


"""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()
A  :     List[Any]    					=    logging.get_logger(__name__)


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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

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

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

			return config


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []

			# 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			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    dct.pop(__UpperCamelCase   )
			__lowerCAmelCase       					=    val


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	):

			'''simple docstring'''




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

			return im


@torch.no_grad()
def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    get_deta_config(__UpperCamelCase   )

			# load original state dict
			if model_name == "deta-swin-large":
						__lowerCAmelCase       					=    hf_hub_download(repo_id="nielsr/deta-checkpoints"					,				filename="adet_swin_ft.pth"   )
			elif model_name == "deta-swin-large-o365":
						__lowerCAmelCase       					=    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       					=    torch.load(__UpperCamelCase					,				map_location="cpu"   )["""model"""]

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

			# rename keys
			__lowerCAmelCase       					=    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       					=    state_dict.pop(__UpperCamelCase   )
									__lowerCAmelCase       					=    val
						if "input_proj" in key:
									__lowerCAmelCase       					=    state_dict.pop(__UpperCamelCase   )
									__lowerCAmelCase       					=    val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									__lowerCAmelCase       					=    state_dict.pop(__UpperCamelCase   )
									__lowerCAmelCase       					=    val

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

			__lowerCAmelCase       					=    """cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(__UpperCamelCase   )

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

			# verify our conversion on image
			__lowerCAmelCase       					=    prepare_img()
			__lowerCAmelCase       					=    processor(images=__UpperCamelCase					,				return_tensors="pt"   )
			__lowerCAmelCase       					=    encoding["""pixel_values"""]
			__lowerCAmelCase       					=    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       					=    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]]   )
						__lowerCAmelCase       					=    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":
						__lowerCAmelCase       					=    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]]   )
						__lowerCAmelCase       					=    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(__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  :     List[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."
	)
	A  :     Union[str, Any]    					=    parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""


A  :     List[Any]    					=    [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A  :     Optional[int]    					=    [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A  :     Optional[int]    					=    {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			assert len(str(_UpperCamelCase   )   ) > 2, "year should be in YYYY format"
			assert 1 <= month <= 12, "month should be between 1 to 12"
			assert 1 <= day <= 31, "day should be between 1 to 31"

			# Doomsday algorithm:
			__lowerCAmelCase       					=    year // 100
			__lowerCAmelCase       					=    (5 * (century % 4) + 2) % 7
			__lowerCAmelCase       					=    year % 100
			__lowerCAmelCase       					=    centurian % 12
			__lowerCAmelCase       					=    (
			    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
			) % 7
			__lowerCAmelCase       					=    (
			    DOOMSDAY_NOT_LEAP[month - 1]
			    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
			    else DOOMSDAY_LEAP[month - 1]
			)
			__lowerCAmelCase       					=    (dooms_day + day - day_anchor) % 7
			return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
	import doctest

	doctest.testmod()




