



'''simple docstring'''





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()
A__						:						Tuple          =      logging.get_logger(__name__)


def     UpperCamelCase(						__UpperCamelCase							:	Tuple				):
		if "backbone.0.body" in orig_key:
				lowerCAmelCase_    :  int             =				orig_key.replace('''backbone.0.body'''  ,'''backbone.conv_encoder.model'''				)
		if "transformer" in orig_key:
				lowerCAmelCase_    :  Optional[Any]             =				orig_key.replace('''transformer.'''  ,''''''				)
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						lowerCAmelCase_    :  List[str]             =				orig_key.replace('''norm1'''  ,'''self_attn_layer_norm'''				)
				else:
						lowerCAmelCase_    :  Any             =				orig_key.replace('''norm1'''  ,'''encoder_attn_layer_norm'''				)
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						lowerCAmelCase_    :  List[Any]             =				orig_key.replace('''norm2'''  ,'''final_layer_norm'''				)
				else:
						lowerCAmelCase_    :  Optional[Any]             =				orig_key.replace('''norm2'''  ,'''self_attn_layer_norm'''				)
		if "norm3" in orig_key:
				lowerCAmelCase_    :  List[str]             =				orig_key.replace('''norm3'''  ,'''final_layer_norm'''				)
		if "linear1" in orig_key:
				lowerCAmelCase_    :  List[Any]             =				orig_key.replace('''linear1'''  ,'''fc1'''				)
		if "linear2" in orig_key:
				lowerCAmelCase_    :  int             =				orig_key.replace('''linear2'''  ,'''fc2'''				)
		if "query_embed" in orig_key:
				lowerCAmelCase_    :  Optional[int]             =				orig_key.replace('''query_embed'''  ,'''query_position_embeddings'''				)
		if "cross_attn" in orig_key:
				lowerCAmelCase_    :  Union[str, Any]             =				orig_key.replace('''cross_attn'''  ,'''encoder_attn'''				)

		return orig_key


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]				):
		# transformer decoder self-attention layers
		for i in range(6				):
				# read in weights + bias of input projection layer of self-attention
				lowerCAmelCase_    :  Union[str, Any]             =				state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""				)
				lowerCAmelCase_    :  Tuple             =				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
				lowerCAmelCase_    :  int             =				in_proj_weight[:256, :]
				lowerCAmelCase_    :  Tuple             =				in_proj_bias[:256]
				lowerCAmelCase_    :  Optional[int]             =				in_proj_weight[256:512, :]
				lowerCAmelCase_    :  List[Any]             =				in_proj_bias[256:512]
				lowerCAmelCase_    :  Union[str, Any]             =				in_proj_weight[-256:, :]
				lowerCAmelCase_    :  Optional[Any]             =				in_proj_bias[-256:]


def     UpperCamelCase(						):
		lowerCAmelCase_    :  List[Any]             =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_    :  Tuple             =				Image.open(requests.get(__UpperCamelCase  ,stream=__UpperCamelCase				).raw				)

		return im


@torch.no_grad()
def     UpperCamelCase(						__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Any  ,):
		lowerCAmelCase_    :  int             =				DeformableDetrConfig()
		# set config attributes
		if single_scale:
				lowerCAmelCase_    :  Union[str, Any]             =				1
		lowerCAmelCase_    :  Dict             =				dilation
		lowerCAmelCase_    :  Optional[Any]             =				with_box_refine
		lowerCAmelCase_    :  Union[str, Any]             =				two_stage
		# set labels
		lowerCAmelCase_    :  Union[str, Any]             =				91
		lowerCAmelCase_    :  Union[str, Any]             =				'''huggingface/label-files'''
		lowerCAmelCase_    :  Tuple             =				'''coco-detection-id2label.json'''
		lowerCAmelCase_    :  Union[str, Any]             =				json.load(open(cached_download(hf_hub_url(__UpperCamelCase  ,__UpperCamelCase  ,repo_type='''dataset'''				)				)  ,'''r'''				)				)
		lowerCAmelCase_    :  Tuple             =				{int(__UpperCamelCase				): v for k, v in idalabel.items()}
		lowerCAmelCase_    :  str             =				idalabel
		lowerCAmelCase_    :  List[str]             =				{v: k for k, v in idalabel.items()}

		# load image processor
		lowerCAmelCase_    :  str             =				DeformableDetrImageProcessor(format='''coco_detection'''				)

		# prepare image
		lowerCAmelCase_    :  Optional[Any]             =				prepare_img()
		lowerCAmelCase_    :  List[Any]             =				image_processor(images=__UpperCamelCase  ,return_tensors='''pt'''				)
		lowerCAmelCase_    :  Tuple             =				encoding['''pixel_values''']

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

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

		lowerCAmelCase_    :  List[str]             =				'''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(__UpperCamelCase				)
		# verify our conversion
		lowerCAmelCase_    :  str             =				model(pixel_values.to(__UpperCamelCase				)				)

		lowerCAmelCase_    :  Dict             =				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], [-10.5634, -5.3_3_7_9, -7.5_1_1_6]]				)
		lowerCAmelCase_    :  str             =				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:
				lowerCAmelCase_    :  str             =				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], [-10.0665, -5.8_4_7_0, -7.7_0_0_3]]				)
				lowerCAmelCase_    :  Optional[int]             =				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:
				lowerCAmelCase_    :  str             =				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], [-10.1178, -4.5_2_7_5, -6.2_6_7_1]]				)
				lowerCAmelCase_    :  Optional[Any]             =				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:
				lowerCAmelCase_    :  int             =				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]]				)
				lowerCAmelCase_    :  Union[str, Any]             =				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:
				lowerCAmelCase_    :  Any             =				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]]				)
				lowerCAmelCase_    :  Any             =				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(__UpperCamelCase				)  ,atol=1e-4				)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,expected_boxes.to(__UpperCamelCase				)  ,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(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
		model.save_pretrained(__UpperCamelCase				)
		image_processor.save_pretrained(__UpperCamelCase				)

		# Push to hub
		if push_to_hub:
				lowerCAmelCase_    :  Tuple             =				'''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=__UpperCamelCase  ,organization='''nielsr'''  ,commit_message='''Add model'''				)


if __name__ == "__main__":
						A__						:						List[str]          =      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.'''
						)
						A__						:						Optional[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

A__						:						Tuple          =      '''Alexander Joslin'''


def     UpperCamelCase(						__UpperCamelCase							:	int				):

		if number <= 0:
				lowerCAmelCase_    :  str             =				f"""n must be greater than 0. Got n = {number}"""
				raise ValueError(__UpperCamelCase				)

		lowerCAmelCase_    :  str             =				[number + 1] * (number + 1)

		# starting position
		lowerCAmelCase_    :  List[Any]             =				0
		for i in range(1  ,__UpperCamelCase				):
				lowerCAmelCase_    :  int             =				min(table[i + 1]  ,table[i] + 1				)
				# check if out of bounds
				if i * 2 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 2]  ,table[i] + 1				)
				# check if out of bounds
				if i * 3 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 3]  ,table[i] + 1				)
		return table[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()





