



from __future__ import annotations

import time

import numpy as np

UpperCAmelCase_									=							[8, 5, 9, 7]
UpperCAmelCase_									=							[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCAmelCase_									=							[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  claim_vector
         _lowerCAmelCase 			=  allocated_resources_table
         _lowerCAmelCase 			=  maximum_claim_table





   def 	__lowerCAmelCase							(	self			):
         return [
             sum(p_item[i] for p_item in self.__allocated_resources_table			)
             for i in range(len(self.__allocated_resources_table[0]			)			)
         ]





   def 	__lowerCAmelCase							(	self			):
         return np.array(self.__claim_vector			) - np.array(
             self.__processes_resource_summation()			)





   def 	__lowerCAmelCase							(	self			):
         return [
             list(np.array(self.__maximum_claim_table[i]			) - np.array(_lowerCAmelCase			)			)
             for i, allocated_resource in enumerate(self.__allocated_resources_table			)
         ]





   def 	__lowerCAmelCase							(	self			):
         return {self.__need().index(_lowerCAmelCase			): i for i in self.__need()}





   def 	__lowerCAmelCase							(	self       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  self.__need()
         _lowerCAmelCase 			=  self.__allocated_resources_table
         _lowerCAmelCase 			=  self.__available_resources()
         _lowerCAmelCase 			=  self.__need_index_manager()
         for kw, val in kwargs.items():
               if kw and val is True:
                     self.__pretty_data()
         print('''_''' * 50 + '''\n'''			)
         while need_list:
               _lowerCAmelCase 			=  False
               for each_need in need_list:
                     _lowerCAmelCase 			=  True
                     for index, need in enumerate(_lowerCAmelCase			):
                           if need > available_resources[index]:
                                 _lowerCAmelCase 			=  False
                                 break
                     if execution:
                           _lowerCAmelCase 			=  True
                           # get the original index of the process from ind_ctrl db
                           for original_need_index, need_clone in need_index_manager.items():
                                 if each_need == need_clone:
                                       _lowerCAmelCase 			=  original_need_index
                           print(F'''Process {process_number + 1} is executing.'''			)
                           # remove the process run from stack
                           need_list.remove(_lowerCAmelCase			)
                           # update available/freed resources stack
                           _lowerCAmelCase 			=  np.array(_lowerCAmelCase			) + np.array(
                               alloc_resources_table[process_number]			)
                           print(
                               '''Updated available resource stack for processes: '''
                               + ''' '''.join([str(_lowerCAmelCase			) for x in available_resources]			)			)
                           break
               if safe:
                     print('''The process is in a safe state.\n'''			)
               else:
                     print('''System in unsafe state. Aborting...\n'''			)
                     break





   def 	__lowerCAmelCase							(	self			):
         print(''' ''' * 9 + '''Allocated Resource Table'''			)
         for item in self.__allocated_resources_table:
               print(
                   F'''P{self.__allocated_resources_table.index(_lowerCAmelCase			) + 1}'''
                   + ''' '''.join(F'''{it:>8}''' for it in item			)
                   + '''\n'''			)
         print(''' ''' * 9 + '''System Resource Table'''			)
         for item in self.__maximum_claim_table:
               print(
                   F'''P{self.__maximum_claim_table.index(_lowerCAmelCase			) + 1}'''
                   + ''' '''.join(F'''{it:>8}''' for it in item			)
                   + '''\n'''			)
         print(
             '''Current Usage by Active Processes: '''
             + ''' '''.join(str(_lowerCAmelCase			) for x in self.__claim_vector			)			)
         print(
             '''Initial Available Resources:       '''
             + ''' '''.join(str(_lowerCAmelCase			) for x in self.__available_resources()			)			)
         time.sleep(1			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      # Initialise PyTorch model
      _lowerCAmelCase 			=  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
      print(f'''Building PyTorch model from configuration: {config}'''				)
      _lowerCAmelCase 			=  AlbertForPreTraining(_SCREAMING_SNAKE_CASE				)

      # Load weights from tf checkpoint
      load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
      torch.save(model.state_dict()     ,				_SCREAMING_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(
        "--albert_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained ALBERT 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.albert_config_file, args.pytorch_dump_path)