



'''simple docstring'''



import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)
class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   List[Any]					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      nn.Linear(3					,     4					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      nn.BatchNormad(4					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      nn.Linear(4					,     5					)







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase					)					)					)


class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      ModelForTest()
													with TemporaryDirectory() as tmp_dir:
																				offload_state_dict(__UpperCamelCase					,     model.state_dict()					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      os.path.join(__UpperCamelCase					,     '''index.json'''					)
																				self.assertTrue(os.path.isfile(__UpperCamelCase					)					)
																				# TODO: add tests on what is inside the index

																				for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      os.path.join(__UpperCamelCase					,     F"""{key}.dat"""					)
																											self.assertTrue(os.path.isfile(__UpperCamelCase					)					)
																											# TODO: add tests on the fact weights are properly loaded







						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      [torch.floataa, torch.floataa, torch.bfloataa]

													for dtype in dtypes:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      torch.randn(2					,     3					,     dtype=__UpperCamelCase					)
																				with TemporaryDirectory() as tmp_dir:
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      offload_weight(__UpperCamelCase					,     '''weight'''					,     __UpperCamelCase					,     {}					)
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      os.path.join(__UpperCamelCase					,     '''weight.dat'''					)
																											self.assertTrue(os.path.isfile(__UpperCamelCase					)					)
																											self.assertDictEqual(__UpperCamelCase					,     {'''weight''': {'''shape''': [2, 3], '''dtype''': str(__UpperCamelCase					).split('''.'''					)[1]}}					)

																											SCREAMING_SNAKE_CASE       :   Dict         =      load_offloaded_weight(__UpperCamelCase					,     index['''weight''']					)
																											self.assertTrue(torch.equal(__UpperCamelCase					,     __UpperCamelCase					)					)







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      ModelForTest()
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model.state_dict()
													SCREAMING_SNAKE_CASE       :   str         =      {k: v for k, v in state_dict.items() if """linear2""" not in k}
													SCREAMING_SNAKE_CASE       :   Any         =      {k: v for k, v in state_dict.items() if """linear2""" in k}

													with TemporaryDirectory() as tmp_dir:
																				offload_state_dict(__UpperCamelCase					,     __UpperCamelCase					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      OffloadedWeightsLoader(state_dict=__UpperCamelCase					,     save_folder=__UpperCamelCase					)

																				# Every key is there with the right value
																				self.assertEqual(sorted(__UpperCamelCase					)					,     sorted(state_dict.keys()					)					)
																				for key, param in state_dict.items():
																											self.assertTrue(torch.allclose(__UpperCamelCase					,     weight_map[key]					)					)

													SCREAMING_SNAKE_CASE       :   int         =      {k: v for k, v in state_dict.items() if """weight""" in k}
													SCREAMING_SNAKE_CASE       :   List[str]         =      {k: v for k, v in state_dict.items() if """weight""" not in k}

													with TemporaryDirectory() as tmp_dir:
																				offload_state_dict(__UpperCamelCase					,     __UpperCamelCase					)
																				SCREAMING_SNAKE_CASE       :   str         =      OffloadedWeightsLoader(state_dict=__UpperCamelCase					,     save_folder=__UpperCamelCase					)

																				# Every key is there with the right value
																				self.assertEqual(sorted(__UpperCamelCase					)					,     sorted(state_dict.keys()					)					)
																				for key, param in state_dict.items():
																											self.assertTrue(torch.allclose(__UpperCamelCase					,     weight_map[key]					)					)

													with TemporaryDirectory() as tmp_dir:
																				offload_state_dict(__UpperCamelCase					,     __UpperCamelCase					)
																				# Duplicates are removed
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      OffloadedWeightsLoader(state_dict=__UpperCamelCase					,     save_folder=__UpperCamelCase					)

																				# Every key is there with the right value
																				self.assertEqual(sorted(__UpperCamelCase					)					,     sorted(state_dict.keys()					)					)
																				for key, param in state_dict.items():
																											self.assertTrue(torch.allclose(__UpperCamelCase					,     weight_map[key]					)					)







						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
													SCREAMING_SNAKE_CASE       :   Any         =      extract_submodules_state_dict(__UpperCamelCase					,     ['''a.1''', '''a.2''']					)
													self.assertDictEqual(__UpperCamelCase					,     {'''a.1''': 0, '''a.2''': 2}					)

													SCREAMING_SNAKE_CASE       :   Any         =      {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
													SCREAMING_SNAKE_CASE       :   Any         =      extract_submodules_state_dict(__UpperCamelCase					,     ['''a.1''', '''a.2''']					)
													self.assertDictEqual(__UpperCamelCase					,     {'''a.1.a''': 0, '''a.2.a''': 2}					)





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:     List[Any]         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Optional[Any]  =		SwinConfig(
						    embed_dim=192		, depths=(2, 2, 18, 2)		, num_heads=(6, 12, 24, 48)		, window_size=12		, out_features=["""stage2""", """stage3""", """stage4"""]		, )

						UpperCamelCase_		:							Optional[Any]  =		DetaConfig(
						    backbone_config=_SCREAMING_SNAKE_CASE		, num_queries=900		, encoder_ffn_dim=2048		, decoder_ffn_dim=2048		, num_feature_levels=5		, assign_first_stage=_SCREAMING_SNAKE_CASE		, with_box_refine=_SCREAMING_SNAKE_CASE		, two_stage=_SCREAMING_SNAKE_CASE		, )

						# set labels
						UpperCamelCase_		:							Dict  =		"""huggingface/label-files"""
						if "o365" in model_name:
												UpperCamelCase_		:							List[Any]  =		366
												UpperCamelCase_		:							str  =		"""object365-id2label.json"""
						else:
												UpperCamelCase_		:							Any  =		91
												UpperCamelCase_		:							Optional[int]  =		"""coco-detection-id2label.json"""

						UpperCamelCase_		:							Union[str, Any]  =		num_labels
						UpperCamelCase_		:							int  =		json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, repo_type="""dataset"""			)			)		, """r"""			)			)
						UpperCamelCase_		:							Optional[Any]  =		{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
						UpperCamelCase_		:							List[str]  =		idalabel
						UpperCamelCase_		:							Union[str, Any]  =		{v: k for k, v in idalabel.items()}

						return config






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							List[str]  =		[]

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")			)
						rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")			)
						rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")			)
						rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")			)
						# stages
						for i in range(len(config.backbone_config.depths			)			):
												for j in range(config.backbone_config.depths[i]			):
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')			)

												if i < 3:
																		rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')			)
																		rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')			)

						rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")			)
						rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")			)
						rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")			)
						rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")			)
						rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")			)
						rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")			)

						# transformer encoder
						for i in range(config.encoder_layers			):
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')			)
												rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')			)

						# transformer decoder
						for i in range(config.decoder_layers			):
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')			)
												rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')			)

						# fmt: on

						return rename_keys






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Dict  =		dct.pop(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							str  =		val






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Tuple			):
						UpperCamelCase_		:							Union[str, Any]  =		[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
						for i in range(len(backbone_config.depths			)			):
												UpperCamelCase_		:							Dict  =		num_features[i]
												for j in range(backbone_config.depths[i]			):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		UpperCamelCase_		:							List[Any]  =		state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
																		UpperCamelCase_		:							Tuple  =		state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''			)
																		# next, add query, keys and values (in that order) to the state dict
																		UpperCamelCase_		:							Dict  =		in_proj_weight[:dim, :]
																		UpperCamelCase_		:							Dict  =		in_proj_bias[: dim]
																		UpperCamelCase_		:							Any  =		in_proj_weight[
																		    dim : dim * 2, :
																		]
																		UpperCamelCase_		:							Optional[int]  =		in_proj_bias[
																		    dim : dim * 2
																		]
																		UpperCamelCase_		:							List[str]  =		in_proj_weight[
																		    -dim :, :
																		]
																		UpperCamelCase_		:							Optional[int]  =		in_proj_bias[-dim :]
																		# fmt: on






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				Optional[int]			):
						# transformer decoder self-attention layers
						UpperCamelCase_		:							List[Any]  =		config.d_model
						for i in range(config.decoder_layers			):
												# read in weights + bias of input projection layer of self-attention
												UpperCamelCase_		:							str  =		state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
												UpperCamelCase_		:							Optional[Any]  =		state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''			)
												# next, add query, keys and values (in that order) to the state dict
												UpperCamelCase_		:							Optional[Any]  =		in_proj_weight[:hidden_size, :]
												UpperCamelCase_		:							int  =		in_proj_bias[:hidden_size]
												UpperCamelCase_		:							Dict  =		in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												UpperCamelCase_		:							Union[str, Any]  =		in_proj_bias[hidden_size : hidden_size * 2]
												UpperCamelCase_		:							Dict  =		in_proj_weight[-hidden_size:, :]
												UpperCamelCase_		:							Dict  =		in_proj_bias[-hidden_size:]






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Optional[Any]  =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase_		:							int  =		Image.open(requests.get(_SCREAMING_SNAKE_CASE		, stream=_SCREAMING_SNAKE_CASE			).raw			)

						return im






@torch.no_grad()
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Tuple  =		get_deta_config(_SCREAMING_SNAKE_CASE			)

						# load original state dict
						if model_name == "deta-swin-large":
												UpperCamelCase_		:							int  =		hf_hub_download(repo_id="""nielsr/deta-checkpoints"""		, filename="""adet_swin_ft.pth"""			)
						elif model_name == "deta-swin-large-o365":
												UpperCamelCase_		:							int  =		hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""		, filename="""deta_swin_pt_o365.pth"""			)
						else:
												raise ValueError(f'''Model name {model_name} not supported'''			)

						UpperCamelCase_		:							Union[str, Any]  =		torch.load(_SCREAMING_SNAKE_CASE		, map_location="""cpu"""			)["""model"""]

						# original state dict
						for name, param in state_dict.items():
												print(_SCREAMING_SNAKE_CASE		, param.shape			)

						# rename keys
						UpperCamelCase_		:							List[str]  =		create_rename_keys(_SCREAMING_SNAKE_CASE			)
						for src, dest in rename_keys:
												rename_key(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						read_in_swin_q_k_v(_SCREAMING_SNAKE_CASE		, config.backbone_config			)
						read_in_decoder_q_k_v(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		UpperCamelCase_		:							Dict  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							Optional[Any]  =		val
												if "input_proj" in key:
																		UpperCamelCase_		:							Tuple  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							int  =		val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		UpperCamelCase_		:							int  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							str  =		val

    # finally, create HuggingFace model and load state dict
						UpperCamelCase_		:							List[Any]  =		DetaForObjectDetection(_SCREAMING_SNAKE_CASE			)
						model.load_state_dict(_SCREAMING_SNAKE_CASE			)
						model.eval()

						UpperCamelCase_		:							Dict  =		"""cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(_SCREAMING_SNAKE_CASE			)

						# load image processor
						UpperCamelCase_		:							Optional[Any]  =		DetaImageProcessor(format="""coco_detection"""			)

						# verify our conversion on image
						UpperCamelCase_		:							Optional[int]  =		prepare_img()
						UpperCamelCase_		:							Any  =		processor(images=_SCREAMING_SNAKE_CASE		, return_tensors="""pt"""			)
						UpperCamelCase_		:							int  =		encoding["""pixel_values"""]
						UpperCamelCase_		:							Tuple  =		model(pixel_values.to(_SCREAMING_SNAKE_CASE			)			)

						# verify logits
						print("""Logits:"""		, outputs.logits[0, :3, :3]			)
						print("""Boxes:"""		, outputs.pred_boxes[0, :3, :3]			)
						if model_name == "deta-swin-large":
												UpperCamelCase_		:							Optional[Any]  =		torch.tensor(
												    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]			)
												UpperCamelCase_		:							Optional[Any]  =		torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]			)
						elif model_name == "deta-swin-large-o365":
												UpperCamelCase_		:							str  =		torch.tensor(
												    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]			)
												UpperCamelCase_		:							Union[str, Any]  =		torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]			)
						assert torch.allclose(outputs.logits[0, :3, :3]		, expected_logits.to(_SCREAMING_SNAKE_CASE			)		, atol=1E-4			)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]		, expected_boxes.to(_SCREAMING_SNAKE_CASE			)		, atol=1E-4			)
						print("""Everything ok!"""			)

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''			)
												Path(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
												model.save_pretrained(_SCREAMING_SNAKE_CASE			)
												processor.save_pretrained(_SCREAMING_SNAKE_CASE			)

						# Push to hub
						if push_to_hub:
												print("""Pushing model and processor to hub..."""			)
												model.push_to_hub(f'''jozhang97/{model_name}'''			)
												processor.push_to_hub(f'''jozhang97/{model_name}'''			)


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE		:     str         =	argparse.ArgumentParser()

	parser.add_argument(
	    "--model_name",
	    type=str,
	    default="deta-swin-large",
	    choices=["deta-swin-large", "deta-swin-large-o365"],
	    help="Name of the 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.",
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)
	SCREAMING_SNAKE_CASE		:     int         =	parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






