






'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)


def 				lowercase_					(     __A			:							Dict       ,   __A			:							str=False      )							->   Any:



 """simple docstring"""




 lowercase       : str						=[]
 for i in range(config.num_hidden_layers      ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F'blocks.{i}.norm1.weight', F'deit.encoder.layer.{i}.layernorm_before.weight')      )
  rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias')      )
  rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight')      )
  rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias')      )
  rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight')      )
  rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias')      )
  rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight')      )
  rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias')      )
  rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight')      )
  rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias')      )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ('''cls_token''', '''deit.embeddings.cls_token'''),
         ('''dist_token''', '''deit.embeddings.distillation_token'''),
         ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
         ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
         ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
     ]      )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
          ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
          ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
      ]      )

  # if just the base model, we should remove "deit" from all keys that start with "deit"
  lowercase       : Union[str, Any]						=[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''      ) else pair for pair in rename_keys]
 else:
  # layernorm + classification heads
  rename_keys.extend(
      [
          ('''norm.weight''', '''deit.layernorm.weight'''),
          ('''norm.bias''', '''deit.layernorm.bias'''),
          ('''head.weight''', '''cls_classifier.weight'''),
          ('''head.bias''', '''cls_classifier.bias'''),
          ('''head_dist.weight''', '''distillation_classifier.weight'''),
          ('''head_dist.bias''', '''distillation_classifier.bias'''),
      ]      )

 return rename_keys


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Dict       ,   __A			:							str=False      )							->   Optional[Any]:



 """simple docstring"""




 for i in range(config.num_hidden_layers      ):
  if base_model:
   lowercase       : Optional[Any]						=''''''
  else:
   lowercase       : Optional[Any]						='''deit.'''
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  lowercase       : Any						=state_dict.pop(F'blocks.{i}.attn.qkv.weight'      )
  lowercase       : str						=state_dict.pop(F'blocks.{i}.attn.qkv.bias'      )
  # next, add query, keys and values (in that order) to the state dict
  lowercase       : Union[str, Any]						=in_proj_weight[
      : config.hidden_size, :
  ]
  lowercase       : Optional[Any]						=in_proj_bias[: config.hidden_size]
  lowercase       : Optional[Any]						=in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowercase       : Tuple						=in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  lowercase       : Union[str, Any]						=in_proj_weight[
      -config.hidden_size :, :
  ]
  lowercase       : Any						=in_proj_bias[-config.hidden_size :]


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Optional[int]       ,   __A			:							List[Any]      )							->   List[Any]:



 """simple docstring"""




 lowercase       : str						=dct.pop(__A      )
 lowercase       : int						=val


def 				lowercase_					(     )							->   str:



 """simple docstring"""




 lowercase       : Dict						='''http://images.cocodataset.org/val2017/000000039769.jpg'''
 lowercase       : List[str]						=Image.open(requests.get(__A       ,   stream=__A      ).raw      )
 return im


@torch.no_grad()
def 				lowercase_					(     __A			:							List[str]       ,   __A			:							str      )							->   Tuple:



 """simple docstring"""




 lowercase       : Any						=DeiTConfig()
 # all deit models have fine-tuned heads
 lowercase       : Dict						=False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 lowercase       : List[str]						=1_0_0_0
 lowercase       : Any						='''huggingface/label-files'''
 lowercase       : List[Any]						='''imagenet-1k-id2label.json'''
 lowercase       : Optional[Any]						=json.load(open(hf_hub_download(__A       ,   __A       ,   repo_type='''dataset'''      )       ,   '''r'''      )      )
 lowercase       : Dict						={int(__A      ): v for k, v in idalabel.items()}
 lowercase       : Tuple						=idalabel
 lowercase       : Any						={v: k for k, v in idalabel.items()}
 lowercase       : Dict						=int(deit_name[-6:-4]      )
 lowercase       : Any						=int(deit_name[-3:]      )
 # size of the architecture
 if deit_name[9:].startswith('''tiny'''      ):
  lowercase       : Any						=1_9_2
  lowercase       : Optional[int]						=7_6_8
  lowercase       : List[Any]						=1_2
  lowercase       : Tuple						=3
 elif deit_name[9:].startswith('''small'''      ):
  lowercase       : List[Any]						=3_8_4
  lowercase       : str						=1_5_3_6
  lowercase       : List[Any]						=1_2
  lowercase       : List[Any]						=6
 if deit_name[9:].startswith('''base'''      ):
  pass
 elif deit_name[4:].startswith('''large'''      ):
  lowercase       : List[Any]						=1_0_2_4
  lowercase       : str						=4_0_9_6
  lowercase       : List[str]						=2_4
  lowercase       : str						=1_6

 # load original model from timm
 lowercase       : Optional[Any]						=timm.create_model(__A       ,   pretrained=__A      )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 lowercase       : Optional[Any]						=timm_model.state_dict()
 lowercase       : Dict						=create_rename_keys(__A       ,   __A      )
 for src, dest in rename_keys:
  rename_key(__A       ,   __A       ,   __A      )
 read_in_q_k_v(__A       ,   __A       ,   __A      )

 # load HuggingFace model
 lowercase       : Optional[int]						=DeiTForImageClassificationWithTeacher(__A      ).eval()
 model.load_state_dict(__A      )

 # Check outputs on an image, prepared by DeiTImageProcessor
 lowercase       : Tuple						=int(
     (2_5_6 / 2_2_4) * config.image_size      )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
 lowercase       : List[str]						=DeiTImageProcessor(size=__A       ,   crop_size=config.image_size      )
 lowercase       : Optional[int]						=image_processor(images=prepare_img()       ,   return_tensors='''pt'''      )
 lowercase       : int						=encoding['''pixel_values''']
 lowercase       : List[Any]						=model(__A      )

 lowercase       : str						=timm_model(__A      )
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(__A       ,   outputs.logits       ,   atol=1E-3      )

 Path(__A      ).mkdir(exist_ok=__A      )
 print(F'Saving model {deit_name} to {pytorch_dump_folder_path}'      )
 model.save_pretrained(__A      )
 print(F'Saving image processor to {pytorch_dump_folder_path}'      )
 image_processor.save_pretrained(__A      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--deit_name',
          default='vit_deit_base_distilled_patch16_224',
          type=str,
          help='Name of the DeiT timm model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )

      SCREAMING_SNAKE_CASE							=						parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCamelCase   :			Any          = logging.get_logger(__name__)

__UpperCamelCase   :			Tuple          = {
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''deformable_detr'''
							UpperCamelCase__   	=    {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}
							def __init__(		self		:List[str]						, __magic_name__		:Tuple=True						, __magic_name__		:str=None						, __magic_name__		:Tuple=3						, __magic_name__		:List[str]=300						, __magic_name__		:Dict=1024						, __magic_name__		:Tuple=6						, __magic_name__		:Tuple=1024						, __magic_name__		:int=8						, __magic_name__		:List[Any]=6						, __magic_name__		:Tuple=1024						, __magic_name__		:List[Any]=8						, __magic_name__		:Union[str, Any]=0.0						, __magic_name__		:Union[str, Any]=True						, __magic_name__		:str="relu"						, __magic_name__		:Optional[Any]=256						, __magic_name__		:int=0.1						, __magic_name__		:List[str]=0.0						, __magic_name__		:str=0.0						, __magic_name__		:str=0.02						, __magic_name__		:int=1.0						, __magic_name__		:List[str]=True						, __magic_name__		:Union[str, Any]=False						, __magic_name__		:List[Any]="sine"						, __magic_name__		:Union[str, Any]="resnet50"						, __magic_name__		:int=True						, __magic_name__		:Any=False						, __magic_name__		:List[str]=4						, __magic_name__		:Tuple=4						, __magic_name__		:Union[str, Any]=4						, __magic_name__		:Optional[int]=False						, __magic_name__		:Tuple=300						, __magic_name__		:Tuple=False						, __magic_name__		:List[Any]=1						, __magic_name__		:List[str]=5						, __magic_name__		:List[Any]=2						, __magic_name__		:Optional[int]=1						, __magic_name__		:Union[str, Any]=1						, __magic_name__		:List[Any]=5						, __magic_name__		:Optional[Any]=2						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:List[str]=0.25						, __magic_name__		:str=False						, **__magic_name__		:List[str]						, ):







								'''simple docstring'''







								if backbone_config is not None and use_timm_backbone:
									raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""							)

								if not use_timm_backbone:
									if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
										a									=				CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
									elif isinstance(__magic_name__						, __magic_name__							):
										a									=				backbone_config.get("""model_type"""							)
										a									=				CONFIG_MAPPING[backbone_model_type]
										a									=				config_class.from_dict(__magic_name__							)
								a									=				use_timm_backbone
								a									=				backbone_config
								a									=				num_channels
								a									=				num_queries
								a									=				max_position_embeddings
								a									=				d_model
								a									=				encoder_ffn_dim
								a									=				encoder_layers
								a									=				encoder_attention_heads
								a									=				decoder_ffn_dim
								a									=				decoder_layers
								a									=				decoder_attention_heads
								a									=				dropout
								a									=				attention_dropout
								a									=				activation_dropout
								a									=				activation_function
								a									=				init_std
								a									=				init_xavier_std
								a									=				encoder_layerdrop
								a									=				auxiliary_loss
								a									=				position_embedding_type
								a									=				backbone
								a									=				use_pretrained_backbone
								a									=				dilation
								# deformable attributes
								a									=				num_feature_levels
								a									=				encoder_n_points
								a									=				decoder_n_points
								a									=				two_stage
								a									=				two_stage_num_proposals
								a									=				with_box_refine
								if two_stage is True and with_box_refine is False:
									raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
								# Hungarian matcher
								a									=				class_cost
								a									=				bbox_cost
								a									=				giou_cost
								# Loss coefficients
								a									=				mask_loss_coefficient
								a									=				dice_loss_coefficient
								a									=				bbox_loss_coefficient
								a									=				giou_loss_coefficient
								a									=				eos_coefficient
								a									=				focal_alpha
								a									=				disable_custom_kernels
								super().__init__(is_encoder_decoder=__magic_name__						, **__magic_name__							)
							@property
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								return self.encoder_attention_heads
							@property
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								return self.d_model





							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				copy.deepcopy(self.__dict__							)
								if self.backbone_config is not None:
									a									=				self.backbone_config.to_dict()
								a									=				self.__class__.model_type
								return output

