

"""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_     				=   logging.get_logger(__name__)
def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Optional[Any]:
   if "backbone.0.body" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("backbone.0.body"    ,  "backbone.conv_encoder.model"				)
   if "transformer" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("transformer."    ,  ""				)
   if "norm1" in orig_key:
      if "encoder" in orig_key:
         _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "self_attn_layer_norm"				)
      else:
         _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "encoder_attn_layer_norm"				)
   if "norm2" in orig_key:
      if "encoder" in orig_key:
         _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "final_layer_norm"				)
      else:
         _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "self_attn_layer_norm"				)
   if "norm3" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm3"    ,  "final_layer_norm"				)
   if "linear1" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("linear1"    ,  "fc1"				)
   if "linear2" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("linear2"    ,  "fc2"				)
   if "query_embed" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("query_embed"    ,  "query_position_embeddings"				)
   if "cross_attn" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("cross_attn"    ,  "encoder_attn"				)

   return orig_key
def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Optional[Any]:
   # transformer decoder self-attention layers
   for i in range(6				):
      # read in weights + bias of input projection layer of self-attention
      _UpperCAmelCase   	=					state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''				)
      _UpperCAmelCase   	=					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   	=					in_proj_weight[:2_56, :]
      _UpperCAmelCase   	=					in_proj_bias[:2_56]
      _UpperCAmelCase   	=					in_proj_weight[2_56:5_12, :]
      _UpperCAmelCase   	=					in_proj_bias[2_56:5_12]
      _UpperCAmelCase   	=					in_proj_weight[-2_56:, :]
      _UpperCAmelCase   	=					in_proj_bias[-2_56:]
def 			__UpperCAmelCase		(		)     ->       Any:
   _UpperCAmelCase   	=					"http://images.cocodataset.org/val2017/000000039769.jpg"
   _UpperCAmelCase   	=					Image.open(requests.get(a_    ,  stream=a_				).raw				)

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

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

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

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

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

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

   _UpperCAmelCase   	=					torch.tensor(
       [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]				)
   _UpperCAmelCase   	=					torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]				)

   if single_scale:
      _UpperCAmelCase   	=					torch.tensor(
          [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]				)
      _UpperCAmelCase   	=					torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]				)

   if single_scale and dilation:
      _UpperCAmelCase   	=					torch.tensor(
          [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]				)
      _UpperCAmelCase   	=					torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]				)

   if with_box_refine:
      _UpperCAmelCase   	=					torch.tensor(
          [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]				)
      _UpperCAmelCase   	=					torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]				)

   if with_box_refine and two_stage:
      _UpperCAmelCase   	=					torch.tensor(
          [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]				)
      _UpperCAmelCase   	=					torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]				)

   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:
      _UpperCAmelCase   	=					"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__":
  SCREAMING_SNAKE_CASE_     				=   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_     				=   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 numpy as np






class     __a			:







       def __init__(					self							,lowerCamelCase_=5							,lowerCamelCase_=5		):



          '''simple docstring'''

          _UpperCAmelCase   	=					depth
          _UpperCAmelCase   	=					0
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					min_leaf_size
          _UpperCAmelCase   	=					None







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          if labels.ndim != 1:
             print("Error: Input labels must be one dimensional"		)

          return np.mean((labels - prediction) ** 2		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          if x.ndim != 1:
             print("Error: Input data set must be one dimensional"		)
             return
          if len(lowerCamelCase_		) != len(lowerCamelCase_		):
             print("Error: X and y have different lengths"		)
             return
          if y.ndim != 1:
             print("Error: Data set labels must be one dimensional"		)
             return

          if len(lowerCamelCase_		) < 2 * self.min_leaf_size:
             _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
             return

          if self.depth == 1:
             _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
             return

          _UpperCAmelCase   	=					0
          _UpperCAmelCase   	=					self.mean_squared_error(lowerCamelCase_							,np.mean(lowerCamelCase_		)		) * 2
          for i in range(len(lowerCamelCase_		)		):
             if len(x[:i]		) < self.min_leaf_size:
                continue
             elif len(x[i:]		) < self.min_leaf_size:
                continue
             else:
                _UpperCAmelCase   	=					self.mean_squared_error(x[:i]							,np.mean(y[:i]		)		)
                _UpperCAmelCase   	=					self.mean_squared_error(x[i:]							,np.mean(y[i:]		)		)
                _UpperCAmelCase   	=					error_left + error_right
                if error < min_error:
                   _UpperCAmelCase   	=					i
                   _UpperCAmelCase   	=					error

          if best_split != 0:
             _UpperCAmelCase   	=					x[:best_split]
             _UpperCAmelCase   	=					y[:best_split]
             _UpperCAmelCase   	=					x[best_split:]
             _UpperCAmelCase   	=					y[best_split:]

             _UpperCAmelCase   	=					x[best_split]
             _UpperCAmelCase   	=					DecisionTree(
                 depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
             _UpperCAmelCase   	=					DecisionTree(
                 depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
             self.left.train(lowerCamelCase_							,lowerCamelCase_		)
             self.right.train(lowerCamelCase_							,lowerCamelCase_		)
          else:
             _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)

          return







       def    _snake_case			(					self							,lowerCamelCase_		):



          '''simple docstring'''

          if self.prediction is not None:
             return self.prediction
          elif self.left or self.right is not None:
             if x >= self.decision_boundary:
                return self.right.predict(lowerCamelCase_		)
             else:
                return self.left.predict(lowerCamelCase_		)
          else:
             print("Error: Decision tree not yet trained"		)
             return None






class     __a			:







       @staticmethod
       def    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					float(0		)
          for label in labels:
             squared_error_sum += (label - prediction) ** 2

          return float(squared_error_sum / labels.size		)



def 			__UpperCAmelCase		(		)     ->       Dict:
   _UpperCAmelCase   	=					np.arange(-1.0    ,  1.0    ,  0.005				)
   _UpperCAmelCase   	=					np.sin(a_				)

   _UpperCAmelCase   	=					DecisionTree(depth=10    ,  min_leaf_size=10				)
   tree.train(a_    ,  a_				)

   _UpperCAmelCase   	=					(np.random.rand(10				) * 2) - 1
   _UpperCAmelCase   	=					np.array([tree.predict(a_				) for x in test_cases]				)
   _UpperCAmelCase   	=					np.mean((predictions - test_cases) ** 2				)

   print("Test values: " + str(a_				)				)
   print("Predictions: " + str(a_				)				)
   print("Average error: " + str(a_				)				)


if __name__ == "__main__":
  main()
  import doctest

  doctest.testmod(name='mean_squarred_error', verbose=True)

