




'''simple docstring'''







from __future__ import annotations

import time

import numpy as np

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


class 			_a  :
       def __init__(	self						:	str   ,				lowercase						:	list[int]   ,				lowercase						:	list[list[int]]   ,				lowercase						:	list[list[int]]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		claim_vector
           UpperCAmelCase			       =		allocated_resources_table
           UpperCAmelCase			       =		maximum_claim_table
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

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



           '''simple docstring'''

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



           '''simple docstring'''

           return [
               list(np.array(self.__maximum_claim_table[i]				) - np.array(SCREAMING_SNAKE_CASE__				)				)
               for i, allocated_resource in enumerate(self.__allocated_resources_table				)
           ]
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return {self.__need().index(SCREAMING_SNAKE_CASE__				): i for i in self.__need()}
       def     A      (	self						:	Dict   ,				**lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.__need()
           UpperCAmelCase			       =		self.__allocated_resources_table
           UpperCAmelCase			       =		self.__available_resources()
           UpperCAmelCase			       =		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:
               UpperCAmelCase			       =		False
               for each_need in need_list:
                   UpperCAmelCase			       =		True
                   for index, need in enumerate(SCREAMING_SNAKE_CASE__				):
                       if need > available_resources[index]:
                           UpperCAmelCase			       =		False
                           break
                   if execution:
                       UpperCAmelCase			       =		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:
                               UpperCAmelCase			       =		original_need_index
                       print(f"Process {process_number + 1} is executing."				)
                       # remove the process run from stack
                       need_list.remove(SCREAMING_SNAKE_CASE__				)
                       # update available/freed resources stack
                       UpperCAmelCase			       =		np.array(SCREAMING_SNAKE_CASE__				) + np.array(
                           alloc_resources_table[process_number]				)
                       print(
                           '''Updated available resource stack for processes: '''
                           + ''' '''.join([str(SCREAMING_SNAKE_CASE__				) 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     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """encoder.version""",
     """decoder.version""",
     """model.encoder.version""",
     """model.decoder.version""",
     """decoder.output_projection.weight""",
     """_float_tensor""",
     """encoder.embed_positions._float_tensor""",
     """decoder.embed_positions._float_tensor""",
 ]
 for k in ignore_keys:
  state_dict.pop(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						emb.weight.shape
 __lowerCAmelCase											=						nn.Linear(snake_case_						,		snake_case_						,		bias=snake_case_      )
 __lowerCAmelCase											=						emb.weight.data
 return lin_layer







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowerCAmelCase											=						mam_aaa["""model"""]
 remove_ignore_keys_(snake_case_      )
 __lowerCAmelCase											=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase											=						MaMaaaConfig(
     vocab_size=snake_case_						,		max_position_embeddings=10_24						,		encoder_layers=args.encoder_layers						,		decoder_layers=args.decoder_layers						,		encoder_attention_heads=args.encoder_attention_heads						,		decoder_attention_heads=args.decoder_attention_heads						,		encoder_ffn_dim=args.encoder_ffn_embed_dim						,		decoder_ffn_dim=args.decoder_ffn_embed_dim						,		d_model=args.encoder_embed_dim						,		encoder_layerdrop=args.encoder_layerdrop						,		decoder_layerdrop=args.decoder_layerdrop						,		dropout=args.dropout						,		attention_dropout=args.attention_dropout						,		activation_dropout=args.activation_dropout						,		activation_function="""relu"""						,		)

 __lowerCAmelCase											=						state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase											=						MaMaaaForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 __lowerCAmelCase											=						make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
 _A			:      Union[str, Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
 parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 _A			:      str					      =				parser.parse_args()
 _A			:      Optional[int]					      =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






