



import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Union[str, Any]        =  "conditional_detr"
       UpperCAmelCase__				:					Optional[int]        =  ["past_key_values"]
       UpperCAmelCase__				:					Tuple        =  {
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=300,			SCREAMING_SNAKE_CASE_=6,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=6,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_="relu",			SCREAMING_SNAKE_CASE_=256,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1.0,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_="sine",			SCREAMING_SNAKE_CASE_="resnet50",			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=5,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=5,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=0.25,			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[Any]:
          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.'       )
                UpperCamelCase    :  int							=	CONFIG_MAPPING['resnet'](out_features=['stage4']       )
             elif isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  Tuple							=	backbone_config.get('model_type'       )
                UpperCamelCase    :  Any							=	CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase    :  List[Any]							=	config_class.from_dict(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	use_timm_backbone
          UpperCamelCase    :  List[str]							=	backbone_config
          UpperCamelCase    :  List[str]							=	num_channels
          UpperCamelCase    :  Optional[Any]							=	num_queries
          UpperCamelCase    :  List[Any]							=	d_model
          UpperCamelCase    :  Optional[int]							=	encoder_ffn_dim
          UpperCamelCase    :  int							=	encoder_layers
          UpperCamelCase    :  Optional[Any]							=	encoder_attention_heads
          UpperCamelCase    :  Dict							=	decoder_ffn_dim
          UpperCamelCase    :  List[Any]							=	decoder_layers
          UpperCamelCase    :  Optional[Any]							=	decoder_attention_heads
          UpperCamelCase    :  int							=	dropout
          UpperCamelCase    :  Dict							=	attention_dropout
          UpperCamelCase    :  Union[str, Any]							=	activation_dropout
          UpperCamelCase    :  Union[str, Any]							=	activation_function
          UpperCamelCase    :  List[Any]							=	init_std
          UpperCamelCase    :  Optional[Any]							=	init_xavier_std
          UpperCamelCase    :  List[Any]							=	encoder_layerdrop
          UpperCamelCase    :  List[Any]							=	decoder_layerdrop
          UpperCamelCase    :  List[str]							=	encoder_layers
          UpperCamelCase    :  List[Any]							=	auxiliary_loss
          UpperCamelCase    :  int							=	position_embedding_type
          UpperCamelCase    :  List[str]							=	backbone
          UpperCamelCase    :  Dict							=	use_pretrained_backbone
          UpperCamelCase    :  Dict							=	dilation
          # Hungarian matcher
          UpperCamelCase    :  Dict							=	class_cost
          UpperCamelCase    :  Optional[Any]							=	bbox_cost
          UpperCamelCase    :  Any							=	giou_cost
          # Loss coefficients
          UpperCamelCase    :  List[str]							=	mask_loss_coefficient
          UpperCamelCase    :  List[str]							=	dice_loss_coefficient
          UpperCamelCase    :  Any							=	cls_loss_coefficient
          UpperCamelCase    :  Dict							=	bbox_loss_coefficient
          UpperCamelCase    :  List[Any]							=	giou_loss_coefficient
          UpperCamelCase    :  Any							=	focal_alpha
          super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self.encoder_attention_heads
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self.d_model






       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Dict							=	copy.deepcopy(self.__dict__       )
          if self.backbone_config is not None:
             UpperCamelCase    :  Union[str, Any]							=	self.backbone_config.to_dict()
          UpperCamelCase    :  Optional[int]							=	self.__class__.model_type
          return output






class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Tuple        =  version.parse("1.11"							)
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                  ('pixel_mask', {0: 'batch'}),
              ]       )
       @property
       def 					snake_case_       (			self       )     ->		float:
          return 1e-5






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 12








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



