


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

def       _a	(					SCREAMING_SNAKE_CASE						:					Any  ):







 """simple docstring"""



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

 return orig_key

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[Any]  ):







 """simple docstring"""



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

def       _a	(					):







 """simple docstring"""



 UpperCamelCase__       :     Optional[Any] 							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 UpperCamelCase__       :     Optional[Any] 							= Image.open(requests.get(UpperCAmelCase_     ,			stream=UpperCAmelCase_  ).raw  )

 return im

@torch.no_grad()
def       _a	(					SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			):







 """simple docstring"""



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

 # load image processor
 UpperCamelCase__       :     Optional[Any] 							= DeformableDetrImageProcessor(format='''coco_detection'''  )

 # prepare image
 UpperCamelCase__       :     Optional[int] 							= prepare_img()
 UpperCamelCase__       :     List[str] 							= image_processor(images=UpperCAmelCase_     ,			return_tensors='''pt'''  )
 UpperCamelCase__       :     int 							= encoding['''pixel_values''']

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

 # load original state dict
 UpperCamelCase__       :     List[str] 							= torch.load(UpperCAmelCase_     ,			map_location='''cpu'''  )['''model''']
 # rename keys
 for key in state_dict.copy().keys():
  UpperCamelCase__       :     Any 							= state_dict.pop(UpperCAmelCase_  )
  UpperCamelCase__       :     List[Any] 							= val
 # query, key and value matrices need special treatment
 read_in_q_k_v(UpperCAmelCase_  )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 UpperCamelCase__       :     Any 							= '''model.'''
 for key in state_dict.copy().keys():
  if not key.startswith('''class_embed'''  ) and not key.startswith('''bbox_embed'''  ):
   UpperCamelCase__       :     Dict 							= state_dict.pop(UpperCAmelCase_  )
   UpperCamelCase__       :     str 							= val
    # finally, create HuggingFace model and load state dict
 UpperCamelCase__       :     Union[str, Any] 							= DeformableDetrForObjectDetection(UpperCAmelCase_  )
 model.load_state_dict(UpperCAmelCase_  )
 model.eval()

 UpperCamelCase__       :     str 							= '''cuda''' if torch.cuda.is_available() else '''cpu'''
 model.to(UpperCAmelCase_  )
 # verify our conversion
 UpperCamelCase__       :     Optional[Any] 							= model(pixel_values.to(UpperCAmelCase_  )  )

 UpperCamelCase__       :     List[Any] 							= torch.tensor(
     [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]  )
 UpperCamelCase__       :     Tuple 							= torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]  )

 if single_scale:
  UpperCamelCase__       :     Dict 							= torch.tensor(
      [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]  )
  UpperCamelCase__       :     Optional[Any] 							= torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]  )

 if single_scale and dilation:
  UpperCamelCase__       :     Tuple 							= torch.tensor(
      [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]  )
  UpperCamelCase__       :     List[str] 							= torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]  )

 if with_box_refine:
  UpperCamelCase__       :     Dict 							= torch.tensor(
      [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]  )
  UpperCamelCase__       :     Union[str, Any] 							= torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]  )

 if with_box_refine and two_stage:
  UpperCamelCase__       :     int 							= torch.tensor(
      [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]  )
  UpperCamelCase__       :     Any 							= torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]  )

 print('''Logits:'''     ,			outputs.logits[0, :3, :3]  )

 assert torch.allclose(outputs.logits[0, :3, :3]     ,			expected_logits.to(UpperCAmelCase_  )     ,			atol=1E-4  )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,			expected_boxes.to(UpperCAmelCase_  )     ,			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(UpperCAmelCase_  ).mkdir(exist_ok=UpperCAmelCase_  )
 model.save_pretrained(UpperCAmelCase_  )
 image_processor.save_pretrained(UpperCAmelCase_  )

 # Push to hub
 if push_to_hub:
  UpperCamelCase__       :     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=UpperCAmelCase_     ,			organization='''nielsr'''     ,			commit_message='''Add model'''  )


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








from __future__ import annotations

import random







class 		__UpperCAmelCase:





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     6

     def __init__(   self			:							int     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							list(range(1    ,					Dice.NUM_SIDES + 1     )     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         return random.choice(self.sides     )






def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ = 2		):
    a__  			=							[Dice() for i in range(UpperCAmelCase_		)]
    a__  			=							[0] * (len(UpperCAmelCase_		) * Dice.NUM_SIDES + 1)
    for _ in range(UpperCAmelCase_		):
        count_of_sum[sum(dice.roll() for dice in dices		)] += 1
    a__  			=							[round((count * 100) / num_throws ,     2		) for count in count_of_sum]
    return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
 import doctest

 doctest.testmod()






