
'''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()
_lowercase					:    Any       				=			logging.get_logger(__name__)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					str				):



						"""simple docstring"""





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

						return orig_key
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





						for i in range(6				):
												# read in weights + bias of input projection layer of self-attention
												lowercase_      :	Dict						=			state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''				)
												lowercase_      :	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
												lowercase_      :	Optional[int]						=			in_proj_weight[:256, :]
												lowercase_      :	Union[str, Any]						=			in_proj_bias[:256]
												lowercase_      :	List[Any]						=			in_proj_weight[256:512, :]
												lowercase_      :	int						=			in_proj_bias[256:512]
												lowercase_      :	Dict						=			in_proj_weight[-256:, :]
												lowercase_      :	List[str]						=			in_proj_bias[-256:]
def    snake_case_						(							):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase_      :	Optional[int]						=			Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE				).raw				)

						return im
@torch.no_grad()
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					str , __SCREAMING_SNAKE_CASE   :					Optional[int] , ):



						"""simple docstring"""





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

						# load image processor
						lowercase_      :	int						=			DeformableDetrImageProcessor(format='''coco_detection'''				)

						# prepare image
						lowercase_      :	List[str]						=			prepare_img()
						lowercase_      :	str						=			image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt'''				)
						lowercase_      :	List[str]						=			encoding['''pixel_values''']

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

						# load original state dict
						lowercase_      :	Tuple						=			torch.load(__SCREAMING_SNAKE_CASE , map_location='''cpu'''				)['''model''']
						# rename keys
						for key in state_dict.copy().keys():
												lowercase_      :	List[str]						=			state_dict.pop(__SCREAMING_SNAKE_CASE				)
												lowercase_      :	Dict						=			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
						lowercase_      :	Optional[int]						=			'''model.'''
						for key in state_dict.copy().keys():
												if not key.startswith('''class_embed'''				) and not key.startswith('''bbox_embed'''				):
																		lowercase_      :	Union[str, Any]						=			state_dict.pop(__SCREAMING_SNAKE_CASE				)
																		lowercase_      :	Union[str, Any]						=			val
    # finally, create HuggingFace model and load state dict
						lowercase_      :	Optional[int]						=			DeformableDetrForObjectDetection(__SCREAMING_SNAKE_CASE				)
						model.load_state_dict(__SCREAMING_SNAKE_CASE				)
						model.eval()

						lowercase_      :	str						=			'''cuda''' if torch.cuda.is_available() else '''cpu'''
						model.to(__SCREAMING_SNAKE_CASE				)
						# verify our conversion
						lowercase_      :	Any						=			model(pixel_values.to(__SCREAMING_SNAKE_CASE				)				)

						lowercase_      :	Any						=			torch.tensor(
						    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]				)
						lowercase_      :	Union[str, Any]						=			torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]				)

						if single_scale:
												lowercase_      :	int						=			torch.tensor(
												    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]				)
												lowercase_      :	Optional[Any]						=			torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]				)

						if single_scale and dilation:
												lowercase_      :	Union[str, Any]						=			torch.tensor(
												    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]				)
												lowercase_      :	List[Any]						=			torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]				)

						if with_box_refine:
												lowercase_      :	str						=			torch.tensor(
												    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]				)
												lowercase_      :	Union[str, Any]						=			torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]				)

						if with_box_refine and two_stage:
												lowercase_      :	List[Any]						=			torch.tensor(
												    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]				)
												lowercase_      :	Tuple						=			torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]				)

						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:
												lowercase_      :	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=__SCREAMING_SNAKE_CASE , organization='''nielsr''' , commit_message='''Add model'''				)


if __name__ == "__main__":
							_lowercase					:    Any       				=			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."
							)
							_lowercase					:    Tuple       				=			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'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	['''image_processor''', '''tokenizer''']
					lowerCAmelCase_	  =	'''EfficientNetImageProcessor'''
					lowerCAmelCase_	  =	('''BertTokenizer''', '''BertTokenizerFast''')






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											super().__init__(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					def __call__(  self							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE="max_length"							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=None							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''						)

											if text is not None:
																	lowercase_      :	str						=			self.tokenizer(
																	    __SCREAMING_SNAKE_CASE							,				padding=__SCREAMING_SNAKE_CASE							,				max_length=__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if images is not None:
																	lowercase_      :	int						=			self.image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if text is not None and images is not None:
																	lowercase_      :	Any						=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE						)							,				tensor_type=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Dict						=			self.tokenizer.model_input_names
											lowercase_      :	Optional[int]						=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)



