
'''simple docstring'''



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def  lowerCAmelCase_						(					SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_    )  ->  Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE  			=					("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

   _SCREAMING_SNAKE_CASE  			=					(
       ("""layer.""", """layer_"""),
       ("""word_embeddings.weight""", """word_embeddings"""),
       ("""position_embeddings.weight""", """position_embeddings"""),
       ("""token_type_embeddings.weight""", """token_type_embeddings"""),
       (""".""", """/"""),
       ("""LayerNorm/weight""", """LayerNorm/gamma"""),
       ("""LayerNorm/bias""", """LayerNorm/beta"""),
       ("""weight""", """kernel"""),
   )

   if not os.path.isdir(__UpperCAmelCase    ):
      os.makedirs(__UpperCAmelCase    )

   _SCREAMING_SNAKE_CASE  			=					model.state_dict()

   def to_tf_var_name(SCREAMING_SNAKE_CASE_    ):
      for patt, repl in iter(__UpperCAmelCase    ):
         _SCREAMING_SNAKE_CASE  			=					name.replace(__UpperCAmelCase     ,   __UpperCAmelCase    )
      return F"bert/{name}"

   def create_tf_var(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_    ):
      _SCREAMING_SNAKE_CASE  			=					tf.dtypes.as_dtype(tensor.dtype    )
      _SCREAMING_SNAKE_CASE  			=					tf.get_variable(dtype=__UpperCAmelCase     ,   shape=tensor.shape     ,   name=__UpperCAmelCase     ,   initializer=tf.zeros_initializer()    )
      session.run(tf.variables_initializer([tf_var]    )    )
      session.run(__UpperCAmelCase    )
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         _SCREAMING_SNAKE_CASE  			=					to_tf_var_name(__UpperCAmelCase    )
         _SCREAMING_SNAKE_CASE  			=					state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose    ):
            _SCREAMING_SNAKE_CASE  			=					torch_tensor.T
         _SCREAMING_SNAKE_CASE  			=					create_tf_var(tensor=__UpperCAmelCase     ,   name=__UpperCAmelCase     ,   session=__UpperCAmelCase    )
         tf.keras.backend.set_value(__UpperCAmelCase     ,   __UpperCAmelCase    )
         _SCREAMING_SNAKE_CASE  			=					session.run(__UpperCAmelCase    )
         print(F"Successfully created {tf_name}: {np.allclose(__UpperCAmelCase     ,   __UpperCAmelCase    )}"    )

      _SCREAMING_SNAKE_CASE  			=					tf.train.Saver(tf.trainable_variables()    )
      saver.save(__UpperCAmelCase     ,   os.path.join(__UpperCAmelCase     ,   model_name.replace("""-"""     ,   """_"""    ) + """.ckpt"""    )    )
def  lowerCAmelCase_						(					SCREAMING_SNAKE_CASE_=None    )  ->  List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE  			=					argparse.ArgumentParser()
   parser.add_argument("""--model_name"""     ,   type=__UpperCAmelCase     ,   required=__UpperCAmelCase     ,   help="""model name e.g. bert-base-uncased"""    )
   parser.add_argument(
       """--cache_dir"""     ,   type=__UpperCAmelCase     ,   default=__UpperCAmelCase     ,   required=__UpperCAmelCase     ,   help="""Directory containing pytorch model"""    )
   parser.add_argument("""--pytorch_model_path"""     ,   type=__UpperCAmelCase     ,   required=__UpperCAmelCase     ,   help="""/path/to/<pytorch-model-name>.bin"""    )
   parser.add_argument("""--tf_cache_dir"""     ,   type=__UpperCAmelCase     ,   required=__UpperCAmelCase     ,   help="""Directory in which to save tensorflow model"""    )
   _SCREAMING_SNAKE_CASE  			=					parser.parse_args(__UpperCAmelCase    )

   _SCREAMING_SNAKE_CASE  			=					BertModel.from_pretrained(
       pretrained_model_name_or_path=args.model_name     ,   state_dict=torch.load(args.pytorch_model_path    )     ,   cache_dir=args.cache_dir     ,   )

   convert_pytorch_checkpoint_to_tf(model=__UpperCAmelCase     ,   ckpt_dir=args.tf_cache_dir     ,   model_name=args.model_name    )


if __name__ == "__main__":
     main()











from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

 # Sort by arrival time.
 a__												= [burst_time[i] for i in np.argsort(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  a__												= 0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   a__												= arrival_time[i]

  a__												= 0
  # Index showing the location of the process being performed
  a__												= 0
  # Saves the current response ratio.
  a__												= 0
  for i in range(0							,  __UpperCAmelCase       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    a__												= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    a__												= temp
    a__												= i

        # Calculate the turn around time
  a__												= current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  a__												= 1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     a_      :      List[Any]    =     5
     a_      :      Optional[int]    =     ['A', 'B', 'C', 'D', 'E']
     a_      :      Union[str, Any]    =     [1, 2, 3, 4, 5]
     a_      :      Dict    =     [1, 2, 3, 4, 5]

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
              f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
          )

     print(f'average waiting time : {mean(waiting_time):.5f}')
     print(f'average turn around time : {mean(turn_around_time):.5f}')





