





"""simple docstring"""





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()
lowerCAmelCase__   	=	logging.get_logger(__name__)


def 					a__    (    _SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		SwinConfig(
		    embed_dim=192     ,		depths=(2, 2, 18, 2)     ,		num_heads=(6, 12, 24, 48)     ,		window_size=12     ,		out_features=["stage2", "stage3", "stage4"]     ,		)

		UpperCamelCase      =		DetaConfig(
		    backbone_config=_SCREAMING_SNAKE_CASE     ,		num_queries=900     ,		encoder_ffn_dim=2_048     ,		decoder_ffn_dim=2_048     ,		num_feature_levels=5     ,		assign_first_stage=_SCREAMING_SNAKE_CASE     ,		with_box_refine=_SCREAMING_SNAKE_CASE     ,		two_stage=_SCREAMING_SNAKE_CASE     ,		)

		# set labels
		UpperCamelCase      =		"huggingface/label-files"
		if "o365" in model_name:
				UpperCamelCase      =		366
				UpperCamelCase      =		"object365-id2label.json"
		else:
				UpperCamelCase      =		91
				UpperCamelCase      =		"coco-detection-id2label.json"

		UpperCamelCase      =		num_labels
		UpperCamelCase      =		json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		repo_type="dataset"			)			)     ,		"r"			)			)
		UpperCamelCase      =		{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
		UpperCamelCase      =		idalabel
		UpperCamelCase      =		{v: k for k, v in idalabel.items()}

		return config


def 					a__    (    _SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		[]

		# 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 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		dct.pop(_SCREAMING_SNAKE_CASE			)
		UpperCamelCase      =		val


def 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
		for i in range(len(backbone_config.depths			)			):
				UpperCamelCase      =		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      =		state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"			)
						UpperCamelCase      =		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      =		in_proj_weight[:dim, :]
						UpperCamelCase      =		in_proj_bias[: dim]
						UpperCamelCase      =		in_proj_weight[
						    dim : dim * 2, :
						]
						UpperCamelCase      =		in_proj_bias[
						    dim : dim * 2
						]
						UpperCamelCase      =		in_proj_weight[
						    -dim :, :
						]
						UpperCamelCase      =		in_proj_bias[-dim :]
						# fmt: on


def 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		config.d_model
		for i in range(config.decoder_layers			):
				# read in weights + bias of input projection layer of self-attention
				UpperCamelCase      =		state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"			)
				UpperCamelCase      =		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      =		in_proj_weight[:hidden_size, :]
				UpperCamelCase      =		in_proj_bias[:hidden_size]
				UpperCamelCase      =		in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				UpperCamelCase      =		in_proj_bias[hidden_size : hidden_size * 2]
				UpperCamelCase      =		in_proj_weight[-hidden_size:, :]
				UpperCamelCase      =		in_proj_bias[-hidden_size:]


def 					a__    (    ):




		"""simple docstring"""


		UpperCamelCase      =		"http://images.cocodataset.org/val2017/000000039769.jpg"
		UpperCamelCase      =		Image.open(requests.get(_SCREAMING_SNAKE_CASE     ,		stream=_SCREAMING_SNAKE_CASE			).raw			)

		return im


@torch.no_grad()
def 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		get_deta_config(_SCREAMING_SNAKE_CASE			)

		# load original state dict
		if model_name == "deta-swin-large":
				UpperCamelCase      =		hf_hub_download(repo_id="nielsr/deta-checkpoints"     ,		filename="adet_swin_ft.pth"			)
		elif model_name == "deta-swin-large-o365":
				UpperCamelCase      =		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      =		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      =		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      =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase      =		val
				if "input_proj" in key:
						UpperCamelCase      =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase      =		val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						UpperCamelCase      =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase      =		val

    # finally, create HuggingFace model and load state dict
		UpperCamelCase      =		DetaForObjectDetection(_SCREAMING_SNAKE_CASE			)
		model.load_state_dict(_SCREAMING_SNAKE_CASE			)
		model.eval()

		UpperCamelCase      =		"cuda" if torch.cuda.is_available() else "cpu"
		model.to(_SCREAMING_SNAKE_CASE			)

		# load image processor
		UpperCamelCase      =		DetaImageProcessor(format="coco_detection"			)

		# verify our conversion on image
		UpperCamelCase      =		prepare_img()
		UpperCamelCase      =		processor(images=_SCREAMING_SNAKE_CASE     ,		return_tensors="pt"			)
		UpperCamelCase      =		encoding["pixel_values"]
		UpperCamelCase      =		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      =		torch.tensor(
				    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]			)
				UpperCamelCase      =		torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]			)
		elif model_name == "deta-swin-large-o365":
				UpperCamelCase      =		torch.tensor(
				    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]			)
				UpperCamelCase      =		torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]			)
		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__":
						lowerCAmelCase__   	=	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.'''
						)
						lowerCAmelCase__   	=	parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


