'''simple docstring'''

from __future__ import annotations

import time

import numpy as np

_A			:      str					      =				[8, 5, 9, 7]
_A			:      Optional[int]					      =				[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
_A			:      int					      =				[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	list[int]      ,       SCREAMING_SNAKE_CASE__   :	list[list[int]]      ,       SCREAMING_SNAKE_CASE__   :	list[list[int]]      ,       )  ->    None:
  __lowerCAmelCase											=						claim_vector
  __lowerCAmelCase											=						allocated_resources_table
  __lowerCAmelCase											=						maximum_claim_table
 def a					(      self   :	List[str]							)  ->    list[int]:
  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							)  ->    list[int]:
  return np.array(self.__claim_vector							) - np.array(
      self.__processes_resource_summation()							)
 def a					(      self   :	int							)  ->    list[list[int]]:
  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]							)  ->    dict[int, list[int]]:
  return {self.__need().index(SCREAMING_SNAKE_CASE__							): i for i in self.__need()}
 def a					(      self   :	Dict      ,       **SCREAMING_SNAKE_CASE__   :	str							)  ->    None:
  __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(SCREAMING_SNAKE_CASE__							):
     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(SCREAMING_SNAKE_CASE__							)
     # update available/freed resources stack
     __lowerCAmelCase											=						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]							)  ->    Dict:
  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 numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]=2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=64      ,       SCREAMING_SNAKE_CASE__   :	int=None							)  ->    Optional[int]:
  __lowerCAmelCase											=						np.random.default_rng(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						length
  __lowerCAmelCase											=						rng.normal(size=(length,)							).astype(np.floataa							)
  __lowerCAmelCase											=						a * self.x + b + rng.normal(scale=0.1      ,       size=(length,)							).astype(np.floataa							)
 def __len__(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  return self.length





 def __getitem__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[int]:
  return {"x": self.x[i], "y": self.y[i]}







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=False							)  ->    Any:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None							)  ->    str:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a[0] + self.b[0]







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    Optional[Any]:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Any=None							)  ->    int:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a + self.b







def      UpperCamelCase_						(					snake_case_       :       List[str]						,		snake_case_       :       int = 16      )					->     int:






 '''simple docstring'''

 from datasets import load_dataset
 from transformers import AutoTokenizer

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 __lowerCAmelCase											=						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
 __lowerCAmelCase											=						load_dataset("""csv"""						,		data_files=snake_case_      )
 __lowerCAmelCase											=						datasets["""train"""].unique("""label"""      )

 __lowerCAmelCase											=						{v: i for i, v in enumerate(snake_case_      )}

 def tokenize_function(snake_case_       :       Optional[int]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						tokenizer(
      examples["""sentence1"""]						,		examples["""sentence2"""]						,		truncation=snake_case_						,		max_length=snake_case_						,		padding="""max_length"""      )
  if "label" in examples:
   __lowerCAmelCase											=						[label_to_id[l] for l in examples["""label"""]]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowerCAmelCase											=						datasets.map(
     snake_case_						,		batched=snake_case_						,		remove_columns=["""sentence1""", """sentence2""", """label"""]						,		)

 def collate_fn(snake_case_       :       List[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=2      )
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=1      )

 return train_dataloader, eval_dataloader






