




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()
SCREAMING_SNAKE_CASE:    str											=   logging.get_logger(__name__)


def 				_a       (				lowerCAmelCase   )->  int:
		if "backbone.0.body" in orig_key:
				SCREAMING_SNAKE_CASE_							=       orig_key.replace('backbone.0.body'						,	'backbone.conv_encoder.model'   )
		if "transformer" in orig_key:
				SCREAMING_SNAKE_CASE_							=       orig_key.replace('transformer.'						,	''   )
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						SCREAMING_SNAKE_CASE_							=       orig_key.replace('norm1'						,	'self_attn_layer_norm'   )
				else:
						SCREAMING_SNAKE_CASE_							=       orig_key.replace('norm1'						,	'encoder_attn_layer_norm'   )
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						SCREAMING_SNAKE_CASE_							=       orig_key.replace('norm2'						,	'final_layer_norm'   )
				else:
						SCREAMING_SNAKE_CASE_							=       orig_key.replace('norm2'						,	'self_attn_layer_norm'   )
		if "norm3" in orig_key:
				SCREAMING_SNAKE_CASE_							=       orig_key.replace('norm3'						,	'final_layer_norm'   )
		if "linear1" in orig_key:
				SCREAMING_SNAKE_CASE_							=       orig_key.replace('linear1'						,	'fc1'   )
		if "linear2" in orig_key:
				SCREAMING_SNAKE_CASE_							=       orig_key.replace('linear2'						,	'fc2'   )
		if "query_embed" in orig_key:
				SCREAMING_SNAKE_CASE_							=       orig_key.replace('query_embed'						,	'query_position_embeddings'   )
		if "cross_attn" in orig_key:
				SCREAMING_SNAKE_CASE_							=       orig_key.replace('cross_attn'						,	'encoder_attn'   )

		return orig_key


def 				_a       (				lowerCAmelCase   )->  Any:
		# transformer decoder self-attention layers
		for i in range(6   ):
				# read in weights + bias of input projection layer of self-attention
				SCREAMING_SNAKE_CASE_							=       state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
				SCREAMING_SNAKE_CASE_							=       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
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[:256, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[:256]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[256:512, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[256:512]
				SCREAMING_SNAKE_CASE_							=       in_proj_weight[-256:, :]
				SCREAMING_SNAKE_CASE_							=       in_proj_bias[-256:]


def 				_a       (				)->  List[Any]:
		SCREAMING_SNAKE_CASE_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
		SCREAMING_SNAKE_CASE_							=       Image.open(requests.get(lowerCAmelCase						,	stream=lowerCAmelCase   ).raw   )

		return im


@torch.no_grad()
def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	)->  Dict:
		SCREAMING_SNAKE_CASE_							=       DeformableDetrConfig()
		# set config attributes
		if single_scale:
				SCREAMING_SNAKE_CASE_							=       1
		SCREAMING_SNAKE_CASE_							=       dilation
		SCREAMING_SNAKE_CASE_							=       with_box_refine
		SCREAMING_SNAKE_CASE_							=       two_stage
		# set labels
		SCREAMING_SNAKE_CASE_							=       91
		SCREAMING_SNAKE_CASE_							=       'huggingface/label-files'
		SCREAMING_SNAKE_CASE_							=       'coco-detection-id2label.json'
		SCREAMING_SNAKE_CASE_							=       json.load(open(cached_download(hf_hub_url(lowerCAmelCase						,	lowerCAmelCase						,	repo_type='dataset'   )   )						,	'r'   )   )
		SCREAMING_SNAKE_CASE_							=       {int(lowerCAmelCase   ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_							=       idalabel
		SCREAMING_SNAKE_CASE_							=       {v: k for k, v in idalabel.items()}

		# load image processor
		SCREAMING_SNAKE_CASE_							=       DeformableDetrImageProcessor(format='coco_detection'   )

		# prepare image
		SCREAMING_SNAKE_CASE_							=       prepare_img()
		SCREAMING_SNAKE_CASE_							=       image_processor(images=lowerCAmelCase						,	return_tensors='pt'   )
		SCREAMING_SNAKE_CASE_							=       encoding['pixel_values']

		logger.info('Converting model...'   )

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

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

		SCREAMING_SNAKE_CASE_							=       torch.tensor(
		    [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]   )
		SCREAMING_SNAKE_CASE_							=       torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]   )

		if single_scale:
				SCREAMING_SNAKE_CASE_							=       torch.tensor(
				    [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]   )
				SCREAMING_SNAKE_CASE_							=       torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]   )

		if single_scale and dilation:
				SCREAMING_SNAKE_CASE_							=       torch.tensor(
				    [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]   )
				SCREAMING_SNAKE_CASE_							=       torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]   )

		if with_box_refine:
				SCREAMING_SNAKE_CASE_							=       torch.tensor(
				    [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]   )
				SCREAMING_SNAKE_CASE_							=       torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]   )

		if with_box_refine and two_stage:
				SCREAMING_SNAKE_CASE_							=       torch.tensor(
				    [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]   )
				SCREAMING_SNAKE_CASE_							=       torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]   )

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

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

		# Push to hub
		if push_to_hub:
				SCREAMING_SNAKE_CASE_							=       '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=lowerCAmelCase						,	organization='nielsr'						,	commit_message='Add model'   )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    int											=   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.'''
						)
						SCREAMING_SNAKE_CASE:    Union[str, Any]											=   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,
						)





from __future__ import annotations


def 				_a       (				lowerCAmelCase   )->  list[int]:
		if not list_of_ints:
				return []

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		return _msd_radix_sort(lowerCAmelCase						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  list[int]:
		if bit_position == 0 or len(lowerCAmelCase   ) in [0, 1]:
				return list_of_ints

		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       []
		# Split numbers based on bit at bit_position from the right
		for number in list_of_ints:
				if (number >> (bit_position - 1)) & 1:
						# number has a one at bit bit_position
						ones.append(lowerCAmelCase   )
				else:
						# number has a zero at bit bit_position
						zeros.append(lowerCAmelCase   )

    # recursively split both lists further
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )

		# recombine lists
		SCREAMING_SNAKE_CASE_							=       zeros
		res.extend(lowerCAmelCase   )

		return res


def 				_a       (				lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if not list_of_ints or length == 1:
				return

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	0						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

		SCREAMING_SNAKE_CASE_							=       begin_index
		SCREAMING_SNAKE_CASE_							=       end_index - 1
		while i <= j:
				SCREAMING_SNAKE_CASE_							=       False
				if not (list_of_ints[i] >> bit_position) & 1:
						# found zero at the beginning
						i += 1
						SCREAMING_SNAKE_CASE_							=       True
				if (list_of_ints[j] >> bit_position) & 1:
						# found one at the end
						j -= 1
						SCREAMING_SNAKE_CASE_							=       True

				if changed:
						continue

				SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       list_of_ints[j], list_of_ints[i]
				j -= 1
				if j != i:
						i += 1

		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()