





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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

def      a    (   _lowercase :  List[str]							)  ->Tuple:


		if "backbone.0.body" in orig_key:
				UpperCamelCase__								=						orig_key.replace("""backbone.0.body"""			,    """backbone.conv_encoder.model"""							)
		if "transformer" in orig_key:
				UpperCamelCase__								=						orig_key.replace("""transformer."""			,    """"""							)
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						UpperCamelCase__								=						orig_key.replace("""norm1"""			,    """self_attn_layer_norm"""							)
				else:
						UpperCamelCase__								=						orig_key.replace("""norm1"""			,    """encoder_attn_layer_norm"""							)
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						UpperCamelCase__								=						orig_key.replace("""norm2"""			,    """final_layer_norm"""							)
				else:
						UpperCamelCase__								=						orig_key.replace("""norm2"""			,    """self_attn_layer_norm"""							)
		if "norm3" in orig_key:
				UpperCamelCase__								=						orig_key.replace("""norm3"""			,    """final_layer_norm"""							)
		if "linear1" in orig_key:
				UpperCamelCase__								=						orig_key.replace("""linear1"""			,    """fc1"""							)
		if "linear2" in orig_key:
				UpperCamelCase__								=						orig_key.replace("""linear2"""			,    """fc2"""							)
		if "query_embed" in orig_key:
				UpperCamelCase__								=						orig_key.replace("""query_embed"""			,    """query_position_embeddings"""							)
		if "cross_attn" in orig_key:
				UpperCamelCase__								=						orig_key.replace("""cross_attn"""			,    """encoder_attn"""							)

		return orig_key

def      a    (   _lowercase :  Tuple							)  ->Dict:


		for i in range(6							):
				# read in weights + bias of input projection layer of self-attention
				UpperCamelCase__								=						state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""							)
				UpperCamelCase__								=						state_dict.pop(F"""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[:2_5_6, :]
				UpperCamelCase__								=						in_proj_bias[:2_5_6]
				UpperCamelCase__								=						in_proj_weight[2_5_6:5_1_2, :]
				UpperCamelCase__								=						in_proj_bias[2_5_6:5_1_2]
				UpperCamelCase__								=						in_proj_weight[-2_5_6:, :]
				UpperCamelCase__								=						in_proj_bias[-2_5_6:]

def      a    (   )  ->int:
		UpperCamelCase__								=						'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase__								=						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,    stream=_SCREAMING_SNAKE_CASE							).raw							)

		return im

@torch.no_grad()
def      a    (   _lowercase :  List[Any]			,    _lowercase :  Optional[Any]			,    _lowercase :  Tuple			,    _lowercase :  int			,    _lowercase :  Dict			,    _lowercase :  List[str]			,    _lowercase :  Dict			,    )  ->List[str]:
		UpperCamelCase__								=						DeformableDetrConfig()
		# set config attributes
		if single_scale:
				UpperCamelCase__								=						1
		UpperCamelCase__								=						dilation
		UpperCamelCase__								=						with_box_refine
		UpperCamelCase__								=						two_stage
		# set labels
		UpperCamelCase__								=						9_1
		UpperCamelCase__								=						'huggingface/label-files'
		UpperCamelCase__								=						'coco-detection-id2label.json'
		UpperCamelCase__								=						json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    repo_type="""dataset"""							)							)			,    """r"""							)							)
		UpperCamelCase__								=						{int(_SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
		UpperCamelCase__								=						idalabel
		UpperCamelCase__								=						{v: k for k, v in idalabel.items()}

		# load image processor
		UpperCamelCase__								=						DeformableDetrImageProcessor(format="""coco_detection"""							)

		# prepare image
		UpperCamelCase__								=						prepare_img()
		UpperCamelCase__								=						image_processor(images=_SCREAMING_SNAKE_CASE			,    return_tensors="""pt"""							)
		UpperCamelCase__								=						encoding['pixel_values']

		logger.info("""Converting model..."""							)

		# load original state dict
		UpperCamelCase__								=						torch.load(_SCREAMING_SNAKE_CASE			,    map_location="""cpu"""							)['model']
		# rename keys
		for key in state_dict.copy().keys():
				UpperCamelCase__								=						state_dict.pop(_SCREAMING_SNAKE_CASE							)
				UpperCamelCase__								=						val
		# query, key and value matrices need special treatment
		read_in_q_k_v(_SCREAMING_SNAKE_CASE							)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		UpperCamelCase__								=						'model.'
		for key in state_dict.copy().keys():
				if not key.startswith("""class_embed"""							) and not key.startswith("""bbox_embed"""							):
						UpperCamelCase__								=						state_dict.pop(_SCREAMING_SNAKE_CASE							)
						UpperCamelCase__								=						val
    # finally, create HuggingFace model and load state dict
		UpperCamelCase__								=						DeformableDetrForObjectDetection(_SCREAMING_SNAKE_CASE							)
		model.load_state_dict(_SCREAMING_SNAKE_CASE							)
		model.eval()

		UpperCamelCase__								=						'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(_SCREAMING_SNAKE_CASE							)
		# verify our conversion
		UpperCamelCase__								=						model(pixel_values.to(_SCREAMING_SNAKE_CASE							)							)

		UpperCamelCase__								=						torch.tensor(
		    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]							)
		UpperCamelCase__								=						torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]							)

		if single_scale:
				UpperCamelCase__								=						torch.tensor(
				    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]							)
				UpperCamelCase__								=						torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]							)

		if single_scale and dilation:
				UpperCamelCase__								=						torch.tensor(
				    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]							)
				UpperCamelCase__								=						torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]							)

		if with_box_refine:
				UpperCamelCase__								=						torch.tensor(
				    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]							)
				UpperCamelCase__								=						torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]							)

		if with_box_refine and two_stage:
				UpperCamelCase__								=						torch.tensor(
				    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]							)
				UpperCamelCase__								=						torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]							)

		print("""Logits:"""			,    outputs.logits[0, :3, :3]							)

		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!"""							)

		# Save model and image processor
		logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
		Path(_SCREAMING_SNAKE_CASE							).mkdir(exist_ok=_SCREAMING_SNAKE_CASE							)
		model.save_pretrained(_SCREAMING_SNAKE_CASE							)
		image_processor.save_pretrained(_SCREAMING_SNAKE_CASE							)

		# Push to hub
		if push_to_hub:
				UpperCamelCase__								=						'deformable-detr'
				model_name += "-single-scale" if single_scale else ""
				model_name += "-dc5" if dilation else ""
				model_name += "-with-box-refine" if with_box_refine else ""
				model_name += "-two-stage" if two_stage else ""
				print("""Pushing model to hub..."""							)
				model.push_to_hub(repo_path_or_name=_SCREAMING_SNAKE_CASE			,    organization="""nielsr"""			,    commit_message="""Add model"""							)


if __name__ == "__main__":
				__lowerCAmelCase 	=      argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_path",
				    type=str,
				    default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
				    help="Path to Pytorch checkpoint (.pth file) you\'d like to convert.",
				)
				parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
				parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
				parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
				parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    required=True,
				    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_deformable_detr_checkpoint(
				    args.checkpoint_path,
				    args.single_scale,
				    args.dilation,
				    args.with_box_refine,
				    args.two_stage,
				    args.pytorch_dump_folder_path,
				    args.push_to_hub,
				)






"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )						)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'  ,       type=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

     # positional
     parser.add_argument(
         'training_script'  ,       type=_SCREAMING_SNAKE_CASE  ,       help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )  ,       )

     # rest from the training program
     parser.add_argument('training_script_args'  ,       nargs=_SCREAMING_SNAKE_CASE						)

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

     # Import training_script as a module.
     __A					:					Dict								=		Path(args.training_script						)
     sys.path.append(str(script_fpath.parent.resolve()						)						)
     __A					:					List[Any]								=		script_fpath.stem
     __A					:					Optional[Any]								=		importlib.import_module(_SCREAMING_SNAKE_CASE						)

     # Patch sys.argv
     __A					:					Union[str, Any]								=		[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores						)]

     xmp.spawn(mod._mp_fn  ,       args=()  ,       nprocs=args.num_cores						)


if __name__ == "__main__":
       main()



