


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


lowerCamelCase__							:						Dict							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Union[str, Any]							     =   """▁"""

lowerCamelCase__							:						Union[str, Any]							     =   {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCamelCase__							:						Union[str, Any]							     =   {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCamelCase__							:						List[Any]							     =   {
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
lowerCamelCase__							:						List[str]							     =   ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Union[str, Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							Optional[int]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowercase				:							Dict	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							Optional[Any]	   =       ['input_ids', 'attention_mask']

     __lowercase				:							List[int]	   =       []
     __lowercase				:							List[int]	   =       []

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

       snake_case__						= {} if sp_model_kwargs is None else sp_model_kwargs
       snake_case__						= legacy_behaviour

       super().__init__(
           bos_token=_a     ,		eos_token=_a     ,		unk_token=_a     ,		sep_token=_a     ,		cls_token=_a     ,		pad_token=_a     ,		mask_token=_a     ,		tokenizer_file=_a     ,		src_lang=_a     ,		tgt_lang=_a     ,		additional_special_tokens=_a     ,		sp_model_kwargs=self.sp_model_kwargs     ,		legacy_behaviour=_a     ,		**_a     ,		)

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(str(_a   )   )
       snake_case__						= 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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       snake_case__						= {'''<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
       snake_case__						= 1

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

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

       if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         self._additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in self._additional_special_tokens]   )

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

     def __getstate__(					self:Optional[Any]   ):
       snake_case__						= self.__dict__.copy()
       snake_case__						= None
       snake_case__						= self.sp_model.serialized_model_proto()
       return state

     def __setstate__(					self:int     ,		_a:List[Any]   ):
       snake_case__						= d

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

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )

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

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

     @src_lang.setter
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str   ):
       snake_case__						= new_src_lang
       self.set_src_lang_special_tokens(self._src_lang   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:List[int]     ,		_a: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 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]
       snake_case__						= [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep   ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]

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

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= {self.convert_ids_to_tokens(_a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[str]   ):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       snake_case__						= self.sp_model.PieceToId(_a   )

       # 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 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:str   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:Any   ):
       snake_case__						= ''''''.join(_a   ).replace(_a     ,		''' '''   ).strip()
       return out_string

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

       return (out_vocab_file,)

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:List[str]     ,		_a:str = "eng_Latn"     ,		_a:Optional[List[str]] = None     ,		_a:str = "fra_Latn"     ,		**_a:Union[str, Any]     ,		):
       snake_case__						= src_lang
       snake_case__						= tgt_lang
       return super().prepare_seqaseq_batch(_a     ,		_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       return self.set_src_lang_special_tokens(self.src_lang   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:int   ):
       snake_case__						= self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
         snake_case__						= []
         snake_case__						= [self.eos_token_id, self.cur_lang_code]
       else:
         snake_case__						= [self.cur_lang_code]
         snake_case__						= [self.eos_token_id]







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:str   ):
       snake_case__						= self.lang_code_to_id[lang]
       if self.legacy_behaviour:
         snake_case__						= []
         snake_case__						= [self.eos_token_id, self.cur_lang_code]
       else:
         snake_case__						= [self.cur_lang_code]
         snake_case__						= [self.eos_token_id]





from __future__ import annotations

from statistics import mean







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  snake_case__						= [0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i]
  snake_case__						= []

  snake_case__						= 0
  snake_case__						= 0

  # When processes are not completed,
  # A process whose arrival time has passed \
  # and has remaining execution time is put into the ready_process.
  # The shortest process in the ready_process, target_process is executed.

  while completed != no_of_processes:
    snake_case__						= []
    snake_case__						= -1

    for i in range(__lowerCAmelCase				):
      if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
        ready_process.append(__lowerCAmelCase				)

    if len(__lowerCAmelCase				) > 0:
      snake_case__						= ready_process[0]
      for i in ready_process:
        if remaining_time[i] < remaining_time[target_process]:
          snake_case__						= i
      total_time += burst_time[target_process]
      completed += 1
      snake_case__						= 0
      snake_case__						= (
          total_time - arrival_time[target_process] - burst_time[target_process]
      )
    else:
      total_time += 1

  return waiting_time







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i] + waiting_time[i]
  return turn_around_time


if __name__ == "__main__":
   print("""[TEST CASE 01]""")

   lowerCamelCase__							:						Tuple							     =   4
   lowerCamelCase__							:						Union[str, Any]							     =   [2, 5, 3, 7]
   lowerCamelCase__							:						Optional[Any]							     =   [0, 0, 0, 0]
   lowerCamelCase__							:						Dict							     =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)
   lowerCamelCase__							:						Union[str, Any]							     =   calculate_turnaroundtime(
       burst_time, no_of_processes, waiting_time
   )

   # Printing the Result
   print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
   for i, process_id in enumerate(list(range(1, 5))):
      print(
          F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
          F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
      )
   print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
   print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")

