


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()
_UpperCAmelCase     :					Dict       =logging.get_logger(__name__)



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

				return orig_key



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



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

				return im



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

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

				# prepare image
				lowerCAmelCase_	: Dict      			=			prepare_img()
				lowerCAmelCase_	: Dict      			=			image_processor(images=lowerCAmelCase_	,				return_tensors='''pt'''	)
				lowerCAmelCase_	: Dict      			=			encoding['''pixel_values''']

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

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

				lowerCAmelCase_	: Optional[int]      			=			'''cuda''' if torch.cuda.is_available() else '''cpu'''
				model.to(lowerCAmelCase_	)
				# verify our conversion
				lowerCAmelCase_	: Tuple      			=			model(pixel_values.to(lowerCAmelCase_	)	)

				lowerCAmelCase_	: Tuple      			=			torch.tensor(
				    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]	)
				lowerCAmelCase_	: Optional[int]      			=			torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]	)

				if single_scale:
								lowerCAmelCase_	: str      			=			torch.tensor(
								    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]	)
								lowerCAmelCase_	: 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:
								lowerCAmelCase_	: Union[str, Any]      			=			torch.tensor(
								    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]	)
								lowerCAmelCase_	: Dict      			=			torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]	)

				if with_box_refine:
								lowerCAmelCase_	: Any      			=			torch.tensor(
								    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]	)
								lowerCAmelCase_	: Dict      			=			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:
								lowerCAmelCase_	: Tuple      			=			torch.tensor(
								    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]	)
								lowerCAmelCase_	: Any      			=			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(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:
								lowerCAmelCase_	: List[Any]      			=			'''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__":
						_UpperCAmelCase     :					Tuple       =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."""
						)
						_UpperCAmelCase     :					List[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 typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase     :					List[str]       =logging.get_logger(__name__)







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[str]   =							["""input_ids""", """attention_mask"""]




						def __init__(			self				,  __lowercase="[PAD]"				,  __lowercase="[BOS]"				,  __lowercase="[EOS]"				,  __lowercase="[MASK]"				,  __lowercase="[CLS]"				,  __lowercase="[SEP]"				,  __lowercase=2_0_4_8				,  **__lowercase				,  )   ->      None:
										lowerCAmelCase_	: Optional[int]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else pad_token
										lowerCAmelCase_	: str      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else bos_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else eos_token
										lowerCAmelCase_	: List[str]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else mask_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else cls_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else sep_token

										super().__init__(
										    pad_token=__lowercase				,  bos_token=__lowercase				,  eos_token=__lowercase				,  mask_token=__lowercase				,  cls_token=__lowercase				,  sep_token=__lowercase				,  model_max_length=__lowercase				,  **__lowercase				,  )

										lowerCAmelCase_	: Dict      			=			2**8  # utf is 8 bits

										# define special tokens dict
										lowerCAmelCase_	: Dict[str, int]      			=			{
										    self.pad_token: 0,
										    self.bos_token: 1,
										    self.eos_token: 2,
										    self.mask_token: 3,
										    self.cls_token: 4,
										    self.sep_token: 5,
										}
										lowerCAmelCase_	: List[Any]      			=			len(self.special_tokens_encoder							)
										lowerCAmelCase_	: Dict[int, str]      			=			{v: k for k, v in self.special_tokens_encoder.items()}




						def   lowercase_	(			self							)   ->      Dict[str, int]:
										lowerCAmelCase_	: int      			=			self.special_tokens_encoder.copy()
										vocab.update(self.added_tokens_encoder							)
										for i in range(self._utf_vocab_size							):
														lowerCAmelCase_	: int      			=			chr(__lowercase							)
														lowerCAmelCase_	: Optional[Any]      			=			i + len(self.special_tokens_encoder							)
										return vocab




						@property
						def   lowercase_	(			self							)   ->      str:
										return self._utf_vocab_size + self._num_special_tokens




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None				,  __lowercase = False							)   ->      List[int]:
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__lowercase				,  token_ids_a=__lowercase				,  already_has_special_tokens=__lowercase							)

										# normal case: some special tokens
										if token_ids_a is None:
														return [1] + [0] * len(__lowercase							) + [1]
										return [1] + ([0] * len(__lowercase							)) + [1] + ([0] * len(__lowercase							)) + [1]




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      List[int]:
										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




						def   lowercase_	(			self				,  __lowercase							)   ->      List[str]:
										lowerCAmelCase_	: Optional[int]      			=			[chr(__lowercase							) for i in text.encode('''utf-8'''							)]
										return tokens




						def   lowercase_	(			self				,  __lowercase							)   ->      Tuple:
										if token in self.special_tokens_encoder:
														lowerCAmelCase_	: Optional[int]      			=			self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
														lowerCAmelCase_	: Any      			=			self.added_tokens_encoder[token]
										elif len(__lowercase							) != 1:
														lowerCAmelCase_	: Any      			=			self.unk_token_id
										else:
														lowerCAmelCase_	: List[Any]      			=			ord(__lowercase							) + self._num_special_tokens
										return token_id




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										if index in self.special_tokens_decoder:
														lowerCAmelCase_	: List[Any]      			=			self.special_tokens_decoder[index]
										elif index in self.added_tokens_decoder:
														lowerCAmelCase_	: List[str]      			=			self.added_tokens_decoder[index]
										else:
														lowerCAmelCase_	: str      			=			chr(index - self._num_special_tokens							)
										return token




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										lowerCAmelCase_	: Tuple      			=			B''''''
										for token in tokens:
														if token in self.special_tokens_decoder:
																		lowerCAmelCase_	: int      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.added_tokens_decoder:
																		lowerCAmelCase_	: Tuple      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.special_tokens_encoder:
																		lowerCAmelCase_	: Any      			=			token.encode('''utf-8'''							)
														elif token in self.added_tokens_encoder:
																		lowerCAmelCase_	: Tuple      			=			token.encode('''utf-8'''							)
														else:
																		lowerCAmelCase_	: str      			=			bytes([ord(__lowercase							)]							)
														bstring += tok_string
										lowerCAmelCase_	: str      			=			bstring.decode('''utf-8'''				,  errors='''replace'''							)
										return string




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      Tuple[str]:
										return ()