




"""simple docstring"""

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a         =      logging.get_logger(__name__)

a         =      '''▁'''

a         =      {'''vocab_file''': '''sentencepiece.bpe.model'''}

a         =      {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

a         =      {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_024,
}

# fmt: off
a         =      ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[str]				  =     VOCAB_FILES_NAMES
 UpperCAmelCase   :					Optional[Any]				  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase   :					Union[str, Any]				  =     PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase   :					str				  =     ['''input_ids''', '''attention_mask''']

 UpperCAmelCase   :					List[int]				  =     []
 UpperCAmelCase   :					List[int]				  =     []

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict=None   ,							_UpperCAmelCase	:	Optional[Any]=None   ,							_UpperCAmelCase	:	List[Any]="</s>"   ,							_UpperCAmelCase	:	Optional[Any]="</s>"   ,							_UpperCAmelCase	:	Dict="<s>"   ,							_UpperCAmelCase	:	List[Any]="<unk>"   ,							_UpperCAmelCase	:	Any="<pad>"   ,							_UpperCAmelCase	:	Union[str, Any]="<mask>"   ,							_UpperCAmelCase	:	Optional[Dict[str, Any]] = None   ,							**_UpperCAmelCase	:	int   ,							):
       # Mask token behave like a normal word, i.e. include the space before it
       _A        =       AddedToken(_UpperCAmelCase   ,							lstrip=_UpperCAmelCase   ,							rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) else mask_token

       _A        =       {} if sp_model_kwargs is None else sp_model_kwargs

       _A        =       kwargs.get('additional_special_tokens'   ,							[]		)
       kwargs["additional_special_tokens"] += [
           code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           src_lang=_UpperCAmelCase   ,							tgt_lang=_UpperCAmelCase   ,							eos_token=_UpperCAmelCase   ,							unk_token=_UpperCAmelCase   ,							sep_token=_UpperCAmelCase   ,							cls_token=_UpperCAmelCase   ,							pad_token=_UpperCAmelCase   ,							mask_token=_UpperCAmelCase   ,							sp_model_kwargs=self.sp_model_kwargs   ,							**_UpperCAmelCase   ,							)

       _A        =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(str(_UpperCAmelCase		)		)
       _A        =       vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       _A        =       {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       _A        =       1

       _A        =       len(self.sp_model		)
       _A        =       {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCAmelCase		)
       }
       _A        =       {v: k for k, v in self.lang_code_to_id.items()}
       _A        =       len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
       _A        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

       _A        =       src_lang if src_lang is not None else 'en_XX'
       _A        =       self.lang_code_to_id[self._src_lang]
       _A        =       tgt_lang
       self.set_src_lang_special_tokens(self._src_lang		)

 @property
 def       lowerCAmelCase_							(			self	:	int		):
       return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token

 @property
 def       lowerCAmelCase_							(			self	:	Tuple		):
       return self._src_lang

 @src_lang.setter
 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str		):
       _A        =       new_src_lang
       self.set_src_lang_special_tokens(self._src_lang		)

 def __getstate__(			self	:	Optional[int]		):
       _A        =       self.__dict__.copy()
       _A        =       None
       return state

 def __setstate__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Dict		):
       _A        =       d

       # for backward compatibility
       if not hasattr(self   ,							'sp_model_kwargs'		):
             _A        =       {}

       _A        =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(self.vocab_file		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       {self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
       vocab.update(self.added_tokens_encoder		)
       return vocab

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str		):
       return self.sp_model.encode(_UpperCAmelCase   ,							out_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	str		):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       _A        =       self.sp_model.PieceToId(_UpperCAmelCase		)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	List[Any]		):
       _A        =       []
       _A        =       ''
       _A        =       False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(_UpperCAmelCase		) + token
                   _A        =       True
                   _A        =       []
             else:
                   current_sub_tokens.append(_UpperCAmelCase		)
                   _A        =       False
       out_string += self.sp_model.decode(_UpperCAmelCase		)
       return out_string.strip()

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[str] = None		):
       if not os.path.isdir(_UpperCAmelCase		):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
             return
       _A        =       os.path.join(
           _UpperCAmelCase   ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCAmelCase		) and os.path.isfile(self.vocab_file		):
             copyfile(self.vocab_file   ,							_UpperCAmelCase		)
       elif not os.path.isfile(self.vocab_file		):
             with open(_UpperCAmelCase   ,							'wb'		) as fi:
                   _A        =       self.sp_model.serialized_model_proto()
                   fi.write(_UpperCAmelCase		)

       return (out_vocab_file,)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None   ,							_UpperCAmelCase	:	bool = False		):

       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase   ,							token_ids_a=_UpperCAmelCase   ,							already_has_special_tokens=_UpperCAmelCase		)

       _A        =       [1] * len(self.prefix_tokens		)
       _A        =       [1] * len(self.suffix_tokens		)
       if token_ids_a is None:
             return prefix_ones + ([0] * len(_UpperCAmelCase		)) + suffix_ones
       return prefix_ones + ([0] * len(_UpperCAmelCase		)) + ([0] * len(_UpperCAmelCase		)) + suffix_ones

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None		):
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[str]   ,							_UpperCAmelCase	:	Optional[str]   ,							**_UpperCAmelCase	:	int		):
       if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
       _A        =       src_lang
       _A        =       self(_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase		)
       _A        =       self.convert_tokens_to_ids(_UpperCAmelCase		)
       _A        =       tgt_lang_id
       return inputs

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str = "en_XX"   ,							_UpperCAmelCase	:	Optional[List[str]] = None   ,							_UpperCAmelCase	:	str = "ro_RO"   ,							**_UpperCAmelCase	:	Optional[int]   ,							):
       _A        =       src_lang
       _A        =       tgt_lang
       return super().prepare_seqaseq_batch(_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int		):
       return self.set_src_lang_special_tokens(self.src_lang		)

 def       lowerCAmelCase_							(			self	:	int		):
       return self.set_tgt_lang_special_tokens(self.tgt_lang		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	str		):
       _A        =       self.lang_code_to_id[src_lang]
       _A        =       [self.cur_lang_code_id]
       _A        =       [self.eos_token_id]



 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	str		):
       _A        =       self.lang_code_to_id[tgt_lang]
       _A        =       [self.cur_lang_code_id]
       _A        =       [self.eos_token_id]







"""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     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	list[list[int]]   ,							_UpperCAmelCase	:	list[list[int]]   ,							):
       _A        =       claim_vector
       _A        =       allocated_resources_table
       _A        =       maximum_claim_table

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

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

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

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return {self.__need().index(_UpperCAmelCase		): i for i in self.__need()}

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	int		):
       _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(_UpperCAmelCase		):
                         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(_UpperCAmelCase		)
                         # update available/freed resources stack
                         _A        =       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       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       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()

