

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	:   Optional[int]	=					logging.get_logger(__name__)




def        __UpperCamelCase     (      _A					:							List[str]		)				->Optional[Any]:



							"""simple docstring"""
							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        __UpperCamelCase     (      _A					:							Union[str, Any]		)				->Optional[Any]:



							"""simple docstring"""
							# 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        __UpperCamelCase     (      )				->str:



							"""simple docstring"""
							lowerCamelCase_								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_								=Image.open(requests.get(_A      ,  stream=_A		).raw		)

							return im




@torch.no_grad()
def        __UpperCamelCase     (      _A					:							List[str]      ,  _A					:							int      ,  _A					:							int      ,  _A					:							Tuple      ,  _A					:							int      ,  _A					:							Union[str, Any]      ,  _A					:							List[str]      ,  )				->Optional[int]:



							"""simple docstring"""
							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(_A      ,  _A      ,  repo_type="""dataset"""		)		)      ,  """r"""		)		)
							lowerCamelCase_								={int(_A		): 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=_A      ,  return_tensors="""pt"""		)
							lowerCamelCase_								=encoding["""pixel_values"""]

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

							# load original state dict
							lowerCamelCase_								=torch.load(_A      ,  map_location="""cpu"""		)["""model"""]
							# rename keys
							for key in state_dict.copy().keys():
														lowerCamelCase_								=state_dict.pop(_A		)
														lowerCamelCase_								=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
							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(_A		)
																					lowerCamelCase_								=val
    # finally, create HuggingFace model and load state dict
							lowerCamelCase_								=DeformableDetrForObjectDetection(_A		)
							model.load_state_dict(_A		)
							model.eval()

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

							lowerCamelCase_								=torch.tensor(
							    [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]		)
							lowerCamelCase_								=torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]		)

							if single_scale:
														lowerCamelCase_								=torch.tensor(
														    [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]		)
														lowerCamelCase_								=torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]		)

							if single_scale and dilation:
														lowerCamelCase_								=torch.tensor(
														    [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]		)
														lowerCamelCase_								=torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]		)

							if with_box_refine:
														lowerCamelCase_								=torch.tensor(
														    [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]		)
														lowerCamelCase_								=torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]		)

							if with_box_refine and two_stage:
														lowerCamelCase_								=torch.tensor(
														    [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]		)
														lowerCamelCase_								=torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]		)

							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:
														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=_A      ,  organization="""nielsr"""      ,  commit_message="""Add model"""		)


if __name__ == "__main__":
	__A	:   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.'
	)
	__A	:   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 typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase__    :    Tuple			=      logging.get_logger(__name__)
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					["pixel_values"]







					def __init__(							self  :      str       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      PILImageResampling = PILImageResampling.BICUBIC       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Union[int, float] = 1 / 255       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )
									UpperCAmelCase__			 =				crop_size if crop_size is not None else {'height': 256, 'width': 256}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				size if size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )

									UpperCAmelCase__			 =				do_center_crop
									UpperCAmelCase__			 =				crop_size
									UpperCAmelCase__			 =				do_resize
									UpperCAmelCase__			 =				size
									UpperCAmelCase__			 =				resample
									UpperCAmelCase__			 =				do_rescale
									UpperCAmelCase__			 =				rescale_factor
									UpperCAmelCase__			 =				do_normalize
									UpperCAmelCase__			 =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									UpperCAmelCase__			 =				image_std if image_std is not None else IMAGENET_DEFAULT_STD







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      Optional[int] = None       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      List[str]       ,):
									UpperCAmelCase__			 =				self.size if size is None else size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )

									UpperCAmelCase__ ,      UpperCAmelCase__			 =				get_image_size(lowerCamelCase__     )
									UpperCAmelCase__			 =				min(lowerCamelCase__       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				(size['height'] / crop_size['height']) * min_dim
									UpperCAmelCase__			 =				(size['width'] / crop_size['width']) * min_dim
									return center_crop(lowerCamelCase__       ,size=(cropped_height, cropped_width)       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      PILImageResampling = PIL.Image.BICUBIC       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      Any       ,):
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''     )
									return resize(
									    lowerCamelCase__       ,size=(size['height'], size['width'])       ,resample=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Union[int, float]       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      int       ,):
									return rescale(lowerCamelCase__       ,scale=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      int       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Union[float, List[float]]       ,lowerCamelCase__  :      Union[float, List[float]]       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      List[Any]       ,):
									return normalize(lowerCamelCase__       ,mean=lowerCamelCase__       ,std=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[Any]       ,lowerCamelCase__  :      ImageInput       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      PILImageResampling = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[float] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[str, TensorType]] = None       ,lowerCamelCase__  :      ChannelDimension = ChannelDimension.FIRST       ,**lowerCamelCase__  :      Optional[int]       ,):
									UpperCAmelCase__			 =				do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__			 =				crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__			 =				size if size is not None else self.size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				resample if resample is not None else self.resample
									UpperCAmelCase__			 =				do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__			 =				rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__			 =				do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__			 =				image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__			 =				image_std if image_std is not None else self.image_std

									UpperCAmelCase__			 =				make_list_of_images(lowerCamelCase__     )

									if not valid_images(lowerCamelCase__     ):
													raise ValueError(
													    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
													    'torch.Tensor, tf.Tensor or jax.ndarray.'     )

									if do_center_crop and crop_size is None:
													raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'     )

									if do_resize and size is None:
													raise ValueError('Size must be specified if do_resize is True.'     )

									if do_rescale and rescale_factor is None:
													raise ValueError('Rescale factor must be specified if do_rescale is True.'     )

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'     )

									# All transformations expect numpy arrays.
									UpperCAmelCase__			 =				[to_numpy_array(lowerCamelCase__     ) for image in images]

									if do_center_crop:
													UpperCAmelCase__			 =				[self.center_crop(lowerCamelCase__       ,lowerCamelCase__       ,size=lowerCamelCase__     ) for image in images]

									if do_resize:
													UpperCAmelCase__			 =				[self.resize(image=lowerCamelCase__       ,size=lowerCamelCase__       ,resample=lowerCamelCase__     ) for image in images]

									if do_rescale:
													UpperCAmelCase__			 =				[self.rescale(image=lowerCamelCase__       ,scale=lowerCamelCase__     ) for image in images]

									if do_normalize:
													UpperCAmelCase__			 =				[self.normalize(image=lowerCamelCase__       ,mean=lowerCamelCase__       ,std=lowerCamelCase__     ) for image in images]

									UpperCAmelCase__			 =				[to_channel_dimension_format(lowerCamelCase__       ,lowerCamelCase__     ) for image in images]

									UpperCAmelCase__			 =				{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__       ,tensor_type=lowerCamelCase__     )



