

from __future__ import annotations

import time

import numpy as np

_lowerCAmelCase  :      List[Any]			 =[8, 5, 9, 7]
_lowerCAmelCase  :      Optional[Any]			 =[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
_lowerCAmelCase  :      List[str]			 =[
    [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				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						UpperCAmelCase__:	Tuple									= claim_vector
						UpperCAmelCase__:	Optional[int]									= allocated_resources_table
						UpperCAmelCase__:	Union[str, Any]									= maximum_claim_table





					def    _UpperCAmelCase    (				self						):
						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table						)
						    for i in range(len(self.__allocated_resources_table[0]						)						)
						]





					def    _UpperCAmelCase    (				self						):
						return np.array(self.__claim_vector						) - np.array(
						    self.__processes_resource_summation()						)





					def    _UpperCAmelCase    (				self						):
						return [
						    list(np.array(self.__maximum_claim_table[i]						) - np.array(lowerCamelCase__						)						)
						    for i, allocated_resource in enumerate(self.__allocated_resources_table						)
						]





					def    _UpperCAmelCase    (				self						):
						return {self.__need().index(lowerCamelCase__						): i for i in self.__need()}





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						UpperCAmelCase__:	Any									= self.__need()
						UpperCAmelCase__:	List[Any]									= self.__allocated_resources_table
						UpperCAmelCase__:	Optional[int]									= self.__available_resources()
						UpperCAmelCase__:	Dict									= 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__:	Tuple									= False
							for each_need in need_list:
								UpperCAmelCase__:	Optional[Any]									= True
								for index, need in enumerate(lowerCamelCase__						):
									if need > available_resources[index]:
										UpperCAmelCase__:	Tuple									= False
										break
								if execution:
									UpperCAmelCase__:	Optional[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[int]									= original_need_index
									print(F"Process {process_number + 1} is executing."						)
									# remove the process run from stack
									need_list.remove(lowerCamelCase__						)
									# update available/freed resources stack
									UpperCAmelCase__:	Optional[Any]									= np.array(lowerCamelCase__						) + np.array(
									    alloc_resources_table[process_number]						)
									print(
									    "Updated available resource stack for processes: "
									    + " ".join([str(lowerCamelCase__						) 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    _UpperCAmelCase    (				self						):
						print(" " * 9 + "Allocated Resource Table"						)
						for item in self.__allocated_resources_table:
							print(
							    F"P{self.__allocated_resources_table.index(lowerCamelCase__						) + 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(lowerCamelCase__						) + 1}"
							    + " ".join(F"{it:>8}" for it in item						)
							    + "\n"						)
						print(
						    "Current Usage by Active Processes: "
						    + " ".join(str(lowerCamelCase__						) for x in self.__claim_vector						)						)
						print(
						    "Initial Available Resources:       "
						    + " ".join(str(lowerCamelCase__						) for x in self.__available_resources()						)						)
						time.sleep(1						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= torch.cat([classifier_free_guidance_embeddings, image_embeddings]				,      dim=0						)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						UpperCAmelCase__:	str									= prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings