


def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->list:


 '''simple docstring'''
 snake_case :			Union[str, Any]							=   sorted(__snake_case			,  key=lambda __lowerCamelCase							: value[2]			,  reverse=__snake_case							)

 # Create a list of size equal to the maximum deadline
 # and initialize it with -1
 snake_case :			List[str]							=   max(__snake_case			,  key=lambda __lowerCamelCase							: value[1]							)[1]
 snake_case :			Tuple							=   [-1] * max_deadline

 # Finding the maximum profit and the count of jobs
 snake_case :			List[str]							=   0
 snake_case :			Dict							=   0
 for job in jobs:
  # Find a free time slot for this job
  # (Note that we start from the last possible slot)
  for i in range(job[1] - 1			,  -1			,  -1							):
   if time_slots[i] == -1:
    snake_case :			Dict							=   job[0]
    count += 1
    max_profit += job[2]
    break
 return [count, max_profit]


if __name__ == "__main__":
  import doctest

  doctest.testmod()








import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    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(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT 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."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
