


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()
__snake_case     =		logging.get_logger(__name__)






def 						_A						(						)			->				str:


     """simple docstring"""


     __UpperCamelCase				=	DetaConfig(
         num_queries=9_00			,       encoder_ffn_dim=20_48			,       decoder_ffn_dim=20_48			,       num_feature_levels=5			,       assign_first_stage=_lowercase			,       with_box_refine=_lowercase			,       two_stage=_lowercase			,       )

     # set labels
     __UpperCamelCase				=	91
     __UpperCamelCase				=	'huggingface/label-files'
     __UpperCamelCase				=	'coco-detection-id2label.json'
     __UpperCamelCase				=	json.load(open(cached_download(hf_hub_url(_lowercase			,       _lowercase			,       repo_type='dataset'				)				)			,       'r'				)				)
     __UpperCamelCase				=	{int(_lowercase				): v for k, v in idalabel.items()}
     __UpperCamelCase				=	idalabel
     __UpperCamelCase				=	{v: k for k, v in idalabel.items()}

     return config






def 						_A						(						_lowercase				)			->				str:


     """simple docstring"""


     __UpperCamelCase				=	[]

     # 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 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				Any:


     """simple docstring"""


     __UpperCamelCase				=	dct.pop(_lowercase				)
     __UpperCamelCase				=	val






def 						_A						(						_lowercase			,       _lowercase				)			->				Optional[Any]:


     """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						(						)			->				int:


     """simple docstring"""


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

     return im






@torch.no_grad()
def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	get_deta_config()

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

     # rename keys
     __UpperCamelCase				=	create_rename_keys(_lowercase				)
     for src, dest in rename_keys:
          rename_key(_lowercase			,       _lowercase			,       _lowercase				)
     read_in_decoder_q_k_v(_lowercase			,       _lowercase				)

     # 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(_lowercase				)
               __UpperCamelCase				=	val
          if "input_proj" in key:
               __UpperCamelCase				=	state_dict.pop(_lowercase				)
               __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(_lowercase				)
               __UpperCamelCase				=	val

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

     __UpperCamelCase				=	'cuda' if torch.cuda.is_available() else 'cpu'
     model.to(_lowercase				)

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

     # verify our conversion on image
     __UpperCamelCase				=	prepare_img()
     __UpperCamelCase				=	processor(images=_lowercase			,       return_tensors='pt'				)
     __UpperCamelCase				=	encoding['pixel_values']
     __UpperCamelCase				=	model(pixel_values.to(_lowercase				)				)

     # verify logits
     if model_name == "deta-resnet-50":
          __UpperCamelCase				=	torch.tensor(
              [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]				)
          __UpperCamelCase				=	torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]				)
     elif model_name == "deta-resnet-50-24-epochs":
          __UpperCamelCase				=	torch.tensor(
              [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]				)
          __UpperCamelCase				=	torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]				)

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

     # 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__":
    __snake_case     =		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.'''
    )
    __snake_case     =		parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import math






def 						_A						(						_lowercase				)			->				bool:


     """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(_lowercase				) + 1				)			,       6				):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def 						_A						(						_lowercase = 2_00_00_00				)			->				int:


     """simple docstring"""


     return sum(num for num in range(3			,       _lowercase			,       2				) if is_prime(_lowercase				)				) + 2 if n > 2 else 0


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


