





from __future__ import annotations

import time

import numpy as np

__A							:			Any         =			[8, 5, 9, 7]
__A							:			Tuple         =			[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__A							:			Optional[Any]         =			[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         _A     =							claim_vector
         _A     =							allocated_resources_table
         _A     =							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(snake_case_							)							)
             for i, allocated_resource in enumerate(self.__allocated_resources_table							)
         ]




     def        lowerCAmelCase__		(    self							):
         return {self.__need().index(snake_case_							): i for i in self.__need()}




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         _A     =							self.__need()
         _A     =							self.__allocated_resources_table
         _A     =							self.__available_resources()
         _A     =							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:
             _A     =							False
             for each_need in need_list:
                 _A     =							True
                 for index, need in enumerate(snake_case_							):
                     if need > available_resources[index]:
                         _A     =							False
                         break
                 if execution:
                     _A     =							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:
                             _A     =							original_need_index
                     print(F"Process {process_number + 1} is executing."							)
                     # remove the process run from stack
                     need_list.remove(snake_case_							)
                     # update available/freed resources stack
                     _A     =							np.array(snake_case_							) + np.array(
                         alloc_resources_table[process_number]							)
                     print(
                         'Updated available resource stack for processes: '
                         + ' '.join([str(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        lowerCAmelCase__		(    self							):
         print(' ' * 9 + 'Allocated Resource Table'							)
         for item in self.__allocated_resources_table:
             print(
                 F"P{self.__allocated_resources_table.index(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(snake_case_							) + 1}"
                 + ' '.join(F"{it:>8}" for it in item							)
                 + '\n'							)
         print(
             'Current Usage by Active Processes: '
             + ' '.join(str(snake_case_							) for x in self.__claim_vector							)							)
         print(
             'Initial Available Resources:       '
             + ' '.join(str(snake_case_							) for x in self.__available_resources()							)							)
         time.sleep(1							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()












import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad							,     model.parameters()				)
    _A     =							sum([np.prod(p.size()				) for p in model_parameters]				)
    return params


__A							:			Union[str, Any]         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    if metric == "rouge2":
        _A     =							'{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _A     =							'{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _A     =							'{val_avg_em:.4f}-{step_count}'
    elif metric == "loss":
        _A     =							'{val_avg_loss:.4f}-{step_count}'
    else:
        raise NotImplementedError(
            F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
            ' function.'				)

    _A     =							ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE							,     filename=_SCREAMING_SNAKE_CASE							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

    return checkpoint_callback





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    return EarlyStopping(
        monitor=F"val_{metric}"							,     mode='min' if 'loss' in metric else 'max'							,     patience=_SCREAMING_SNAKE_CASE							,     verbose=_SCREAMING_SNAKE_CASE							,     )







class      lowerCamelCase(						pl.Callback	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							{F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
         pl_module.logger.log_metrics(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=True							):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"							)
         _A     =							trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
         # Log results
         _A     =							Path(pl_module.hparams.output_dir							)
         if type_path == "test":
             _A     =							od / 'test_results.txt'
             _A     =							od / 'test_generations.txt'
         else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             _A     =							od / F"{type_path}_results/{trainer.global_step:05d}.txt"
             _A     =							od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
             results_file.parent.mkdir(exist_ok=snake_case_							)
             generations_file.parent.mkdir(exist_ok=snake_case_							)
         with open(snake_case_				,     'a+'							) as writer:
             for key in sorted(snake_case_							):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 _A     =							metrics[key]
                 if isinstance(snake_case_				,     torch.Tensor							):
                     _A     =							val.item()
                 _A     =							F"{key}: {val:.6f}\n"
                 writer.write(snake_case_							)

         if not save_generations:
             return

         if "preds" in metrics:
             _A     =							'\n'.join(metrics['preds']							)
             generations_file.open('w+'							).write(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         try:
             _A     =							pl_module.model.model.num_parameters()
         except AttributeError:
             _A     =							pl_module.model.num_parameters()

         _A     =							count_trainable_parameters(snake_case_							)
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         return self._write_logs(snake_case_				,     snake_case_				,     'test'							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





