


import argparse
import json

import torch

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


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=1				)    ->  Union[str, Any]:
						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					(  _UpperCAmelCase							,     _UpperCAmelCase=0				)    ->  List[Any]:
						_a     =			[]
						for old_item in old_list:
												_a     =			old_item
												_a     =			new_item.replace('block.'							,     'resnets.'				)
												_a     =			new_item.replace('conv_shorcut'							,     'conv1'				)
												_a     =			new_item.replace('in_shortcut'							,     'conv_shortcut'				)
												_a     =			new_item.replace('temb_proj'							,     'time_emb_proj'				)

												_a     =			shave_segments(_UpperCAmelCase							,     n_shave_prefix_segments=_UpperCAmelCase				)

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

						return mapping


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=0							,     _UpperCAmelCase=False				)    ->  Dict:
						_a     =			[]
						for old_item in old_list:
												_a     =			old_item

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

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

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

						return mapping


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase=None							,     _UpperCAmelCase=None							,     _UpperCAmelCase=None				)    ->  Any:
						assert isinstance(_UpperCAmelCase							,     _UpperCAmelCase				), "Paths should be a list of dicts containing 'old' and 'new' keys."

						if attention_paths_to_split is not None:
												if config is None:
																		raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'				)

												for path, path_map in attention_paths_to_split.items():
																		_a     =			old_checkpoint[path]
																		_a     =			old_tensor.shape[0] // 3

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

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

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

																		_a     =			query.reshape(_UpperCAmelCase				).squeeze()
																		_a     =			key.reshape(_UpperCAmelCase				).squeeze()
																		_a     =			value.reshape(_UpperCAmelCase				).squeeze()

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

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

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

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

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


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
						_a     =			{}

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

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

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

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

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

						for i in range(_UpperCAmelCase				):
												_a     =			(i - 1) // (config['layers_per_block'] + 1)

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

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']				):
																														_a     =			renew_resnet_paths(blocks[j]				)
																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)

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

																		if num_attn > 0:
																								for j in range(config['layers_per_block']				):
																														_a     =			renew_attention_paths(attns[j]				)
																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     config=_UpperCAmelCase				)

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

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

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

						_a     =			renew_attention_paths(_UpperCAmelCase							,     in_mid=_UpperCAmelCase				)
						assign_to_checkpoint(
						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,     )

						for i in range(_UpperCAmelCase				):
												_a     =			num_up_blocks - 1 - i

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

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

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

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

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

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


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  str:
						_a     =			{}

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

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

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

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

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

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

						for i in range(_UpperCAmelCase				):
												_a     =			(i - 1) // (config['layers_per_block'] + 1)

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

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

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']				):
																														_a     =			renew_resnet_paths(blocks[j]				)
																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)

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

																		if num_attn > 0:
																								for j in range(config['layers_per_block']				):
																														_a     =			renew_attention_paths(attns[j]				)
																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     config=_UpperCAmelCase				)

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

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

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

						_a     =			renew_attention_paths(_UpperCAmelCase							,     in_mid=_UpperCAmelCase				)
						assign_to_checkpoint(
						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,     )

						for i in range(_UpperCAmelCase				):
												_a     =			num_up_blocks - 1 - i

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

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

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

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

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

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

						return new_checkpoint


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

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

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

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

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

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

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

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






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_								=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_								=    []
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
		)
		rename_keys.append(
		    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
		)
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
		rename_keys.append(
		    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
		)
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
		)
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
		)
		rename_keys.append(
		    (
		        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
		        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
		    )
		)
		rename_keys.append(
		    (
		        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
		        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
		    )
		)
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
		)
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
		)
		rename_keys.append(
		    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
		)
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
						_a     =			state_dict.pop(_UpperCAmelCase				)
						_a     =			val


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[str]:
						_a     =			OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		_a     =			key.replace('backbone.0.body'							,     'backbone.conv_encoder.model'				)
																		_a     =			value
												else:
																		_a     =			value

						return new_state_dict


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=False				)    ->  Tuple:
						_a     =			''
						if is_panoptic:
												_a     =			'detr.'

						# first: transformer encoder
						for i in range(6				):
												# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
												_a     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
												_a     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
												# next, add query, keys and values (in that order) to the state dict
												_a     =			in_proj_weight[:256, :]
												_a     =			in_proj_bias[:256]
												_a     =			in_proj_weight[256:512, :]
												_a     =			in_proj_bias[256:512]
												_a     =			in_proj_weight[-256:, :]
												_a     =			in_proj_bias[-256:]
						# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
						for i in range(6				):
												# read in weights + bias of input projection layer of self-attention
												_a     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
												_a     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""				)
												# next, add query, keys and values (in that order) to the state dict
												_a     =			in_proj_weight[:256, :]
												_a     =			in_proj_bias[:256]
												_a     =			in_proj_weight[256:512, :]
												_a     =			in_proj_bias[256:512]
												_a     =			in_proj_weight[-256:, :]
												_a     =			in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												_a     =			state_dict.pop(
												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
												_a     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""				)
												# next, add query, keys and values (in that order) of cross-attention to the state dict
												_a     =			in_proj_weight_cross_attn[:256, :]
												_a     =			in_proj_bias_cross_attn[:256]
												_a     =			in_proj_weight_cross_attn[256:512, :]
												_a     =			in_proj_bias_cross_attn[256:512]
												_a     =			in_proj_weight_cross_attn[-256:, :]
												_a     =			in_proj_bias_cross_attn[-256:]


def 							SCREAMING_SNAKE_CASE					(  )    ->  Union[str, Any]:
						_a     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
						_a     =			Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase				).raw				)

						return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  List[str]:
						_a     =			DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												_a     =			'resnet101'
						if "dc5" in model_name:
												_a     =			True
						_a     =			'panoptic' in model_name
						if is_panoptic:
												_a     =			250
						else:
												_a     =			91
												_a     =			'huggingface/label-files'
												_a     =			'coco-detection-id2label.json'
												_a     =			json.load(open(hf_hub_download(_UpperCAmelCase							,     _UpperCAmelCase							,     repo_type='dataset'				)							,     'r'				)				)
												_a     =			{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
												_a     =			idalabel
												_a     =			{v: k for k, v in idalabel.items()}

						# load image processor
						_a     =			'coco_panoptic' if is_panoptic else 'coco_detection'
						_a     =			DetrImageProcessor(format=_UpperCAmelCase				)

						# prepare image
						_a     =			prepare_img()
						_a     =			image_processor(images=_UpperCAmelCase							,     return_tensors='pt'				)
						_a     =			encoding['pixel_values']

						logger.info(f"""Converting model {model_name}..."""				)

						# load original model from torch hub
						_a     =			torch.hub.load('facebookresearch/detr'							,     _UpperCAmelCase							,     pretrained=_UpperCAmelCase				).eval()
						_a     =			detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		_a     =			'detr.' + src
												rename_key(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
						_a     =			rename_backbone_keys(_UpperCAmelCase				)
						# query, key and value matrices need special treatment
						read_in_q_k_v(_UpperCAmelCase							,     is_panoptic=_UpperCAmelCase				)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_a     =			'detr.model.' if is_panoptic else 'model.'
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith('detr'				)
																		    and not key.startswith('class_labels_classifier'				)
																		    and not key.startswith('bbox_predictor'				)
																		):
																								_a     =			state_dict.pop(_UpperCAmelCase				)
																								_a     =			val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								_a     =			state_dict.pop(_UpperCAmelCase				)
																								_a     =			val
																		elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
																								continue
																		else:
																								_a     =			state_dict.pop(_UpperCAmelCase				)
																								_a     =			val
												else:
																		if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
																								_a     =			state_dict.pop(_UpperCAmelCase				)
																								_a     =			val
    # finally, create HuggingFace model and load state dict
						_a     =			DetrForSegmentation(_UpperCAmelCase				) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase				)
						model.load_state_dict(_UpperCAmelCase				)
						model.eval()
						# verify our conversion
						_a     =			detr(_UpperCAmelCase				)
						_a     =			model(_UpperCAmelCase				)
						assert torch.allclose(outputs.logits							,     original_outputs['pred_logits']							,     atol=1e-4				)
						assert torch.allclose(outputs.pred_boxes							,     original_outputs['pred_boxes']							,     atol=1e-4				)
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks							,     original_outputs['pred_masks']							,     atol=1e-4				)

						# Save model and image processor
						logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
						Path(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
						model.save_pretrained(_UpperCAmelCase				)
						image_processor.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
		lowercase_								=    argparse.ArgumentParser()

		parser.add_argument(
		    '--model_name', default='detr_resnet50', type=str, help='Name of the DETR model you\'d like to convert.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		lowercase_								=    parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


