



'''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()
SCREAMING_SNAKE_CASE__     : Dict				=   logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      DetaConfig(
		    num_queries=900 ,		encoder_ffn_dim=2048 ,		decoder_ffn_dim=2048 ,		num_feature_levels=5 ,		assign_first_stage=SCREAMING_SNAKE_CASE ,		with_box_refine=SCREAMING_SNAKE_CASE ,		two_stage=SCREAMING_SNAKE_CASE ,		)

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

		return config







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      []

		# 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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      dct.pop(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      val







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







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      'http://images.cocodataset.org/val2017/000000039769.jpg'
		SCREAMING_SNAKE_CASE_						:str										=      Image.open(requests.get(SCREAMING_SNAKE_CASE ,		stream=SCREAMING_SNAKE_CASE     ).raw     )

		return im







@torch.no_grad()
def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      get_deta_config()

		# load original state dict
		if model_name == "deta-resnet-50":
				SCREAMING_SNAKE_CASE_						:List[Any]										=      'adet_checkpoint0011.pth'
		elif model_name == "deta-resnet-50-24-epochs":
				SCREAMING_SNAKE_CASE_						:Dict										=      'adet_2x_checkpoint0023.pth'
		else:
				raise ValueError(F'Model name {model_name} not supported'     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      hf_hub_download(repo_id='nielsr/deta-checkpoints' ,		filename=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.load(SCREAMING_SNAKE_CASE ,		map_location='cpu'     )['model']

		# rename keys
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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_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:
						SCREAMING_SNAKE_CASE_						:List[Any]										=      state_dict.pop(SCREAMING_SNAKE_CASE     )
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      val
				if "input_proj" in key:
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      state_dict.pop(SCREAMING_SNAKE_CASE     )
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						SCREAMING_SNAKE_CASE_						:Optional[Any]										=      state_dict.pop(SCREAMING_SNAKE_CASE     )
						SCREAMING_SNAKE_CASE_						:List[Any]										=      val

    # finally, create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE_						:List[Any]										=      DetaForObjectDetection(SCREAMING_SNAKE_CASE     )
		model.load_state_dict(SCREAMING_SNAKE_CASE     )
		model.eval()

		SCREAMING_SNAKE_CASE_						:Any										=      'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(SCREAMING_SNAKE_CASE     )

		# load image processor
		SCREAMING_SNAKE_CASE_						:str										=      DetaImageProcessor(format='coco_detection'     )

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

		# verify logits
		if model_name == "deta-resnet-50":
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.tensor(
				    [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]     )
				SCREAMING_SNAKE_CASE_						:Dict										=      torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]     )
		elif model_name == "deta-resnet-50-24-epochs":
				SCREAMING_SNAKE_CASE_						:int										=      torch.tensor(
				    [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]     )
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]     )

		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__":
							SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   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."""
							)
							SCREAMING_SNAKE_CASE__     : List[Any]				=   parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


import math
def lowerCamelCase_       (			A			:      int						):




      """simple docstring"""

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5		,					int(math.sqrt(A						) + 1						)		,					6						):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True
def lowerCamelCase_       (			A			:      int = 2_00_00_00						):




      """simple docstring"""
      return sum(num for num in range(3		,					A		,					2						) if is_prime(A						)						) + 2 if n > 2 else 0


if __name__ == "__main__":
 print(f'''{solution() = }''')



