
"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''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=__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
				_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(__UpperCamelCase					,     __UpperCamelCase					,     repo_type='''dataset'''				)				)					,     '''r'''				)				)
				_UpperCAmelCase					=    {int(__UpperCamelCase				): v for k, v in idalabel.items()}
				_UpperCAmelCase					=    idalabel
				_UpperCAmelCase					=    {v: k for k, v in idalabel.items()}

				return config







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				_UpperCAmelCase					=    dct.pop(__UpperCamelCase				)
				_UpperCAmelCase					=    val







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''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  lowercase	(      ):






				'''simple docstring'''



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

				return im







@torch.no_grad()
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_deta_config(__UpperCamelCase				)

				# 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(__UpperCamelCase					,     map_location='''cpu'''				)["""model"""]

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

				# rename keys
				_UpperCAmelCase					=    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:
												_UpperCAmelCase					=    state_dict.pop(__UpperCamelCase				)
												_UpperCAmelCase					=    val
								if "input_proj" in key:
												_UpperCAmelCase					=    state_dict.pop(__UpperCamelCase				)
												_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(__UpperCamelCase				)
												_UpperCAmelCase					=    val

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

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

				# load image processor
				_UpperCAmelCase					=    DetaImageProcessor(format='''coco_detection'''				)

				# verify our conversion on image
				_UpperCAmelCase					=    prepare_img()
				_UpperCAmelCase					=    processor(images=__UpperCamelCase					,     return_tensors='''pt'''				)
				_UpperCAmelCase					=    encoding["""pixel_values"""]
				_UpperCAmelCase					=    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":
								_UpperCAmelCase					=    torch.tensor(
								    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]				)
								_UpperCAmelCase					=    torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]				)
				elif model_name == "deta-swin-large-o365":
								_UpperCAmelCase					=    torch.tensor(
								    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]				)
								_UpperCAmelCase					=    torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]				)
				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"""


class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    list[int]   )->None:
							_UpperCAmelCase					=    len(__UpperCamelCase   )
							_UpperCAmelCase					=    [0] * len_array

							if len_array > 0:
											_UpperCAmelCase					=    array[0]

							for i in range(1				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    self.prefix_sum[i - 1] + array[i]



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int   )->int:
							if start == 0:
											return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    int   )->bool:
							_UpperCAmelCase					=    {0}
							for sum_item in self.prefix_sum:
											if sum_item - target_sum in sums:
															return True

											sums.add(__UpperCamelCase   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()



