



'''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()
__SCREAMING_SNAKE_CASE :      List[Any]             =logging.get_logger(__name__)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]				):


			'''simple docstring'''







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

			return orig_key



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      str				):


			'''simple docstring'''







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



def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      Dict														=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
			A:      Optional[int]														=   Image.open(requests.get(lowerCamelCase__							,							stream=lowerCamelCase__				).raw				)

			return im



@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Tuple							,							lowerCamelCase__   :      Optional[int]							,							lowerCamelCase__   :      Optional[int]							,							lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      Optional[int]							,							):


			'''simple docstring'''







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

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

			# prepare image
			A:      Any														=   prepare_img()
			A:      Tuple														=   image_processor(images=lowerCamelCase__							,							return_tensors="""pt"""				)
			A:      str														=   encoding["""pixel_values"""]

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

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

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

			A:      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]]				)
			A:      List[str]														=   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:
						A:      Union[str, 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]]				)
						A:      List[str]														=   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:
						A:      str														=   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]]				)
						A:      List[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:
						A:      Optional[int]														=   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]]				)
						A:      str														=   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:
						A:      Tuple														=   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]]				)
						A:      Tuple														=   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(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:
						A:      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=lowerCamelCase__							,							organization="""nielsr"""							,							commit_message="""Add model"""				)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE :      str             =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.'
					)
					__SCREAMING_SNAKE_CASE :      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,
					)










'''simple docstring'''
from ..utils import DummyObject, requires_backends


class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							Tuple:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Optional[int]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							List[Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)







class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							List[str]:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





