



'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

UpperCamelCase__:     Tuple										=    [8, 5, 9, 7]
UpperCamelCase__:     int										=    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCamelCase__:     List[str]										=    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  list[int]	,    __snake_case    :  list[list[int]]	,    __snake_case    :  list[list[int]]	,    )    ->	None:
								UpperCAmelCase				: Union[str, Any]											=	claim_vector
								UpperCAmelCase				: Tuple											=	allocated_resources_table
								UpperCAmelCase				: List[str]											=	maximum_claim_table







	def   A			(		self    :  Optional[Any]	)    ->	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    :  Union[str, Any]	)    ->	list[int]:
								return np.array(self.__claim_vector	) - np.array(
								    self.__processes_resource_summation()	)







	def   A			(		self    :  str	)    ->	list[list[int]]:
								return [
								    list(np.array(self.__maximum_claim_table[i]	) - np.array(__snake_case	)	)
								    for i, allocated_resource in enumerate(self.__allocated_resources_table	)
								]







	def   A			(		self    :  List[Any]	)    ->	dict[int, list[int]]:
								return {self.__need().index(__snake_case	): i for i in self.__need()}







	def   A			(		self    :  Tuple	,    **__snake_case    :  Union[str, Any]	)    ->	None:
								UpperCAmelCase				: Tuple											=	self.__need()
								UpperCAmelCase				: Union[str, Any]											=	self.__allocated_resources_table
								UpperCAmelCase				: int											=	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('''_''' * 50 + '''\n'''	)
								while need_list:
															UpperCAmelCase				: Dict											=	False
															for each_need in need_list:
																						UpperCAmelCase				: Optional[int]											=	True
																						for index, need in enumerate(__snake_case	):
																													if need > available_resources[index]:
																																				UpperCAmelCase				: List[str]											=	False
																																				break
																						if execution:
																													UpperCAmelCase				: str											=	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				: Union[str, Any]											=	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				: Optional[int]											=	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   A			(		self    :  Dict	)    ->	Optional[Any]:
								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()










'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





