

from __future__ import annotations

import time

import numpy as np

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






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> None:




						'''simple docstring'''
						__a        =claim_vector
						__a        =allocated_resources_table
						__a        =maximum_claim_table





			def 		__magic_name__    (		self					)   -> list[int]:




						'''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 		__magic_name__    (		self					)   -> list[int]:




						'''simple docstring'''
						return np.array(self.__claim_vector					) - np.array(
						    self.__processes_resource_summation()					)





			def 		__magic_name__    (		self					)   -> list[list[int]]:




						'''simple docstring'''
						return [
						    list(np.array(self.__maximum_claim_table[i]					) - np.array(__snake_case					)					)
						    for i, allocated_resource in enumerate(self.__allocated_resources_table					)
						]





			def 		__magic_name__    (		self					)   -> dict[int, list[int]]:




						'''simple docstring'''
						return {self.__need().index(__snake_case					): i for i in self.__need()}





			def 		__magic_name__    (		self					,  **__snake_case					)   -> None:




						'''simple docstring'''
						__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(__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(__snake_case					)
															# update available/freed resources stack
															__a        =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 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						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()









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    'decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

			__a        ={key.replace('decoder'       ,     'model'	): val for key, val in state_dict.items()}

			__a        =XGLMConfig(
			    vocab_size=_snake_case       ,     max_position_embeddings=args.max_target_positions       ,     num_layers=args.decoder_layers       ,     attention_heads=args.decoder_attention_heads       ,     ffn_dim=args.decoder_ffn_embed_dim       ,     d_model=args.decoder_embed_dim       ,     layerdrop=args.decoder_layerdrop       ,     dropout=args.dropout       ,     attention_dropout=args.attention_dropout       ,     activation_dropout=args.activation_dropout       ,     activation_function='gelu'       ,     scale_embedding=not args.no_scale_embedding       ,     tie_word_embeddings=args.share_decoder_input_output_embed       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			str  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
						parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						_lowerCAmelCase					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






