


'''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()
lowerCAmelCase			:   int 		=  logging.get_logger(__name__)






def 	lowercase (_A    ):







       """simple docstring"""







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

       return orig_key






def 	lowercase (_A    ):







       """simple docstring"""







       for i in range(6    ):
              # read in weights + bias of input projection layer of self-attention
              _lowerCAmelCase			:							Dict      	=				state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'    )
              _lowerCAmelCase			:							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
              _lowerCAmelCase			:							List[Any]      	=				in_proj_weight[:2_5_6, :]
              _lowerCAmelCase			:							Any      	=				in_proj_bias[:2_5_6]
              _lowerCAmelCase			:							Any      	=				in_proj_weight[2_5_6:5_1_2, :]
              _lowerCAmelCase			:							Union[str, Any]      	=				in_proj_bias[2_5_6:5_1_2]
              _lowerCAmelCase			:							Dict      	=				in_proj_weight[-2_5_6:, :]
              _lowerCAmelCase			:							Optional[int]      	=				in_proj_bias[-2_5_6:]






def 	lowercase ():







       """simple docstring"""







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

       return im






@torch.no_grad()
def 	lowercase (_A      ,  _A      ,  _A      ,  _A      ,  _A      ,  _A      ,  _A      ,  ):







       """simple docstring"""







       _lowerCAmelCase			:							List[str]      	=				DeformableDetrConfig()
       # set config attributes
       if single_scale:
              _lowerCAmelCase			:							Dict      	=				1
       _lowerCAmelCase			:							List[str]      	=				dilation
       _lowerCAmelCase			:							str      	=				with_box_refine
       _lowerCAmelCase			:							int      	=				two_stage
       # set labels
       _lowerCAmelCase			:							Optional[Any]      	=				9_1
       _lowerCAmelCase			:							Tuple      	=				"huggingface/label-files"
       _lowerCAmelCase			:							str      	=				"coco-detection-id2label.json"
       _lowerCAmelCase			:							Optional[int]      	=				json.load(open(cached_download(hf_hub_url(_a      ,  _a      ,  repo_type='dataset'    )    )      ,  'r'    )    )
       _lowerCAmelCase			:							List[str]      	=				{int(_a    ): v for k, v in idalabel.items()}
       _lowerCAmelCase			:							Union[str, Any]      	=				idalabel
       _lowerCAmelCase			:							Dict      	=				{v: k for k, v in idalabel.items()}

       # load image processor
       _lowerCAmelCase			:							int      	=				DeformableDetrImageProcessor(format='coco_detection'    )

       # prepare image
       _lowerCAmelCase			:							Dict      	=				prepare_img()
       _lowerCAmelCase			:							List[str]      	=				image_processor(images=_a      ,  return_tensors='pt'    )
       _lowerCAmelCase			:							str      	=				encoding["pixel_values"]

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

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

       _lowerCAmelCase			:							Tuple      	=				"cuda" if torch.cuda.is_available() else "cpu"
       model.to(_a    )
       # verify our conversion
       _lowerCAmelCase			:							Any      	=				model(pixel_values.to(_a    )    )

       _lowerCAmelCase			:							Dict      	=				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]]    )
       _lowerCAmelCase			:							Union[str, Any]      	=				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:
              _lowerCAmelCase			:							Optional[Any]      	=				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]]    )
              _lowerCAmelCase			:							int      	=				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:
              _lowerCAmelCase			:							int      	=				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]]    )
              _lowerCAmelCase			:							Any      	=				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:
              _lowerCAmelCase			:							Union[str, Any]      	=				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]]    )
              _lowerCAmelCase			:							Optional[int]      	=				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:
              _lowerCAmelCase			:							List[Any]      	=				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]]    )
              _lowerCAmelCase			:							List[Any]      	=				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:
              _lowerCAmelCase			:							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=_a      ,  organization='nielsr'      ,  commit_message='Add model'    )


if __name__ == "__main__":
       lowerCAmelCase			:   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."""
       )
       lowerCAmelCase			:   List[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,
       )









from __future__ import annotations

def    lowerCAmelCase__		(			_a				:	list[int]     ,					_a				:	int			):
		snake_case_       ,				snake_case_ :							Tuple         =				0, len(_a			) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
							print(double_linear_search(list(range(1_00)), 40))
