



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()
A__  :  Optional[int]								=					logging.get_logger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		Any ):
		lowerCAmelCase_			:     Union[str, Any]									= SwinConfig(
		    embed_dim=192     ,depths=(2, 2, 18, 2)     ,num_heads=(6, 12, 24, 48)     ,window_size=12     ,out_features=['''stage2''', '''stage3''', '''stage4''']     ,)

		lowerCAmelCase_			:     Union[str, Any]									= DetaConfig(
		    backbone_config=__UpperCamelCase     ,num_queries=900     ,encoder_ffn_dim=2048     ,decoder_ffn_dim=2048     ,num_feature_levels=5     ,assign_first_stage=__UpperCamelCase     ,with_box_refine=__UpperCamelCase     ,two_stage=__UpperCamelCase     ,)

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

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

		return config


def 			UpperCamelCase(  __UpperCamelCase							:		Tuple ):
		lowerCAmelCase_			:     Union[str, Any]									= []

		# 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 			UpperCamelCase(  __UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		Tuple ):
		lowerCAmelCase_			:     Any									= dct.pop(__UpperCamelCase )
		lowerCAmelCase_			:     Tuple									= val


def 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		int ):
		lowerCAmelCase_			:     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 ) ):
				lowerCAmelCase_			:     int									= 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)
						lowerCAmelCase_			:     List[Any]									= state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight""" )
						lowerCAmelCase_			:     str									= 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
						lowerCAmelCase_			:     Union[str, Any]									= in_proj_weight[:dim, :]
						lowerCAmelCase_			:     List[str]									= in_proj_bias[: dim]
						lowerCAmelCase_			:     Union[str, Any]									= in_proj_weight[
						    dim : dim * 2, :
						]
						lowerCAmelCase_			:     Any									= in_proj_bias[
						    dim : dim * 2
						]
						lowerCAmelCase_			:     List[str]									= in_proj_weight[
						    -dim :, :
						]
						lowerCAmelCase_			:     List[Any]									= in_proj_bias[-dim :]
						# fmt: on


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		List[str] ):
		# transformer decoder self-attention layers
		lowerCAmelCase_			:     List[Any]									= config.d_model
		for i in range(config.decoder_layers ):
				# read in weights + bias of input projection layer of self-attention
				lowerCAmelCase_			:     Any									= state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
				lowerCAmelCase_			:     int									= 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
				lowerCAmelCase_			:     List[str]									= in_proj_weight[:hidden_size, :]
				lowerCAmelCase_			:     List[Any]									= in_proj_bias[:hidden_size]
				lowerCAmelCase_			:     Union[str, Any]									= in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				lowerCAmelCase_			:     Union[str, Any]									= in_proj_bias[hidden_size : hidden_size * 2]
				lowerCAmelCase_			:     str									= in_proj_weight[-hidden_size:, :]
				lowerCAmelCase_			:     Dict									= in_proj_bias[-hidden_size:]


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Any									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_			:     str									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )

		return im


@torch.no_grad()
def 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Any     ,__UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Optional[Any]									= get_deta_config(__UpperCamelCase )

		# load original state dict
		if model_name == "deta-swin-large":
				lowerCAmelCase_			:     Dict									= hf_hub_download(repo_id='''nielsr/deta-checkpoints'''     ,filename='''adet_swin_ft.pth''' )
		elif model_name == "deta-swin-large-o365":
				lowerCAmelCase_			:     Optional[Any]									= 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""" )

		lowerCAmelCase_			:     List[Any]									= torch.load(__UpperCamelCase     ,map_location='''cpu''' )['''model''']

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

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

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

    # finally, create HuggingFace model and load state dict
		lowerCAmelCase_			:     Optional[int]									= DetaForObjectDetection(__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )
		model.eval()

		lowerCAmelCase_			:     str									= '''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(__UpperCamelCase )

		# load image processor
		lowerCAmelCase_			:     List[str]									= DetaImageProcessor(format='''coco_detection''' )

		# verify our conversion on image
		lowerCAmelCase_			:     Optional[int]									= prepare_img()
		lowerCAmelCase_			:     Union[str, Any]									= processor(images=__UpperCamelCase     ,return_tensors='''pt''' )
		lowerCAmelCase_			:     Any									= encoding['''pixel_values''']
		lowerCAmelCase_			:     Dict									= model(pixel_values.to(__UpperCamelCase ) )

		# verify logits
		print('''Logits:'''     ,outputs.logits[0, :3, :3] )
		print('''Boxes:'''     ,outputs.pred_boxes[0, :3, :3] )
		if model_name == "deta-swin-large":
				lowerCAmelCase_			:     str									= torch.tensor(
				    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]] )
				lowerCAmelCase_			:     str									= torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]] )
		elif model_name == "deta-swin-large-o365":
				lowerCAmelCase_			:     str									= torch.tensor(
				    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]] )
				lowerCAmelCase_			:     Tuple									= torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]] )
		assert torch.allclose(outputs.logits[0, :3, :3]     ,expected_logits.to(__UpperCamelCase )     ,atol=1e-4 )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,expected_boxes.to(__UpperCamelCase )     ,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(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
				model.save_pretrained(__UpperCamelCase )
				processor.save_pretrained(__UpperCamelCase )

		# 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__":
						A__  :  Dict								=					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.'''
						)
						A__  :  Dict								=					parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













import numpy as np







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return 1 / (1 + np.exp(-vector			))







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return vector * sigmoid(1.7_0_2 * vector			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()