





from __future__ import annotations

import time

import numpy as np

lowercase__						:      Optional[Any]	=    [8, 5, 9, 7]
lowercase__						:      Optional[int]	=    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase__						:      Any	=    [
    [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       :		List[str]	,			lowerCAmelCase__       :		list[int]	,			lowerCAmelCase__       :		list[list[int]]	,			lowerCAmelCase__       :		list[list[int]]	,			)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	claim_vector
            _UpperCamelCase       =	allocated_resources_table
            _UpperCamelCase       =	maximum_claim_table





       def snake_case__						(							self       :		Union[str, Any]					)   ->					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 snake_case__						(							self       :		List[Any]					)   ->					Any:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					List[Any]:

            '''simple docstring'''




            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       :		Optional[Any]					)   ->					str:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Union[str, Any]	,			**lowerCAmelCase__       :		Any					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	self.__need()
            _UpperCamelCase       =	self.__allocated_resources_table
            _UpperCamelCase       =	self.__available_resources()
            _UpperCamelCase       =	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       =	False
                 for each_need in need_list:
                      _UpperCamelCase       =	True
                      for index, need in enumerate(__SCREAMING_SNAKE_CASE					):
                           if need > available_resources[index]:
                                _UpperCamelCase       =	False
                                break
                      if execution:
                           _UpperCamelCase       =	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       =	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
                           _UpperCamelCase       =	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       :		Dict					)   ->					Any:

            '''simple docstring'''




            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()










'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)





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

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__					)	,			lowerCAmelCase__					)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__					)	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,			'''<s>NOTUSED'''					)
            self.assertEqual(vocab_keys[1]	,			'''<pad>'''					)
            self.assertEqual(vocab_keys[-1]	,			'''<mask>'''					)
            self.assertEqual(len(lowerCAmelCase__					)	,			1004					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size	,			1005					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





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

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            _UpperCamelCase       =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowerCAmelCase__	,			model_name='''camembert-base'''	,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''	,			sequences=lowerCAmelCase__	,			)



