


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 			_a      (    ):
     a_     :  int				      =     DetaConfig(
         num_queries=9_0_0	,	encoder_ffn_dim=2_0_4_8	,	decoder_ffn_dim=2_0_4_8	,	num_feature_levels=5	,	assign_first_stage=__UpperCamelCase	,	with_box_refine=__UpperCamelCase	,	two_stage=__UpperCamelCase	,	)

     # set labels
     a_     :  Union[str, Any]				      =     9_1
     a_     :  int				      =     """huggingface/label-files"""
     a_     :  List[str]				      =     """coco-detection-id2label.json"""
     a_     :  int				      =     json.load(open(cached_download(hf_hub_url(__UpperCamelCase	,	__UpperCamelCase	,	repo_type="""dataset"""			)			)	,	"""r"""			)			)
     a_     :  Any				      =     {int(__UpperCamelCase			): v for k, v in idalabel.items()}
     a_     :  int				      =     idalabel
     a_     :  str				      =     {v: k for k, v in idalabel.items()}

     return config



def 			_a      (    __UpperCamelCase			):
     a_     :  List[str]				      =     []

     # 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      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Any				      =     dct.pop(__UpperCamelCase			)
     a_     :  Union[str, Any]				      =     val



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



def 			_a      (    ):
     a_     :  Optional[int]				      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
     a_     :  Optional[Any]				      =     Image.open(requests.get(__UpperCamelCase	,	stream=__UpperCamelCase			).raw			)

     return im



@torch.no_grad()
def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Any				      =     get_deta_config()

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

     # rename keys
     a_     :  List[Any]				      =     create_rename_keys(__UpperCamelCase			)
     for src, dest in rename_keys:
          rename_key(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
     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:
               a_     :  Tuple				      =     state_dict.pop(__UpperCamelCase			)
               a_     :  Any				      =     val
          if "input_proj" in key:
               a_     :  str				      =     state_dict.pop(__UpperCamelCase			)
               a_     :  str				      =     val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               a_     :  Any				      =     state_dict.pop(__UpperCamelCase			)
               a_     :  Tuple				      =     val

    # finally, create HuggingFace model and load state dict
     a_     :  Union[str, Any]				      =     DetaForObjectDetection(__UpperCamelCase			)
     model.load_state_dict(__UpperCamelCase			)
     model.eval()

     a_     :  List[str]				      =     """cuda""" if torch.cuda.is_available() else """cpu"""
     model.to(__UpperCamelCase			)

     # load image processor
     a_     :  List[str]				      =     DetaImageProcessor(format="""coco_detection"""			)

     # verify our conversion on image
     a_     :  Tuple				      =     prepare_img()
     a_     :  Tuple				      =     processor(images=__UpperCamelCase	,	return_tensors="""pt"""			)
     a_     :  str				      =     encoding["""pixel_values"""]
     a_     :  Any				      =     model(pixel_values.to(__UpperCamelCase			)			)

     # verify logits
     if model_name == "deta-resnet-50":
          a_     :  List[Any]				      =     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]]			)
          a_     :  str				      =     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":
          a_     :  Dict				      =     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]]			)
          a_     :  Union[str, Any]				      =     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(__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__":
       __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)





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
											# Log results
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   od / 'test_generations.txt'
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											try:
															__lowercase								=   pl_module.model.model.num_parameters()
											except AttributeError:
															__lowercase								=   pl_module.model.num_parameters()

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}							)





							@rank_zero_only
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





