

'''simple docstring'''






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


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      {
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Any							     =     'yolos'






  def __init__( self  :	Union[str, Any]   ,lowercase__  :	Any=7_6_8   ,lowercase__  :	Optional[Any]=1_2   ,lowercase__  :	Optional[Any]=1_2   ,lowercase__  :	List[Any]=3_0_7_2   ,lowercase__  :	List[Any]="gelu"   ,lowercase__  :	Optional[int]=0.0   ,lowercase__  :	Tuple=0.0   ,lowercase__  :	str=0.0_2   ,lowercase__  :	Any=1e-1_2   ,lowercase__  :	Optional[Any]=[5_1_2, 8_6_4]   ,lowercase__  :	Union[str, Any]=1_6   ,lowercase__  :	Optional[int]=3   ,lowercase__  :	Any=True   ,lowercase__  :	str=1_0_0   ,lowercase__  :	int=True   ,lowercase__  :	str=False   ,lowercase__  :	List[Any]=1   ,lowercase__  :	List[Any]=5   ,lowercase__  :	Dict=2   ,lowercase__  :	int=5   ,lowercase__  :	int=2   ,lowercase__  :	Optional[Any]=0.1   ,**lowercase__  :	int   ,):
   super().__init__(**lowercase__     )

   __lowercase				    =					hidden_size
   __lowercase				    =					num_hidden_layers
   __lowercase				    =					num_attention_heads
   __lowercase				    =					intermediate_size
   __lowercase				    =					hidden_act
   __lowercase				    =					hidden_dropout_prob
   __lowercase				    =					attention_probs_dropout_prob
   __lowercase				    =					initializer_range
   __lowercase				    =					layer_norm_eps
   __lowercase				    =					image_size
   __lowercase				    =					patch_size
   __lowercase				    =					num_channels
   __lowercase				    =					qkv_bias
   __lowercase				    =					num_detection_tokens
   __lowercase				    =					use_mid_position_embeddings
   __lowercase				    =					auxiliary_loss
   # Hungarian matcher
   __lowercase				    =					class_cost
   __lowercase				    =					bbox_cost
   __lowercase				    =					giou_cost
   # Loss coefficients
   __lowercase				    =					bbox_loss_coefficient
   __lowercase				    =					giou_loss_coefficient
   __lowercase				    =					eos_coefficient






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    List[str]							     =     version.parse('1.11' )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return OrderedDict(
       [
           ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
       ]     )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return 1e-4






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   return 1_2







import argparse

import torch

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


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		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."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
