




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     __lowerCAmelCase :

			"""simple docstring"""







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



							'''simple docstring'''
							__lowerCamelCase		       =    claim_vector
							__lowerCamelCase		       =    allocated_resources_table
							__lowerCamelCase		       =    maximum_claim_table


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



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


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



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


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



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


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      None:



							'''simple docstring'''
							__lowerCamelCase		       =    self.__need()
							__lowerCamelCase		       =    self.__allocated_resources_table
							__lowerCamelCase		       =    self.__available_resources()
							__lowerCamelCase		       =    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:
											__lowerCamelCase		       =    False
											for each_need in need_list:
															__lowerCamelCase		       =    True
															for index, need in enumerate(lowerCamelCase__							):
																			if need > available_resources[index]:
																							__lowerCamelCase		       =    False
																							break
															if execution:
																			__lowerCamelCase		       =    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:
																											__lowerCamelCase		       =    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
																			__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							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 argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__A        =    data_utils.TransfoXLTokenizer
__A        =    data_utils.TransfoXLCorpus
__A        =    data_utils
__A        =    data_utils







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(UpperCamelCase__ ,     'rb'      ) as fp:
												__lowerCamelCase		       =    pickle.load(UpperCamelCase__ ,     encoding='latin1'      )
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
								print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""      )
								__lowerCamelCase		       =    corpus.vocab.__dict__
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

								__lowerCamelCase		       =    corpus.__dict__
								corpus_dict_no_vocab.pop('vocab' ,     UpperCamelCase__      )
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + CORPUS_NAME
								print(F"""Save dataset to {pytorch_dataset_dump_path}"""      )
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )

								print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												__lowerCamelCase		       =    TransfoXLConfig()
								else:
												__lowerCamelCase		       =    TransfoXLConfig.from_json_file(UpperCamelCase__      )
								print(F"""Building PyTorch model from configuration: {config}"""      )
								__lowerCamelCase		       =    TransfoXLLMHeadModel(UpperCamelCase__      )

								__lowerCamelCase		       =    load_tf_weights_in_transfo_xl(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								# Save pytorch-model
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""Save PyTorch model to {os.path.abspath(UpperCamelCase__      )}"""      )
								torch.save(model.state_dict() ,     UpperCamelCase__      )
								print(F"""Save configuration file to {os.path.abspath(UpperCamelCase__      )}"""      )
								with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8'      ) as f:
												f.write(config.to_json_string()      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the folder to store the PyTorch model or dataset/vocab.",
					)
					parser.add_argument(
					    "--tf_checkpoint_path",
					    default="",
					    type=str,
					    help="An optional path to a TensorFlow checkpoint path to be converted.",
					)
					parser.add_argument(
					    "--transfo_xl_config_file",
					    default="",
					    type=str,
					    help=(
					        "An optional config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--transfo_xl_dataset_file",
					    default="",
					    type=str,
					    help="An optional dataset file to be converted in a vocabulary.",
					)
					__A        =    parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)


