



"""simple docstring"""







from __future__ import annotations

import time

import numpy as np

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






class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	)      ->	None:
				A__											=					claim_vector
				A__											=					allocated_resources_table
				A__											=					maximum_claim_table


			def   snake_case__	(      self		)      ->	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   snake_case__	(      self		)      ->	list[int]:
				return np.array(self.__claim_vector		) - np.array(
				    self.__processes_resource_summation()		)


			def   snake_case__	(      self		)      ->	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   snake_case__	(      self		)      ->	dict[int, list[int]]:
				return {self.__need().index(SCREAMING_SNAKE_CASE__		): i for i in self.__need()}


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	None:
				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(SCREAMING_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(SCREAMING_SNAKE_CASE__		)
							# update available/freed resources stack
							A__											=					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   snake_case__	(      self		)      ->	List[Any]:
				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()












from typing import Dict

from .base import GenericTensor, Pipeline
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       Union[str, Any]=None,    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								if tokenize_kwargs is None:
												lowercase__  					=     {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''    )
												lowercase__  					=     truncation

								lowercase__  					=     tokenize_kwargs

								lowercase__  					=     {}
								if return_tensors is not None:
												lowercase__  					=     return_tensors

								return preprocess_params, {}, postprocess_params




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       Optional[int],    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.framework
								lowercase__  					=     self.tokenizer(lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase    )
								return model_inputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     self.model(**lowerCamelCase    )
								return model_outputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Tuple=False    ):

								'''simple docstring'''
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(	self		:       List[str],    *lowerCamelCase		:       List[Any],    **lowerCamelCase		:       str    ):

								'''simple docstring'''
								return super().__call__(*lowerCamelCase,    **lowerCamelCase    )




