




"""simple docstring"""





from __future__ import annotations

import time

import numpy as np

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



class   a__					:


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     list[int]   ,	UpperCamelCase_   :     list[list[int]]   ,	UpperCamelCase_   :     list[list[int]]   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       claim_vector
						__UpperCAmelCase						:			Optional[Any]												=       allocated_resources_table
						__UpperCAmelCase						:			Optional[int]												=       maximum_claim_table


			def    a_  (   self   :     Tuple):


						"""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    a_  (   self   :     Dict):


						"""simple docstring"""




						return np.array(self.__claim_vector) - np.array(
						    self.__processes_resource_summation())


			def    a_  (   self   :     int):


						"""simple docstring"""




						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(UpperCamelCase_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						return {self.__need().index(UpperCamelCase_): i for i in self.__need()}


			def    a_  (   self   :     List[Any]   ,	**UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.__need()
						__UpperCAmelCase						:			Tuple												=       self.__allocated_resources_table
						__UpperCAmelCase						:			str												=       self.__available_resources()
						__UpperCAmelCase						:			Any												=       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						:			List[str]												=       False
									for each_need in need_list:
												__UpperCAmelCase						:			Tuple												=       True
												for index, need in enumerate(UpperCamelCase_):
															if need > available_resources[index]:
																		__UpperCAmelCase						:			Optional[Any]												=       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						:			Optional[Any]												=       original_need_index
															print(F"Process {process_number + 1} is executing.")
															# remove the process run from stack
															need_list.remove(UpperCamelCase_)
															# update available/freed resources stack
															__UpperCAmelCase						:			Tuple												=       np.array(UpperCamelCase_) + np.array(
															    alloc_resources_table[process_number])
															print(
															    "Updated available resource stack for processes: "
															    + " ".join([str(UpperCamelCase_) 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   :     List[str]):


						"""simple docstring"""




						print(" " * 9 + "Allocated Resource Table")
						for item in self.__allocated_resources_table:
									print(
									    F"P{self.__allocated_resources_table.index(UpperCamelCase_) + 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(UpperCamelCase_) + 1}"
									    + " ".join(F"{it:>8}" for it in item)
									    + "\n")
						print(
						    "Current Usage by Active Processes: "
						    + " ".join(str(UpperCamelCase_) for x in self.__claim_vector))
						print(
						    "Initial Available Resources:       "
						    + " ".join(str(UpperCamelCase_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()









import tensorflow as tf

from ...tf_utils import shape_list
class     UpperCamelCase__					(     tf.keras.layers.Layer				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__=1,   snake_case__=False,   **snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									super().__init__(**snake_case__  )

									lowercase_	:   Tuple  				=     vocab_size
									lowercase_	:   Union[str, Any]  				=     d_embed
									lowercase_	:   Optional[Any]  				=     d_proj

									lowercase_	:   Optional[Any]  				=     cutoffs + [vocab_size]
									lowercase_	:   Optional[int]  				=     [0] + self.cutoffs
									lowercase_	:   Union[str, Any]  				=     div_val

									lowercase_	:   Union[str, Any]  				=     self.cutoffs[0]
									lowercase_	:   List[str]  				=     len(self.cutoffs  ) - 1
									lowercase_	:   List[str]  				=     self.shortlist_size + self.n_clusters
									lowercase_	:   Tuple  				=     keep_order

									lowercase_	:   Dict  				=     []
									lowercase_	:   int  				=     []






		def 						snake_case__				(							self,   snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									if self.n_clusters > 0:
																lowercase_	:   List[str]  				=     self.add_weight(
																    shape=(self.n_clusters, self.d_embed),   initializer="""zeros""",   trainable=snake_case__,   name="""cluster_weight"""  )
																lowercase_	:   List[Any]  				=     self.add_weight(
																    shape=(self.n_clusters,),   initializer="""zeros""",   trainable=snake_case__,   name="""cluster_bias"""  )

									if self.div_val == 1:
																for i in range(len(self.cutoffs  )  ):
																							if self.d_proj != self.d_embed:
																														lowercase_	:   Any  				=     self.add_weight(
																														    shape=(self.d_embed, self.d_proj),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_projs_._{i}""",   )
																														self.out_projs.append(snake_case__  )
																							else:
																														self.out_projs.append(snake_case__  )
																							lowercase_	:   List[str]  				=     self.add_weight(
																							    shape=(self.vocab_size, self.d_embed),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._weight""",   )
																							lowercase_	:   List[str]  				=     self.add_weight(
																							    shape=(self.vocab_size,),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._bias""",   )
																							self.out_layers.append((weight, bias)  )
									else:
																for i in range(len(self.cutoffs  )  ):
																							lowercase_				,		lowercase_	:   Optional[Any]  				=     self.cutoff_ends[i], self.cutoff_ends[i + 1]
																							lowercase_	:   Union[str, Any]  				=     self.d_embed // (self.div_val**i)

																							lowercase_	:   int  				=     self.add_weight(
																							    shape=(d_emb_i, self.d_proj),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_projs_._{i}"""  )
																							self.out_projs.append(snake_case__  )
																							lowercase_	:   Any  				=     self.add_weight(
																							    shape=(r_idx - l_idx, d_emb_i),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._weight""",   )
																							lowercase_	:   Tuple  				=     self.add_weight(
																							    shape=(r_idx - l_idx,),   initializer="""zeros""",   trainable=snake_case__,   name=f"""out_layers_._{i}_._bias""",   )
																							self.out_layers.append((weight, bias)  )
									super().build(snake_case__  )






		@staticmethod
		def 						snake_case__				(							snake_case__,   snake_case__,   snake_case__,   snake_case__=None  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   Dict  				=     x
									if proj is not None:
																lowercase_	:   List[Any]  				=     tf.einsum("""ibd,ed->ibe""",   snake_case__,   snake_case__  )
									return tf.einsum("""ibd,nd->ibn""",   snake_case__,   snake_case__  ) + b






		@staticmethod
		def 						snake_case__				(							snake_case__,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     shape_list(snake_case__  )
									lowercase_	:   Optional[Any]  				=     tf.range(lp_size[0],   dtype=target.dtype  )
									lowercase_	:   str  				=     tf.stack([r, target],   1  )
									return tf.gather_nd(snake_case__,   snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__=True,   snake_case__=False  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Any  				=     0
									if self.n_clusters == 0:
																lowercase_	:   Union[str, Any]  				=     self._logit(snake_case__,   self.out_layers[0][0],   self.out_layers[0][1],   self.out_projs[0]  )
																if target is not None:
																							lowercase_	:   str  				=     tf.nn.sparse_softmax_cross_entropy_with_logits(labels=snake_case__,   logits=snake_case__  )
																lowercase_	:   Optional[int]  				=     tf.nn.log_softmax(snake_case__,   axis=-1  )
									else:
																lowercase_	:   int  				=     shape_list(snake_case__  )
																lowercase_	:   List[Any]  				=     []
																lowercase_	:   Tuple  				=     tf.zeros(hidden_sizes[:2]  )
																for i in range(len(self.cutoffs  )  ):
																							lowercase_				,		lowercase_	:   Optional[int]  				=     self.cutoff_ends[i], self.cutoff_ends[i + 1]
																							if target is not None:
																														lowercase_	:   Tuple  				=     (target >= l_idx) & (target < r_idx)
																														lowercase_	:   Union[str, Any]  				=     tf.where(snake_case__  )
																														lowercase_	:   Optional[Any]  				=     tf.boolean_mask(snake_case__,   snake_case__  ) - l_idx

																							if self.div_val == 1:
																														lowercase_	:   Optional[Any]  				=     self.out_layers[0][0][l_idx:r_idx]
																														lowercase_	:   Union[str, Any]  				=     self.out_layers[0][1][l_idx:r_idx]
																							else:
																														lowercase_	:   Dict  				=     self.out_layers[i][0]
																														lowercase_	:   int  				=     self.out_layers[i][1]

																							if i == 0:
																														lowercase_	:   Optional[int]  				=     tf.concat([cur_W, self.cluster_weight],   0  )
																														lowercase_	:   Optional[Any]  				=     tf.concat([cur_b, self.cluster_bias],   0  )

																														lowercase_	:   List[str]  				=     self._logit(snake_case__,   snake_case__,   snake_case__,   self.out_projs[0]  )
																														lowercase_	:   List[str]  				=     tf.nn.log_softmax(snake_case__  )
																														out.append(head_logprob[..., : self.cutoffs[0]]  )
																														if target is not None:
																																					lowercase_	:   int  				=     tf.boolean_mask(snake_case__,   snake_case__  )
																																					lowercase_	:   Optional[int]  				=     self._gather_logprob(snake_case__,   snake_case__  )
																							else:
																														lowercase_	:   List[str]  				=     self._logit(snake_case__,   snake_case__,   snake_case__,   self.out_projs[i]  )
																														lowercase_	:   Dict  				=     tf.nn.log_softmax(snake_case__  )
																														lowercase_	:   Optional[int]  				=     self.cutoffs[0] + i - 1  # No probability for the head cluster
																														lowercase_	:   int  				=     head_logprob[..., cluster_prob_idx, None] + tail_logprob
																														out.append(snake_case__  )
																														if target is not None:
																																					lowercase_	:   Optional[Any]  				=     tf.boolean_mask(snake_case__,   snake_case__  )
																																					lowercase_	:   Optional[int]  				=     tf.boolean_mask(snake_case__,   snake_case__  )
																																					lowercase_	:   Union[str, Any]  				=     self._gather_logprob(snake_case__,   snake_case__  )
																																					cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																							if target is not None:
																														loss += tf.scatter_nd(snake_case__,   -cur_logprob,   shape_list(snake_case__  )  )
																lowercase_	:   List[str]  				=     tf.concat(snake_case__,   axis=-1  )

									if target is not None:
																if return_mean:
																							lowercase_	:   Tuple  				=     tf.reduce_mean(snake_case__  )
																# Add the training-time loss value to the layer using `self.add_loss()`.
																self.add_loss(snake_case__  )

																# Log the loss as a metric (we could log arbitrary metrics,
																# including different metrics for training and inference.
																self.add_metric(snake_case__,   name=self.name,   aggregation="""mean""" if return_mean else """"""  )

									return out