





'''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()
snake_case_       :    str          =					logging.get_logger(__name__)





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

						return orig_key





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





def     lowerCamelCase_ (					)	->		int:
						UpperCAmelCase_     : Any         =					"http://images.cocodataset.org/val2017/000000039769.jpg"
						UpperCAmelCase_     : Union[str, Any]         =					Image.open(requests.get(_SCREAMING_SNAKE_CASE,     stream=_SCREAMING_SNAKE_CASE				).raw				)

						return im





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

						# load image processor
						UpperCAmelCase_     : Union[str, Any]         =					DeformableDetrImageProcessor(format='''coco_detection'''				)

						# prepare image
						UpperCAmelCase_     : Optional[int]         =					prepare_img()
						UpperCAmelCase_     : Dict         =					image_processor(images=_SCREAMING_SNAKE_CASE,     return_tensors='''pt'''				)
						UpperCAmelCase_     : List[str]         =					encoding["pixel_values"]

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

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

						UpperCAmelCase_     : Optional[Any]         =					"cuda" if torch.cuda.is_available() else "cpu"
						model.to(_SCREAMING_SNAKE_CASE				)
						# verify our conversion
						UpperCAmelCase_     : int         =					model(pixel_values.to(_SCREAMING_SNAKE_CASE				)				)

						UpperCAmelCase_     : Optional[int]         =					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]]				)
						UpperCAmelCase_     : Tuple         =					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:
												UpperCAmelCase_     : Any         =					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]]				)
												UpperCAmelCase_     : Union[str, Any]         =					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:
												UpperCAmelCase_     : int         =					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]]				)
												UpperCAmelCase_     : Union[str, Any]         =					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:
												UpperCAmelCase_     : List[str]         =					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]]				)
												UpperCAmelCase_     : int         =					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:
												UpperCAmelCase_     : Optional[int]         =					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]]				)
												UpperCAmelCase_     : List[Any]         =					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(_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:
												UpperCAmelCase_     : 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__":
					snake_case_       :    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."
					)
					snake_case_       :    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,
					)








'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class    __a (unittest.TestCase       ):

							def __init__(							self							:       Dict    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       str=7    ,       __magic_name__							:       Optional[int]=3    ,       __magic_name__							:       Union[str, Any]=18    ,       __magic_name__							:       Dict=30    ,       __magic_name__							:       List[Any]=4_00    ,       __magic_name__							:       Dict=True    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       Optional[Any]=0.8_7_5    ,       __magic_name__							:       List[Any]=True    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					size if size is not None else {'''shortest_edge''': 20}
													UpperCAmelCase_     : Optional[Any]         =					parent
													UpperCAmelCase_     : Any         =					batch_size
													UpperCAmelCase_     : Optional[Any]         =					num_channels
													UpperCAmelCase_     : Optional[Any]         =					image_size
													UpperCAmelCase_     : Optional[int]         =					min_resolution
													UpperCAmelCase_     : Optional[Any]         =					max_resolution
													UpperCAmelCase_     : Optional[int]         =					do_resize
													UpperCAmelCase_     : Optional[Any]         =					size
													UpperCAmelCase_     : int         =					crop_pct
													UpperCAmelCase_     : Optional[int]         =					do_normalize
													UpperCAmelCase_     : Dict         =					image_mean
													UpperCAmelCase_     : int         =					image_std



							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    int:







													"""simple docstring"""
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "crop_pct": self.crop_pct,
													}





@require_torch
@require_vision
class    __a (lowerCamelCase    ,						unittest.TestCase       ):
							__a   :				Dict       =    ConvNextImageProcessor if is_vision_available() else None

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					ConvNextImageProcessingTester(self				)

							@property
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Any:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_resize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''size'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''crop_pct'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_normalize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_mean'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_std'''				)				)

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 20}				)

													UpperCAmelCase_     : List[Any]         =					self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 42}				)

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													pass

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : int         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       Image.Image				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Tuple         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Optional[Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													UpperCAmelCase_     : Tuple         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       numpify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       np.ndarray				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Union[str, Any]         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )



							def       UpperCAmelCase__		(							self							:       str				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       torchify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       torch.Tensor				)

													# Test not batched input
													UpperCAmelCase_     : List[Any]         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : str         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )





