping







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_=0      ,					lowerCAmelCase_=False):
	'''simple docstring'''


	lowerCamelCase_			:							Any      =       []
	for old_item in old_list:
		lowerCamelCase_			:							Any      =       old_item

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

		lowerCamelCase_			:							Tuple      =       new_item.replace("proj_out"      ,					"proj_attn")
		lowerCamelCase_			:							List[str]      =       new_item.replace("norm"      ,					"group_norm")

		lowerCamelCase_			:							Dict      =       shave_segments(lowerCAmelCase_      ,					n_shave_prefix_segments=lowerCAmelCase_)
		mapping.append({"old": old_item, "new": new_item})

	return mapping







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_=None      ,					lowerCAmelCase_=None      ,					lowerCAmelCase_=None):
	'''simple docstring'''


	assert isinstance(lowerCAmelCase_      ,					lowerCAmelCase_), "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_			:							List[str]      =       old_checkpoint[path]
			lowerCamelCase_			:							int      =       old_tensor.shape[0] // 3

			lowerCamelCase_			:							Tuple      =       (-1, channels) if len(old_tensor.shape) == 3 else (-1)

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

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

			lowerCamelCase_			:							Optional[int]      =       query.reshape(lowerCAmelCase_).squeeze()
			lowerCamelCase_			:							Dict      =       key.reshape(lowerCAmelCase_).squeeze()
			lowerCamelCase_			:							List[str]      =       value.reshape(lowerCAmelCase_).squeeze()

	for path in paths:
		lowerCamelCase_			:							List[str]      =       path["new"]

		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		lowerCamelCase_			:							Optional[Any]      =       new_path.replace("down."      ,					"down_blocks.")
		lowerCamelCase_			:							Any      =       new_path.replace("up."      ,					"up_blocks.")

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

		if "attentions" in new_path:
			lowerCamelCase_			:							List[str]      =       old_checkpoint[path["old"]].squeeze()
		else:
			lowerCamelCase_			:							int      =       old_checkpoint[path["old"]]







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       {}

	lowerCamelCase_			:							int      =       checkpoint["temb.dense.0.weight"]
	lowerCamelCase_			:							List[Any]      =       checkpoint["temb.dense.0.bias"]
	lowerCamelCase_			:							Optional[int]      =       checkpoint["temb.dense.1.weight"]
	lowerCamelCase_			:							str      =       checkpoint["temb.dense.1.bias"]

	lowerCamelCase_			:							Dict      =       checkpoint["norm_out.weight"]
	lowerCamelCase_			:							List[str]      =       checkpoint["norm_out.bias"]

	lowerCamelCase_			:							Dict      =       checkpoint["conv_in.weight"]
	lowerCamelCase_			:							List[Any]      =       checkpoint["conv_in.bias"]
	lowerCamelCase_			:							Tuple      =       checkpoint["conv_out.weight"]
	lowerCamelCase_			:							Dict      =       checkpoint["conv_out.bias"]

	lowerCamelCase_			:							Optional[Any]      =       len({".".join(layer.split(".")[:2]) for layer in checkpoint if "down" in layer})
	lowerCamelCase_			:							str      =       {
	    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(lowerCAmelCase_)
	}

	lowerCamelCase_			:							Union[str, Any]      =       len({".".join(layer.split(".")[:2]) for layer in checkpoint if "up" in layer})
	lowerCamelCase_			:							Union[str, Any]      =       {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowerCAmelCase_)}

	for i in range(lowerCAmelCase_):
		lowerCamelCase_			:							Optional[Any]      =       (i - 1) // (config["layers_per_block"] + 1)

		if any("downsample" in layer for layer in down_blocks[i]):
			lowerCamelCase_			:							Tuple      =       checkpoint[
			    F"""down.{i}.downsample.op.weight"""
			]
			lowerCamelCase_			:							Optional[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]):
			lowerCamelCase_			:							Any      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					2).split(".")[:2]) for layer in down_blocks[i] if "block" in layer})
			lowerCamelCase_			:							Union[str, Any]      =       {
			    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
			    for layer_id in range(lowerCAmelCase_)
			}

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

		if any("attn" in layer for layer in down_blocks[i]):
			lowerCamelCase_			:							Tuple      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					2).split(".")[:2]) for layer in down_blocks[i] if "attn" in layer})
			lowerCamelCase_			:							str      =       {
			    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
			    for layer_id in range(lowerCAmelCase_)
			}

			if num_attn > 0:
				for j in range(config["layers_per_block"]):
					lowerCamelCase_			:							Optional[int]      =       renew_attention_paths(attns[j])
					assign_to_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					config=lowerCAmelCase_)

	lowerCamelCase_			:							Optional[Any]      =       [key for key in checkpoint if "mid.block_1" in key]
	lowerCamelCase_			:							Optional[int]      =       [key for key in checkpoint if "mid.block_2" in key]
	lowerCamelCase_			:							int      =       [key for key in checkpoint if "mid.attn_1" in key]

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

	lowerCamelCase_			:							Optional[int]      =       renew_resnet_paths(lowerCAmelCase_)
	assign_to_checkpoint(
	    lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]      ,					)

	lowerCamelCase_			:							Optional[int]      =       renew_attention_paths(lowerCAmelCase_      ,					in_mid=lowerCAmelCase_)
	assign_to_checkpoint(
	    lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]      ,					)

	for i in range(lowerCAmelCase_):
		lowerCamelCase_			:							str      =       num_up_blocks - 1 - i

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

		if any("block" in layer for layer in up_blocks[i]):
			lowerCamelCase_			:							Optional[Any]      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					2).split(".")[:2]) for layer in up_blocks[i] if "block" in layer})
			lowerCamelCase_			:							Optional[int]      =       {
			    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowerCAmelCase_)
			}

			if num_blocks > 0:
				for j in range(config["layers_per_block"] + 1):
					lowerCamelCase_			:							List[Any]      =       {"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
					lowerCamelCase_			:							Tuple      =       renew_resnet_paths(blocks[j])
					assign_to_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					additional_replacements=[replace_indices])

		if any("attn" in layer for layer in up_blocks[i]):
			lowerCamelCase_			:							List[str]      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					2).split(".")[:2]) for layer in up_blocks[i] if "attn" in layer})
			lowerCamelCase_			:							str      =       {
			    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowerCAmelCase_)
			}

			if num_attn > 0:
				for j in range(config["layers_per_block"] + 1):
					lowerCamelCase_			:							Optional[Any]      =       {"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
					lowerCamelCase_			:							List[Any]      =       renew_attention_paths(attns[j])
					assign_to_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					additional_replacements=[replace_indices])

	lowerCamelCase_			:							List[str]      =       {k.replace("mid_new_2"      ,					"mid_block"): v for k, v in new_checkpoint.items()}
	return new_checkpoint







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Any      =       {}

	lowerCamelCase_			:							int      =       checkpoint["encoder.norm_out.weight"]
	lowerCamelCase_			:							str      =       checkpoint["encoder.norm_out.bias"]

	lowerCamelCase_			:							Tuple      =       checkpoint["encoder.conv_in.weight"]
	lowerCamelCase_			:							Any      =       checkpoint["encoder.conv_in.bias"]
	lowerCamelCase_			:							List[Any]      =       checkpoint["encoder.conv_out.weight"]
	lowerCamelCase_			:							List[Any]      =       checkpoint["encoder.conv_out.bias"]

	lowerCamelCase_			:							Optional[Any]      =       checkpoint["decoder.norm_out.weight"]
	lowerCamelCase_			:							Optional[Any]      =       checkpoint["decoder.norm_out.bias"]

	lowerCamelCase_			:							List[str]      =       checkpoint["decoder.conv_in.weight"]
	lowerCamelCase_			:							Union[str, Any]      =       checkpoint["decoder.conv_in.bias"]
	lowerCamelCase_			:							List[str]      =       checkpoint["decoder.conv_out.weight"]
	lowerCamelCase_			:							List[Any]      =       checkpoint["decoder.conv_out.bias"]

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

	lowerCamelCase_			:							Union[str, Any]      =       len({".".join(layer.split(".")[:3]) for layer in checkpoint if "up" in layer})
	lowerCamelCase_			:							Tuple      =       {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowerCAmelCase_)}

	for i in range(lowerCAmelCase_):
		lowerCamelCase_			:							Optional[Any]      =       (i - 1) // (config["layers_per_block"] + 1)

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

		if any("block" in layer for layer in down_blocks[i]):
			lowerCamelCase_			:							Optional[Any]      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					3).split(".")[:3]) for layer in down_blocks[i] if "block" in layer})
			lowerCamelCase_			:							Optional[int]      =       {
			    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
			    for layer_id in range(lowerCAmelCase_)
			}

			if num_blocks > 0:
				for j in range(config["layers_per_block"]):
					lowerCamelCase_			:							Optional[int]      =       renew_resnet_paths(blocks[j])
					assign_to_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_)

		if any("attn" in layer for layer in down_blocks[i]):
			lowerCamelCase_			:							Tuple      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					3).split(".")[:3]) for layer in down_blocks[i] if "attn" in layer})
			lowerCamelCase_			:							Tuple      =       {
			    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
			    for layer_id in range(lowerCAmelCase_)
			}

			if num_attn > 0:
				for j in range(config["layers_per_block"]):
					lowerCamelCase_			:							List[str]      =       renew_attention_paths(attns[j])
					assign_to_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					config=lowerCAmelCase_)

	lowerCamelCase_			:							List[Any]      =       [key for key in checkpoint if "mid.block_1" in key]
	lowerCamelCase_			:							List[Any]      =       [key for key in checkpoint if "mid.block_2" in key]
	lowerCamelCase_			:							Optional[Any]      =       [key for key in checkpoint if "mid.attn_1" in key]

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

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

	lowerCamelCase_			:							List[str]      =       renew_attention_paths(lowerCAmelCase_      ,					in_mid=lowerCAmelCase_)
	assign_to_checkpoint(
	    lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]      ,					)

	for i in range(lowerCAmelCase_):
		lowerCamelCase_			:							List[str]      =       num_up_blocks - 1 - i

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

		if any("block" in layer for layer in up_blocks[i]):
			lowerCamelCase_			:							List[str]      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					3).split(".")[:3]) for layer in up_blocks[i] if "block" in layer})
			lowerCamelCase_			:							Dict      =       {
			    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowerCAmelCase_)
			}

			if num_blocks > 0:
				for j in range(config["layers_per_block"] + 1):
					lowerCamelCase_			:							Tuple      =       {"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
					lowerCamelCase_			:							Dict      =       renew_resnet_paths(blocks[j])
					assign_to_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					additional_replacements=[replace_indices])

		if any("attn" in layer for layer in up_blocks[i]):
			lowerCamelCase_			:							str      =       len(
			    {".".join(shave_segments(lowerCAmelCase_      ,					3).split(".")[:3]) for layer in up_blocks[i] if "attn" in layer})
			lowerCamelCase_			:							List[Any]      =       {
			    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowerCAmelCase_)
			}

			if num_attn > 0:
				for j in range(config["layers_per_block"] + 1):
					lowerCamelCase_			:							Optional[Any]      =       {"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
					lowerCamelCase_			:							List[str]      =       renew_attention_paths(attns[j])
					assign_to_checkpoint(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					additional_replacements=[replace_indices])

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

	return new_checkpoint


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

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

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

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

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

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

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

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





from typing import TYPE_CHECKING

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


__magic_name__       					=      {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''DetrFeatureExtractor''']
							__magic_name__       					=      ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

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

