
import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm
				import torch_xla.debug.metrics as met







class 				UpperCAmelCase						( __lowerCamelCase       ):

					def __init__(      self   :       Union[str, Any]			,   *lowerCAmelCase   :       Any			,   lowerCAmelCase   :       Union[str, Any]=None			,   lowerCAmelCase   :       List[str]=None			,   **lowerCAmelCase   :       Any       ):
											super().__init__(*lowerCAmelCase			,   **lowerCAmelCase       )
											lowercase							:     Any									=	eval_examples
											lowercase							:     Optional[int]									=	post_process_function

					def 							_lowerCAmelCase						(      self   :       Dict			,   lowerCAmelCase   :       str = None			,   lowerCAmelCase   :       List[Any]=None			,   lowerCAmelCase   :       Any = None			,   lowerCAmelCase   :       str = "eval"			,   **lowerCAmelCase   :       Dict			,   ):
											lowercase							:     Tuple									=	gen_kwargs.copy()
											lowercase							:     int									=	(
											    gen_kwargs['max_length'] if gen_kwargs.get('''max_length'''       ) is not None else self.args.generation_max_length
											)
											lowercase							:     Dict									=	(
											    gen_kwargs['num_beams'] if gen_kwargs.get('''num_beams'''       ) is not None else self.args.generation_num_beams
											)
											lowercase							:     Dict									=	gen_kwargs

											lowercase							:     Tuple									=	self.eval_dataset if eval_dataset is None else eval_dataset
											lowercase							:     Union[str, Any]									=	self.get_eval_dataloader(lowerCAmelCase       )
											lowercase							:     Tuple									=	self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											lowercase							:     Dict									=	self.compute_metrics
											lowercase							:     Optional[Any]									=	None
											lowercase							:     str									=	time.time()
											lowercase							:     int									=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																	lowercase							:     str									=	eval_loop(
																	    lowerCAmelCase			,   description='''Evaluation'''			,   prediction_loss_only=True if compute_metrics is None else None			,   ignore_keys=lowerCAmelCase			,   metric_key_prefix=lowerCAmelCase			,   )
											finally:
																	lowercase							:     str									=	compute_metrics
											lowercase							:     Optional[int]									=	self.args.eval_batch_size * self.args.world_size
											if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																	start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        lowerCAmelCase			,   lowerCAmelCase			,   num_samples=output.num_samples			,   num_steps=math.ceil(output.num_samples / total_batch_size       )			,   )       )

											if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																	# Only the main node write the results by default
																	lowercase							:     Tuple									=	self.post_process_function(lowerCAmelCase			,   lowerCAmelCase			,   lowerCAmelCase       )
																	lowercase							:     Union[str, Any]									=	self.compute_metrics(lowerCAmelCase       )

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()       ):
																							if not key.startswith(f'''{metric_key_prefix}_'''       ):
																													lowercase							:     Any									=	metrics.pop(lowerCAmelCase       )

																	metrics.update(output.metrics       )
											else:
																	lowercase							:     str									=	output.metrics

											if self.args.should_log:
																	# Only the main node log the results by default
																	self.log(lowerCAmelCase       )

											if self.args.tpu_metrics_debug or self.args.debug:
																	# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																	xm.master_print(met.metrics_report()       )

											lowercase							:     Any									=	self.callback_handler.on_evaluate(self.args			,   self.state			,   self.control			,   lowerCAmelCase       )
											return metrics




					def 							_lowerCAmelCase						(      self   :       Optional[Any]			,   lowerCAmelCase   :       Optional[Any]			,   lowerCAmelCase   :       Dict			,   lowerCAmelCase   :       Tuple=None			,   lowerCAmelCase   :       Tuple = "test"			,   **lowerCAmelCase   :       Optional[Any]       ):
											lowercase							:     Optional[int]									=	gen_kwargs.copy()

											lowercase							:     int									=	self.get_test_dataloader(lowerCAmelCase       )

											# Temporarily disable metric computation, we will do it in the loop here.
											lowercase							:     str									=	self.compute_metrics
											lowercase							:     Union[str, Any]									=	None
											lowercase							:     Union[str, Any]									=	time.time()
											lowercase							:     Tuple									=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																	lowercase							:     Any									=	eval_loop(
																	    lowerCAmelCase			,   description='''Prediction'''			,   prediction_loss_only=True if compute_metrics is None else None			,   ignore_keys=lowerCAmelCase			,   metric_key_prefix=lowerCAmelCase			,   )
											finally:
																	lowercase							:     Any									=	compute_metrics

											lowercase							:     Dict									=	self.args.eval_batch_size * self.args.world_size
											if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																	start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        lowerCAmelCase			,   lowerCAmelCase			,   num_samples=output.num_samples			,   num_steps=math.ceil(output.num_samples / total_batch_size       )			,   )       )
											if self.post_process_function is None or self.compute_metrics is None:
																	return output

											lowercase							:     Tuple									=	self.post_process_function(lowerCAmelCase			,   lowerCAmelCase			,   lowerCAmelCase			,   '''predict'''       )
											lowercase							:     Union[str, Any]									=	self.compute_metrics(lowerCAmelCase       )

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()       ):
																	if not key.startswith(f'''{metric_key_prefix}_'''       ):
																							lowercase							:     Any									=	metrics.pop(lowerCAmelCase       )
											metrics.update(output.metrics       )
											return PredictionOutput(predictions=predictions.predictions			,   label_ids=predictions.label_ids			,   metrics=lowerCAmelCase       )






'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

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






class    __magic_name__				:






       def __init__(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =claim_vector
          _UpperCAmelCase  :  Optional[int]				       =allocated_resources_table
          _UpperCAmelCase  :  int				       =maximum_claim_table






       def      lowerCAmelCase						(     self)		->    list[int]:




          '''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      lowerCAmelCase						(     self)		->    list[int]:




          '''simple docstring'''
          return np.array(self.__claim_vector) - np.array(
              self.__processes_resource_summation())






       def      lowerCAmelCase						(     self)		->    list[list[int]]:




          '''simple docstring'''
          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)		->    dict[int, list[int]]:




          '''simple docstring'''
          return {self.__need().index(snake_case): i for i in self.__need()}






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =self.__need()
          _UpperCAmelCase  :  str				       =self.__allocated_resources_table
          _UpperCAmelCase  :  Dict				       =self.__available_resources()
          _UpperCAmelCase  :  Tuple				       =self.__need_index_manager()
          for kw, val in kwargs.items():
             if kw and val is True:
                self.__pretty_data()
          print('_' * 5_0 + '\n')
          while need_list:
             _UpperCAmelCase  :  Any				       =False
             for each_need in need_list:
                _UpperCAmelCase  :  Any				       =True
                for index, need in enumerate(snake_case):
                   if need > available_resources[index]:
                      _UpperCAmelCase  :  Optional[int]				       =False
                      break
                if execution:
                   _UpperCAmelCase  :  Union[str, Any]				       =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  :  List[str]				       =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
                   _UpperCAmelCase  :  Any				       =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)		->    Dict:




          '''simple docstring'''
          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()

