

"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


a__			:						Any							=			logging.get_logger(__name__)


if is_vision_available():
							import PIL


class       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  ['pixel_values']







						def __init__(self  ,				UpperCAmelCase_ = True  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = PILImageResampling.BILINEAR  ,				UpperCAmelCase_ = True  ,				UpperCAmelCase_ = False  ,				UpperCAmelCase_ = True  ,				UpperCAmelCase_ = True  ,				UpperCAmelCase_ = 1 / 2_5_5  ,				UpperCAmelCase_ = True  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				**UpperCAmelCase_  ,				)      ->    None:
											super().__init__(**UpperCAmelCase_				)

											__lowercase        =      size if size is not None else {'''height''': 2_5_6_0, '''width''': 1_9_2_0}
											if isinstance(UpperCAmelCase_  ,				(tuple, list)				):
																# The previous feature extractor size parameter was in (width, height) format
																__lowercase        =      size[::-1]
											__lowercase        =      get_size_dict(UpperCAmelCase_				)

											__lowercase        =      do_resize
											__lowercase        =      size
											__lowercase        =      resample
											__lowercase        =      do_thumbnail
											__lowercase        =      do_align_long_axis
											__lowercase        =      do_pad
											__lowercase        =      do_rescale
											__lowercase        =      rescale_factor
											__lowercase        =      do_normalize
											__lowercase        =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowercase        =      image_std if image_std is not None else IMAGENET_STANDARD_STD







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = None				)      ->    np.ndarray:
											__lowercase    ,			__lowercase        =      get_image_size(UpperCAmelCase_				)
											__lowercase    ,			__lowercase        =      size['''height'''], size['''width''']

											if (output_width < output_height and input_width > input_height) or (
											    output_width > output_height and input_width < input_height
											):
																__lowercase        =      np.rotaa(UpperCAmelCase_  ,				3				)

											if data_format is not None:
																__lowercase        =      to_channel_dimension_format(UpperCAmelCase_  ,				UpperCAmelCase_				)

											return image







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = False  ,				UpperCAmelCase_ = None  ,				)      ->    np.ndarray:
											__lowercase    ,			__lowercase        =      size['''height'''], size['''width''']
											__lowercase    ,			__lowercase        =      get_image_size(UpperCAmelCase_				)

											__lowercase        =      output_width - input_width
											__lowercase        =      output_height - input_height

											if random_padding:
																__lowercase        =      np.random.randint(low=0  ,				high=delta_height + 1				)
																__lowercase        =      np.random.randint(low=0  ,				high=delta_width + 1				)
											else:
																__lowercase        =      delta_height // 2
																__lowercase        =      delta_width // 2

											__lowercase        =      delta_height - pad_top
											__lowercase        =      delta_width - pad_left

											__lowercase        =      ((pad_top, pad_bottom), (pad_left, pad_right))
											return pad(UpperCAmelCase_  ,				UpperCAmelCase_  ,				data_format=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				*UpperCAmelCase_  ,				**UpperCAmelCase_				)      ->    int:
											logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''				)
											return self.pad_image(*UpperCAmelCase_  ,				**UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = PILImageResampling.BICUBIC  ,				UpperCAmelCase_ = None  ,				**UpperCAmelCase_  ,				)      ->    np.ndarray:
											__lowercase    ,			__lowercase        =      get_image_size(UpperCAmelCase_				)
											__lowercase    ,			__lowercase        =      size['''height'''], size['''width''']

											# We always resize to the smallest of either the input or output size.
											__lowercase        =      min(UpperCAmelCase_  ,				UpperCAmelCase_				)
											__lowercase        =      min(UpperCAmelCase_  ,				UpperCAmelCase_				)

											if height == input_height and width == input_width:
																return image

											if input_height > input_width:
																__lowercase        =      int(input_width * height / input_height				)
											elif input_width > input_height:
																__lowercase        =      int(input_height * width / input_width				)

											return resize(
											    UpperCAmelCase_  ,				size=(height, width)  ,				resample=UpperCAmelCase_  ,				reducing_gap=2.0  ,				data_format=UpperCAmelCase_  ,				**UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = PILImageResampling.BICUBIC  ,				UpperCAmelCase_ = None  ,				**UpperCAmelCase_  ,				)      ->    np.ndarray:
											__lowercase        =      get_size_dict(UpperCAmelCase_				)
											__lowercase        =      min(size['''height''']  ,				size['''width''']				)
											__lowercase        =      get_resize_output_image_size(UpperCAmelCase_  ,				size=UpperCAmelCase_  ,				default_to_square=UpperCAmelCase_				)
											__lowercase        =      resize(UpperCAmelCase_  ,				size=UpperCAmelCase_  ,				resample=UpperCAmelCase_  ,				data_format=UpperCAmelCase_  ,				**UpperCAmelCase_				)
											return resized_image







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = None  ,				**UpperCAmelCase_  ,				)      ->    Optional[int]:
											return rescale(UpperCAmelCase_  ,				scale=UpperCAmelCase_  ,				data_format=UpperCAmelCase_  ,				**UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = None  ,				**UpperCAmelCase_  ,				)      ->    np.ndarray:
											return normalize(UpperCAmelCase_  ,				mean=UpperCAmelCase_  ,				std=UpperCAmelCase_  ,				data_format=UpperCAmelCase_  ,				**UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = False  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = ChannelDimension.FIRST  ,				**UpperCAmelCase_  ,				)      ->    PIL.Image.Image:
											__lowercase        =      do_resize if do_resize is not None else self.do_resize
											__lowercase        =      size if size is not None else self.size
											if isinstance(UpperCAmelCase_  ,				(tuple, list)				):
																# Previous feature extractor had size in (width, height) format
																__lowercase        =      size[::-1]
											__lowercase        =      get_size_dict(UpperCAmelCase_				)
											__lowercase        =      resample if resample is not None else self.resample
											__lowercase        =      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
											__lowercase        =      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
											__lowercase        =      do_pad if do_pad is not None else self.do_pad
											__lowercase        =      do_rescale if do_rescale is not None else self.do_rescale
											__lowercase        =      rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowercase        =      do_normalize if do_normalize is not None else self.do_normalize
											__lowercase        =      image_mean if image_mean is not None else self.image_mean
											__lowercase        =      image_std if image_std is not None else self.image_std

											__lowercase        =      make_list_of_images(UpperCAmelCase_				)

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

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

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

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

											if do_align_long_axis:
																__lowercase        =      [self.align_long_axis(UpperCAmelCase_  ,				size=UpperCAmelCase_				) for image in images]

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

											if do_thumbnail:
																__lowercase        =      [self.thumbnail(image=UpperCAmelCase_  ,				size=UpperCAmelCase_				) for image in images]

											if do_pad:
																__lowercase        =      [self.pad_image(image=UpperCAmelCase_  ,				size=UpperCAmelCase_  ,				random_padding=UpperCAmelCase_				) for image in images]

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

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

											__lowercase        =      [to_channel_dimension_format(UpperCAmelCase_  ,				UpperCAmelCase_				) for image in images]

											__lowercase        =      {'''pixel_values''': images}
											return BatchFeature(data=UpperCAmelCase_  ,				tensor_type=UpperCAmelCase_				)





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





def 					__UpperCamelCase (     a__:			str      )					->      List[str]:




					"""simple docstring"""



					if "backbone.0.body" in orig_key:
										__lowercase        =      orig_key.replace('''backbone.0.body'''			,						'''backbone.conv_encoder.model'''      )
					if "transformer" in orig_key:
										__lowercase        =      orig_key.replace('''transformer.'''			,						''''''      )
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															__lowercase        =      orig_key.replace('''norm1'''			,						'''self_attn_layer_norm'''      )
										else:
															__lowercase        =      orig_key.replace('''norm1'''			,						'''encoder_attn_layer_norm'''      )
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															__lowercase        =      orig_key.replace('''norm2'''			,						'''final_layer_norm'''      )
										else:
															__lowercase        =      orig_key.replace('''norm2'''			,						'''self_attn_layer_norm'''      )
					if "norm3" in orig_key:
										__lowercase        =      orig_key.replace('''norm3'''			,						'''final_layer_norm'''      )
					if "linear1" in orig_key:
										__lowercase        =      orig_key.replace('''linear1'''			,						'''fc1'''      )
					if "linear2" in orig_key:
										__lowercase        =      orig_key.replace('''linear2'''			,						'''fc2'''      )
					if "query_embed" in orig_key:
										__lowercase        =      orig_key.replace('''query_embed'''			,						'''query_position_embeddings'''      )
					if "cross_attn" in orig_key:
										__lowercase        =      orig_key.replace('''cross_attn'''			,						'''encoder_attn'''      )

					return orig_key





def 					__UpperCamelCase (     a__:			Union[str, Any]      )					->      Tuple:




					"""simple docstring"""



					for i in range(6      ):
										# read in weights + bias of input projection layer of self-attention
										__lowercase        =      state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
										__lowercase        =      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
										__lowercase        =      in_proj_weight[:2_56, :]
										__lowercase        =      in_proj_bias[:2_56]
										__lowercase        =      in_proj_weight[2_56:5_12, :]
										__lowercase        =      in_proj_bias[2_56:5_12]
										__lowercase        =      in_proj_weight[-2_56:, :]
										__lowercase        =      in_proj_bias[-2_56:]





def 					__UpperCamelCase (     )					->      str:




					"""simple docstring"""



					__lowercase        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__lowercase        =      Image.open(requests.get(a__			,						stream=a__      ).raw      )

					return im





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




					"""simple docstring"""



					__lowercase        =      DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__lowercase        =      1
					__lowercase        =      dilation
					__lowercase        =      with_box_refine
					__lowercase        =      two_stage
					# set labels
					__lowercase        =      91
					__lowercase        =      '''huggingface/label-files'''
					__lowercase        =      '''coco-detection-id2label.json'''
					__lowercase        =      json.load(open(cached_download(hf_hub_url(a__			,						a__			,						repo_type='''dataset'''      )      )			,						'''r'''      )      )
					__lowercase        =      {int(a__      ): v for k, v in idalabel.items()}
					__lowercase        =      idalabel
					__lowercase        =      {v: k for k, v in idalabel.items()}

					# load image processor
					__lowercase        =      DeformableDetrImageProcessor(format='''coco_detection'''      )

					# prepare image
					__lowercase        =      prepare_img()
					__lowercase        =      image_processor(images=a__			,						return_tensors='''pt'''      )
					__lowercase        =      encoding['''pixel_values''']

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

					# load original state dict
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )['''model''']
					# rename keys
					for key in state_dict.copy().keys():
										__lowercase        =      state_dict.pop(a__      )
										__lowercase        =      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
					__lowercase        =      '''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_embed'''      ) and not key.startswith('''bbox_embed'''      ):
															__lowercase        =      state_dict.pop(a__      )
															__lowercase        =      val
    # finally, create HuggingFace model and load state dict
					__lowercase        =      DeformableDetrForObjectDetection(a__      )
					model.load_state_dict(a__      )
					model.eval()

					__lowercase        =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(a__      )
					# verify our conversion
					__lowercase        =      model(pixel_values.to(a__      )      )

					__lowercase        =      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]]      )
					__lowercase        =      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:
										__lowercase        =      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]]      )
										__lowercase        =      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:
										__lowercase        =      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]]      )
										__lowercase        =      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:
										__lowercase        =      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]]      )
										__lowercase        =      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:
										__lowercase        =      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]]      )
										__lowercase        =      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(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:
										__lowercase        =      '''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__			:						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."""
							)
							a__			:						Union[str, 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,
							)


