





'''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					: Any      =    logging.get_logger(__name__)





def    _lowerCAmelCase						(						lowercase	)					->					Optional[int]:
			if "backbone.0.body" in orig_key:
						__lowerCAmelCase											=				orig_key.replace("""backbone.0.body"""					,	"""backbone.conv_encoder.model"""	)
			if "transformer" in orig_key:
						__lowerCAmelCase											=				orig_key.replace("""transformer."""					,	""""""	)
			if "norm1" in orig_key:
						if "encoder" in orig_key:
									__lowerCAmelCase											=				orig_key.replace("""norm1"""					,	"""self_attn_layer_norm"""	)
						else:
									__lowerCAmelCase											=				orig_key.replace("""norm1"""					,	"""encoder_attn_layer_norm"""	)
			if "norm2" in orig_key:
						if "encoder" in orig_key:
									__lowerCAmelCase											=				orig_key.replace("""norm2"""					,	"""final_layer_norm"""	)
						else:
									__lowerCAmelCase											=				orig_key.replace("""norm2"""					,	"""self_attn_layer_norm"""	)
			if "norm3" in orig_key:
						__lowerCAmelCase											=				orig_key.replace("""norm3"""					,	"""final_layer_norm"""	)
			if "linear1" in orig_key:
						__lowerCAmelCase											=				orig_key.replace("""linear1"""					,	"""fc1"""	)
			if "linear2" in orig_key:
						__lowerCAmelCase											=				orig_key.replace("""linear2"""					,	"""fc2"""	)
			if "query_embed" in orig_key:
						__lowerCAmelCase											=				orig_key.replace("""query_embed"""					,	"""query_position_embeddings"""	)
			if "cross_attn" in orig_key:
						__lowerCAmelCase											=				orig_key.replace("""cross_attn"""					,	"""encoder_attn"""	)

			return orig_key





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





def    _lowerCAmelCase						(						)					->					Tuple:
			__lowerCAmelCase											=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			__lowerCAmelCase											=				Image.open(requests.get(lowercase					,	stream=lowercase	).raw	)

			return im





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

			# load image processor
			__lowerCAmelCase											=				DeformableDetrImageProcessor(format="""coco_detection"""	)

			# prepare image
			__lowerCAmelCase											=				prepare_img()
			__lowerCAmelCase											=				image_processor(images=lowercase					,	return_tensors="""pt"""	)
			__lowerCAmelCase											=				encoding["""pixel_values"""]

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

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

			__lowerCAmelCase											=				"""cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(lowercase	)
			# verify our conversion
			__lowerCAmelCase											=				model(pixel_values.to(lowercase	)	)

			__lowerCAmelCase											=				torch.tensor(
			    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]	)
			__lowerCAmelCase											=				torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]	)

			if single_scale:
						__lowerCAmelCase											=				torch.tensor(
						    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]	)
						__lowerCAmelCase											=				torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]	)

			if single_scale and dilation:
						__lowerCAmelCase											=				torch.tensor(
						    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]	)
						__lowerCAmelCase											=				torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]	)

			if with_box_refine:
						__lowerCAmelCase											=				torch.tensor(
						    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]	)
						__lowerCAmelCase											=				torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]	)

			if with_box_refine and two_stage:
						__lowerCAmelCase											=				torch.tensor(
						    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]	)
						__lowerCAmelCase											=				torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]	)

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

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

			# Push to hub
			if push_to_hub:
						__lowerCAmelCase											=				"""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=lowercase					,	organization="""nielsr"""					,	commit_message="""Add model"""	)


if __name__ == "__main__":
	_a					: Optional[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."""
	)
	_a					: Dict      =    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'''







import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


_a					: Dict      =    (3_0, 4_8_7)
_a					: int      =    (3_6, 1_0_2_4)
_a					: str      =    (1_0_0, 1_0_0)
_a					: Tuple      =    1_0_0

_a					: List[str]      =    datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

_a    ,_a					: Optional[int]      =    os.path.split(__file__)
_a					: List[Any]      =    os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					Optional[Any]:
			with ArrowWriter(features=lowercase					,	path=os.path.join(lowercase					,	"""beta.arrow"""	)	) as writer:
						for key, record in dummy_data:
									__lowerCAmelCase											=				my_features.encode_example(lowercase	)
									writer.write(lowercase	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				writer.finalize()





@get_duration
def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					List[str]:
			__lowerCAmelCase											=				datasets.Dataset.from_file(
			    filename=os.path.join(lowercase					,	"""beta.arrow"""	)					,	info=datasets.DatasetInfo(features=lowercase	)	)
			for _ in dataset:
						pass





@get_duration
def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					Any:
			__lowerCAmelCase											=				datasets.Dataset.from_file(
			    filename=os.path.join(lowercase					,	"""beta.arrow"""	)					,	info=datasets.DatasetInfo(features=lowercase	)	)
			dataset.set_format("""numpy"""	)
			for _ in dataset:
						pass





@get_duration
def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					List[str]:
			__lowerCAmelCase											=				10
			__lowerCAmelCase											=				datasets.Dataset.from_file(
			    filename=os.path.join(lowercase					,	"""beta.arrow"""	)					,	info=datasets.DatasetInfo(features=lowercase	)	)
			for i in range(0					,	len(lowercase	)					,	lowercase	):
						__lowerCAmelCase											=				dataset[i : i + batch_size]





@get_duration
def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					List[str]:
			__lowerCAmelCase											=				10
			__lowerCAmelCase											=				datasets.Dataset.from_file(
			    filename=os.path.join(lowercase					,	"""beta.arrow"""	)					,	info=datasets.DatasetInfo(features=lowercase	)	)
			dataset.set_format("""numpy"""	)
			for i in range(0					,	len(lowercase	)					,	lowercase	):
						__lowerCAmelCase											=				dataset[i : i + batch_size]





@get_duration
def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					Union[str, Any]:
			__lowerCAmelCase											=				datasets.Dataset.from_file(
			    filename=os.path.join(lowercase					,	"""beta.arrow"""	)					,	info=datasets.DatasetInfo(features=lowercase	)	)
			for col in feats:
						__lowerCAmelCase											=				dataset[col]





@get_duration
def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					Optional[int]:
			__lowerCAmelCase											=				datasets.Dataset.from_file(
			    filename=os.path.join(lowercase					,	"""beta.arrow"""	)					,	info=datasets.DatasetInfo(features=lowercase	)	)
			dataset.set_format("""numpy"""	)
			for col in feats:
						__lowerCAmelCase											=				dataset[col]





def    _lowerCAmelCase						(						)					->					Optional[Any]:
			__lowerCAmelCase											=				{}
			__lowerCAmelCase											=				(
			    read_unformated,
			    read_formatted_as_numpy,
			    read_batch_unformated,
			    read_batch_formatted_as_numpy,
			    read_col_unformated,
			    read_col_formatted_as_numpy,
			)
			with tempfile.TemporaryDirectory() as tmp_dir:
						__lowerCAmelCase											=				datasets.Features({"""image""": ArrayaD(lowercase					,	dtype="""float32"""	)}	)
						__lowerCAmelCase											=				generate_examples(features=lowercase					,	num_examples=lowercase	)
						__lowerCAmelCase											=				write(lowercase					,	lowercase					,	lowercase	)
						for read_func in read_functions:
									__lowerCAmelCase											=				read_func(lowercase					,	lowercase	)

			with tempfile.TemporaryDirectory() as tmp_dir:
						# don't use fixed length for fair comparison
						# feats = datasets.Features(
						#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
						# )
						__lowerCAmelCase											=				datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""	)	)	)}	)
						__lowerCAmelCase											=				generate_examples(
						    features=lowercase					,	num_examples=lowercase					,	seq_shapes={"""image""": SPEED_TEST_SHAPE}	)
						__lowerCAmelCase											=				write(lowercase					,	lowercase					,	lowercase	)
						for read_func in read_functions:
									__lowerCAmelCase											=				read_func(lowercase					,	lowercase	)

			with tempfile.TemporaryDirectory() as tmp_dir:
						# don't use fixed length for fair comparison
						# feats = datasets.Features(
						#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
						# )
						__lowerCAmelCase											=				datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""	)	)}	)
						__lowerCAmelCase											=				generate_examples(
						    features=lowercase					,	num_examples=lowercase					,	seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}					,	)
						__lowerCAmelCase											=				write(lowercase					,	lowercase					,	lowercase	)
						for read_func in read_functions:
									__lowerCAmelCase											=				read_func(lowercase					,	lowercase	)

			with open(lowercase					,	"""wb"""	) as f:
						f.write(json.dumps(lowercase	).encode("""utf-8"""	)	)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_array_xd()

