






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     :							int	     =			logging.get_logger(__name__)

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

		return orig_key

def    lowerCAmelCase__		(			_a				:	int			):
		# transformer decoder self-attention layers
		for i in range(6			):
				# read in weights + bias of input projection layer of self-attention
				snake_case_ :							Dict         =				state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''			)
				snake_case_ :							Optional[Any]         =				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
				snake_case_ :							List[Any]         =				in_proj_weight[:2_56, :]
				snake_case_ :							Any         =				in_proj_bias[:2_56]
				snake_case_ :							Any         =				in_proj_weight[2_56:5_12, :]
				snake_case_ :							Union[str, Any]         =				in_proj_bias[2_56:5_12]
				snake_case_ :							Dict         =				in_proj_weight[-2_56:, :]
				snake_case_ :							Optional[int]         =				in_proj_bias[-2_56:]

def    lowerCAmelCase__		(			):
		snake_case_ :							Optional[int]         =				"http://images.cocodataset.org/val2017/000000039769.jpg"
		snake_case_ :							int         =				Image.open(requests.get(_a     ,					stream=_a			).raw			)

		return im

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

		# load image processor
		snake_case_ :							int         =				DeformableDetrImageProcessor(format="coco_detection"			)

		# prepare image
		snake_case_ :							Dict         =				prepare_img()
		snake_case_ :							List[str]         =				image_processor(images=_a     ,					return_tensors="pt"			)
		snake_case_ :							str         =				encoding["pixel_values"]

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

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

		snake_case_ :							Tuple         =				"cuda" if torch.cuda.is_available() else "cpu"
		model.to(_a			)
		# verify our conversion
		snake_case_ :							Any         =				model(pixel_values.to(_a			)			)

		snake_case_ :							Dict         =				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]]			)
		snake_case_ :							Union[str, Any]         =				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:
				snake_case_ :							Optional[Any]         =				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]]			)
				snake_case_ :							int         =				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:
				snake_case_ :							int         =				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]]			)
				snake_case_ :							Any         =				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:
				snake_case_ :							Union[str, Any]         =				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]]			)
				snake_case_ :							Optional[int]         =				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:
				snake_case_ :							List[Any]         =				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]]			)
				snake_case_ :							List[Any]         =				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(_a			)     ,					atol=1E-4			)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,					expected_boxes.to(_a			)     ,					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(_a			).mkdir(exist_ok=_a			)
		model.save_pretrained(_a			)
		image_processor.save_pretrained(_a			)

		# Push to hub
		if push_to_hub:
				snake_case_ :							int         =				"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=_a     ,					organization="nielsr"     ,					commit_message="Add model"			)


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








import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
							lowercase     :							List[Any]	     =			[]

							lowercase     :							Optional[int]	     =			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
							lowercase     :							Optional[Any]	     =			glob.glob(pattern)
							# TODO: deal with TF/Flax too
							lowercase     :							Tuple	     =			[
							    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
							]

							for test_file in test_files:
														lowercase     :							Union[str, Any]	     =			get_tester_classes(test_file)
														for tester_class in tester_classes:
																					# A few tester classes don't have `parent` parameter in `__init__`.
																					# TODO: deal this better
																					try:
																												lowercase     :							Union[str, Any]	     =			tester_class(parent=None)
																					except Exception:
																												continue
																					if hasattr(tester, '''get_config'''):
																												lowercase     :							Tuple	     =			tester.get_config()
																												for k, v in config.to_dict().items():
																																			if isinstance(v, int):
																																										lowercase     :							Tuple	     =			None
																																										if k in ["vocab_size"]:
																																																	lowercase     :							str	     =			1_00
																																										elif k in ["max_position_embeddings"]:
																																																	lowercase     :							List[str]	     =			1_28
																																										elif k in ["hidden_size", "d_model"]:
																																																	lowercase     :							Union[str, Any]	     =			40
																																										elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																																	lowercase     :							List[str]	     =			5
																																										if target is not None and v > target:
																																																	failures.append(
																																																	    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
																																																	    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
																																																	    F""" than {target}."""
																																																	)

							if len(failures) > 0:
														raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))
