






import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


__UpperCAmelCase      				=							logging.get_logger(__name__)
class 					a_(						nn.Module					):



     """simple docstring"""



     def __init__(						self			:					Optional[Any])    ->					Union[str, Any]:

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__) < version.parse('1.12.0'):
       raise ImportError(
           f'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
           'PytorchGELUTanh. Please upgrade torch.')





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return nn.functional.gelu(lowerCAmelCase__       ,       approximate='tanh')

class 					a_(						nn.Module					):



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__       ,       3.0))))

class 					a_(						nn.Module					):



     """simple docstring"""



     def __init__(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					bool = False)    ->					Any:

      """simple docstring"""
      super().__init__()
      if use_gelu_python:
       SCREAMING_SNAKE_CASE							  =						self._gelu_python
      else:
       SCREAMING_SNAKE_CASE							  =						nn.functional.gelu





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return self.act(lowerCAmelCase__)

class 					a_(						nn.Module					):



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(input * 0.79_78_84_56_08 * (1.0 + 0.04_47_15 * input * input)))

class 					a_(						nn.Module					):



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return input * torch.sigmoid(1.7_02 * input)

class 					a_(						nn.Module					):



     """simple docstring"""



     def __init__(						self			:					Optional[int]       ,       lowerCAmelCase__			:					float       ,       lowerCAmelCase__			:					float)    ->					str:

      """simple docstring"""
      if min > max:
       raise ValueError(f'''min should be < max (got min: {min}, max: {max})''')

      super().__init__()
      SCREAMING_SNAKE_CASE							  =						min
      SCREAMING_SNAKE_CASE							  =						max





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return torch.clip(gelu(lowerCAmelCase__)       ,       self.min       ,       self.max)

class 					a_(						nn.Module					):



     """simple docstring"""



     def __init__(						self			:					int)    ->					Dict:

      """simple docstring"""
      super().__init__()
      SCREAMING_SNAKE_CASE							  =						math.sqrt(2 / math.pi)





     def   __UpperCamelCase		(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__       ,       3))))

class 					a_(						nn.Module					):



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return nn.functional.silu(lowerCAmelCase__)

class 					a_(						nn.Module					):



     """simple docstring"""



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

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__) < version.parse('1.9.0'):
       SCREAMING_SNAKE_CASE							  =						self._mish_python
      else:
       SCREAMING_SNAKE_CASE							  =						nn.functional.mish





     def   __UpperCamelCase		(						self			:					Optional[int]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return input * torch.tanh(nn.functional.softplus(lowerCAmelCase__))





     def   __UpperCamelCase		(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return self.act(lowerCAmelCase__)

class 					a_(						nn.Module					):



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					Tensor)    ->					Tensor:

      """simple docstring"""
      return input

class 					a_(						nn.Module					):



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					Tuple       ,       lowerCAmelCase__			:					Dict=0.70_71_07       ,       lowerCAmelCase__			:					Optional[int]=0.28_20_95)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						(input - mu).div(sigma * math.sqrt(2.0))
      return 0.5 * (1.0 + torch.erf(lowerCAmelCase__))

class 					a_(						nn.Module					):



     """simple docstring"""



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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						nn.functional.relu(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						torch.square(lowerCAmelCase__)
      return squared

class 					a_(						lowercase__					):



     """simple docstring"""



     def __getitem__(						self			:					List[Any]       ,       lowerCAmelCase__			:					List[str])    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						super().__getitem__(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						content if isinstance(lowerCAmelCase__       ,       lowerCAmelCase__) else (content, {})
      return cls(**lowerCAmelCase__)

__UpperCAmelCase      				=							{
    "gelu": GELUActivation,
    "gelu_10": (ClippedGELUActivation, {"min": -1_0, "max": 1_0}),
    "gelu_fast": FastGELUActivation,
    "gelu_new": NewGELUActivation,
    "gelu_python": (GELUActivation, {"use_gelu_python": True}),
    "gelu_pytorch_tanh": PytorchGELUTanh,
    "gelu_accurate": AccurateGELUActivation,
    "laplace": LaplaceActivation,
    "linear": LinearActivation,
    "mish": MishActivation,
    "quick_gelu": QuickGELUActivation,
    "relu": nn.ReLU,
    "relu2": ReLUSquaredActivation,
    "relu6": nn.ReLUa,
    "sigmoid": nn.Sigmoid,
    "silu": SiLUActivation,
    "swish": SiLUActivation,
    "tanh": nn.Tanh,
}
__UpperCAmelCase      				=							ClassInstantier(ACTaCLS)





def 		A_			(		lowercase_	)							->str:







 """simple docstring"""

 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()	)}'''	)


# For backwards compatibility with: from activations import gelu_python
__UpperCAmelCase      				=							get_activation("gelu_python")
__UpperCAmelCase      				=							get_activation("gelu_new")
__UpperCAmelCase      				=							get_activation("gelu")
__UpperCAmelCase      				=							get_activation("gelu_fast")
__UpperCAmelCase      				=							get_activation("quick_gelu")
__UpperCAmelCase      				=							get_activation("silu")
__UpperCAmelCase      				=							get_activation("mish")
__UpperCAmelCase      				=							get_activation("linear")












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      				=							logging.get_logger(__name__)





def 		A_			(		lowercase_	)							->Tuple:







 """simple docstring"""

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

 return orig_key





def 		A_			(		lowercase_	)							->Dict:







 """simple docstring"""

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





def 		A_			(		)							->Union[str, Any]:







 """simple docstring"""

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

 return im





@torch.no_grad()
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       )							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						DeformableDetrConfig()
 # set config attributes
 if single_scale:
  SCREAMING_SNAKE_CASE							  =						1
 SCREAMING_SNAKE_CASE							  =						dilation
 SCREAMING_SNAKE_CASE							  =						with_box_refine
 SCREAMING_SNAKE_CASE							  =						two_stage
 # set labels
 SCREAMING_SNAKE_CASE							  =						9_1
 SCREAMING_SNAKE_CASE							  =						'huggingface/label-files'
 SCREAMING_SNAKE_CASE							  =						'coco-detection-id2label.json'
 SCREAMING_SNAKE_CASE							  =						json.load(open(cached_download(hf_hub_url(lowercase_					,       lowercase_					,       repo_type='dataset'	)	)					,       'r'	)	)
 SCREAMING_SNAKE_CASE							  =						{int(lowercase_	): v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE							  =						idalabel
 SCREAMING_SNAKE_CASE							  =						{v: k for k, v in idalabel.items()}

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

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

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

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

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

 SCREAMING_SNAKE_CASE							  =						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]]	)
 SCREAMING_SNAKE_CASE							  =						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:
  SCREAMING_SNAKE_CASE							  =						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]]	)
  SCREAMING_SNAKE_CASE							  =						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:
  SCREAMING_SNAKE_CASE							  =						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]]	)
  SCREAMING_SNAKE_CASE							  =						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:
  SCREAMING_SNAKE_CASE							  =						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]]	)
  SCREAMING_SNAKE_CASE							  =						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:
  SCREAMING_SNAKE_CASE							  =						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]]	)
  SCREAMING_SNAKE_CASE							  =						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(lowercase_	)					,       atol=1e-4	)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,       expected_boxes.to(lowercase_	)					,       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(lowercase_	).mkdir(exist_ok=lowercase_	)
 model.save_pretrained(lowercase_	)
 image_processor.save_pretrained(lowercase_	)

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


if __name__ == "__main__":
      __UpperCAmelCase      				=							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      				=							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,
      )




