





"""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
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_          =	logging.get_logger(__name__)



def   __lowerCamelCase     (							)    ->   Any:
						__SCREAMING_SNAKE_CASE     :Dict	       =						DetaConfig(
						    num_queries=9_00				,     encoder_ffn_dim=20_48				,     decoder_ffn_dim=20_48				,     num_feature_levels=5				,     assign_first_stage=__a				,     with_box_refine=__a				,     two_stage=__a				,     )

						# set labels
						__SCREAMING_SNAKE_CASE     :Dict	       =						91
						__SCREAMING_SNAKE_CASE     :Dict	       =						'''huggingface/label-files'''
						__SCREAMING_SNAKE_CASE     :List[str]	       =						'''coco-detection-id2label.json'''
						__SCREAMING_SNAKE_CASE     :Tuple	       =						json.load(open(cached_download(hf_hub_url(__a				,     __a				,     repo_type='''dataset'''							)							)				,     '''r'''							)							)
						__SCREAMING_SNAKE_CASE     :int	       =						{int(__a							): v for k, v in idalabel.items()}
						__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						idalabel
						__SCREAMING_SNAKE_CASE     :Any	       =						{v: k for k, v in idalabel.items()}

						return config



def   __lowerCamelCase     (							a_  :	Tuple							)    ->   Union[str, Any]:
						__SCREAMING_SNAKE_CASE     :int	       =						[]

						# stem
						# fmt: off
						rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')							)
						rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')							)
						rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')							)
						rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')							)
						rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''')							)
						# stages
						for stage_idx in range(len(config.backbone_config.depths							)							):
												for layer_idx in range(config.backbone_config.depths[stage_idx]							):
																		# shortcut
																		if layer_idx == 0:
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																								    )							)
																		# 3 convs
																		for i in range(3							):
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																								    )							)
																								rename_keys.append(
																								    (
																								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																								    )							)
    # 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     (							a_  :	Dict				,     a_  :	Tuple				,     a_  :	Optional[int]							)    ->   Union[str, Any]:
						__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						dct.pop(__a							)
						__SCREAMING_SNAKE_CASE     :List[Any]	       =						val



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



def   __lowerCamelCase     (							)    ->   int:
						__SCREAMING_SNAKE_CASE     :Tuple	       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						Image.open(requests.get(__a				,     stream=__a							).raw							)

						return im



@torch.no_grad()
def   __lowerCamelCase     (							a_  :	str				,     a_  :	List[str]				,     a_  :	List[str]							)    ->   Optional[Any]:
						__SCREAMING_SNAKE_CASE     :str	       =						get_deta_config()

						# load original state dict
						if model_name == "deta-resnet-50":
												__SCREAMING_SNAKE_CASE     :int	       =						'''adet_checkpoint0011.pth'''
						elif model_name == "deta-resnet-50-24-epochs":
												__SCREAMING_SNAKE_CASE     :str	       =						'''adet_2x_checkpoint0023.pth'''
						else:
												raise ValueError(f'''Model name {model_name} not supported'''							)
						__SCREAMING_SNAKE_CASE     :Optional[int]	       =						hf_hub_download(repo_id='''nielsr/deta-checkpoints'''				,     filename=__a							)
						__SCREAMING_SNAKE_CASE     :str	       =						torch.load(__a				,     map_location='''cpu'''							)['''model''']

						# rename keys
						__SCREAMING_SNAKE_CASE     :int	       =						create_rename_keys(__a							)
						for src, dest in rename_keys:
												rename_key(__a				,     __a				,     __a							)
						read_in_decoder_q_k_v(__a				,     __a							)

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

    # finally, create HuggingFace model and load state dict
						__SCREAMING_SNAKE_CASE     :str	       =						DetaForObjectDetection(__a							)
						model.load_state_dict(__a							)
						model.eval()

						__SCREAMING_SNAKE_CASE     :int	       =						'''cuda''' if torch.cuda.is_available() else '''cpu'''
						model.to(__a							)

						# load image processor
						__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						DetaImageProcessor(format='''coco_detection'''							)

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

						# verify logits
						if model_name == "deta-resnet-50":
												__SCREAMING_SNAKE_CASE     :Optional[int]	       =						torch.tensor(
												    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]							)
												__SCREAMING_SNAKE_CASE     :Tuple	       =						torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]							)
						elif model_name == "deta-resnet-50-24-epochs":
												__SCREAMING_SNAKE_CASE     :Any	       =						torch.tensor(
												    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]							)
												__SCREAMING_SNAKE_CASE     :List[Any]	       =						torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]							)

						assert torch.allclose(outputs.logits[0, :3, :3]				,     expected_logits.to(__a							)				,     atol=1e-4							)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,     expected_boxes.to(__a							)				,     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(__a							).mkdir(exist_ok=__a							)
												model.save_pretrained(__a							)
												processor.save_pretrained(__a							)

						# 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-resnet-50",
		    choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
		    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"""
def   __lowerCamelCase     (							a_  :	int = 10**6							)    ->   int:

      if n <= 0:
            raise ValueError('''Please enter an integer greater than 0'''							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						list(range(n + 1							)							)
      for number in range(2				,     n + 1							):
            if phi[number] == number:
                  phi[number] -= 1
                  for multiple in range(number * 2				,     n + 1				,     a_							):
                        __SCREAMING_SNAKE_CASE     :List[Any]	       =						(phi[multiple] // number) * (number - 1)

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						1
      for number in range(1				,     n + 1							):
            if (answer / phi[answer]) < (number / phi[number]):
                  __SCREAMING_SNAKE_CASE     :str	       =						number

      return answer


if __name__ == "__main__":
  print(solution())