
import copy

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


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''deta'''
     lowerCAmelCase_							   =			{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }



     def __init__(				self							:							List[str]     ,						_A							:							Dict=None     ,						_A							:							Optional[int]=900     ,						_A							:							Dict=2048     ,						_A							:							int=6     ,						_A							:							Dict=2048     ,						_A							:							List[Any]=8     ,						_A							:							Union[str, Any]=6     ,						_A							:							Tuple=1024     ,						_A							:							Tuple=8     ,						_A							:							Dict=0.0     ,						_A							:							List[Any]=True     ,						_A							:							List[Any]="relu"     ,						_A							:							Tuple=256     ,						_A							:							Union[str, Any]=0.1     ,						_A							:							Optional[Any]=0.0     ,						_A							:							Dict=0.0     ,						_A							:							int=0.02     ,						_A							:							int=1.0     ,						_A							:							Optional[Any]=True     ,						_A							:							Any=False     ,						_A							:							Optional[int]="sine"     ,						_A							:							Optional[int]=5     ,						_A							:							List[Any]=4     ,						_A							:							Tuple=4     ,						_A							:							Dict=True     ,						_A							:							Tuple=300     ,						_A							:							Union[str, Any]=True     ,						_A							:							Optional[Any]=True     ,						_A							:							Optional[Any]=1     ,						_A							:							Dict=5     ,						_A							:							Optional[int]=2     ,						_A							:							Tuple=1     ,						_A							:							Union[str, Any]=1     ,						_A							:							Union[str, Any]=5     ,						_A							:							Tuple=2     ,						_A							:							str=0.1     ,						_A							:							Optional[int]=0.25     ,						**_A							:							Optional[Any]     ,						):







       """simple docstring"""




       if backbone_config is None:
         logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
         __SCREAMING_SNAKE_CASE      :	Dict					=		CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
       else:
         if isinstance(_A     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	int					=		backbone_config.pop('''model_type''' )
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		CONFIG_MAPPING[backbone_model_type]
           __SCREAMING_SNAKE_CASE      :	Any					=		config_class.from_dict(_A )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		backbone_config
       __SCREAMING_SNAKE_CASE      :	Tuple					=		num_queries
       __SCREAMING_SNAKE_CASE      :	List[str]					=		max_position_embeddings
       __SCREAMING_SNAKE_CASE      :	Tuple					=		d_model
       __SCREAMING_SNAKE_CASE      :	Any					=		encoder_ffn_dim
       __SCREAMING_SNAKE_CASE      :	int					=		encoder_layers
       __SCREAMING_SNAKE_CASE      :	List[str]					=		encoder_attention_heads
       __SCREAMING_SNAKE_CASE      :	Tuple					=		decoder_ffn_dim
       __SCREAMING_SNAKE_CASE      :	str					=		decoder_layers
       __SCREAMING_SNAKE_CASE      :	Tuple					=		decoder_attention_heads
       __SCREAMING_SNAKE_CASE      :	Tuple					=		dropout
       __SCREAMING_SNAKE_CASE      :	int					=		attention_dropout
       __SCREAMING_SNAKE_CASE      :	Dict					=		activation_dropout
       __SCREAMING_SNAKE_CASE      :	str					=		activation_function
       __SCREAMING_SNAKE_CASE      :	List[str]					=		init_std
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		init_xavier_std
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		encoder_layerdrop
       __SCREAMING_SNAKE_CASE      :	Tuple					=		auxiliary_loss
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		position_embedding_type
       # deformable attributes
       __SCREAMING_SNAKE_CASE      :	Dict					=		num_feature_levels
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		encoder_n_points
       __SCREAMING_SNAKE_CASE      :	Dict					=		decoder_n_points
       __SCREAMING_SNAKE_CASE      :	List[str]					=		two_stage
       __SCREAMING_SNAKE_CASE      :	Dict					=		two_stage_num_proposals
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		with_box_refine
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		assign_first_stage
       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
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		class_cost
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		bbox_cost
       __SCREAMING_SNAKE_CASE      :	str					=		giou_cost
       # Loss coefficients
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		mask_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	Tuple					=		dice_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	int					=		bbox_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	str					=		giou_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	int					=		eos_coefficient
       __SCREAMING_SNAKE_CASE      :	List[str]					=		focal_alpha
       super().__init__(is_encoder_decoder=_A     ,						**_A )



     @property
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       return self.encoder_attention_heads



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return self.d_model




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		copy.deepcopy(self.__dict__ )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.backbone_config.to_dict()
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.__class__.model_type
       return output







'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							LevitConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True		)		->  int:
   print(F'''Converting {name}...'''		)

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            snake_case 						=   timm.create_model("""levit_128s"""      ,    pretrained=__lowerCAmelCase		)
         else:
            snake_case 						=   timm.create_model("""levit_128"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 1_92:
         snake_case 						=   timm.create_model("""levit_192"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 2_56:
         snake_case 						=   timm.create_model("""levit_256"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 3_84:
         snake_case 						=   timm.create_model("""levit_384"""      ,    pretrained=__lowerCAmelCase		)

      from_model.eval()
      snake_case 						=   LevitForImageClassificationWithTeacher(__lowerCAmelCase		).eval()
      snake_case 						=   OrderedDict()

      snake_case 						=   from_model.state_dict()
      snake_case 						=   list(from_model.state_dict().keys()		)
      snake_case 						=   list(our_model.state_dict().keys()		)
      print(len(__lowerCAmelCase		)      ,    len(__lowerCAmelCase		)		)
      for i in range(len(__lowerCAmelCase		)		):
         snake_case 						=   weights[og_keys[i]]
      our_model.load_state_dict(__lowerCAmelCase		)

      snake_case 						=   torch.randn((2, 3, 2_24, 2_24)		)
      snake_case 						=   from_model(__lowerCAmelCase		)
      snake_case 						=   our_model(__lowerCAmelCase		).logits

   assert torch.allclose(__lowerCAmelCase      ,    __lowerCAmelCase		), "The model logits don't match the original one."

   snake_case 						=   name
   print(__lowerCAmelCase		)

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name		)
      snake_case 						=   LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name		)

      print(F'''Pushed {checkpoint_name}'''		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  List[Any]:
   snake_case 						=   """imagenet-1k-id2label.json"""
   snake_case 						=   10_00
   snake_case 						=   (1, num_labels)

   snake_case 						=   """huggingface/label-files"""
   snake_case 						=   num_labels
   snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
   snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}

   snake_case 						=   idalabel
   snake_case 						=   {v: k for k, v in idalabel.items()}

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

   snake_case 						=   {
       """levit-128S""": 1_28,
       """levit-128""": 1_28,
       """levit-192""": 1_92,
       """levit-256""": 2_56,
       """levit-384""": 3_84,
   }

   snake_case 						=   {
       """levit-128S""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[2, 3, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-128""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 8, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-192""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]      ,    num_attention_heads=[3, 5, 6]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-256""": ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-384""": ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]      ,    num_attention_heads=[6, 9, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0.1      ,    ),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]      ,    __lowerCAmelCase      ,    names_to_config[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   return config, expected_shape


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default=None,
     type=str,
     help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="levit-dump-folder/",
     type=Path,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 _SCREAMING_SNAKE_CASE							     =			args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

