

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_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      '''▁'''

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

lowerCAmelCase_			      =      {
    '''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'''
        ),
    }
}

lowerCAmelCase_			      =      {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
lowerCAmelCase_			      =      ['''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       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Dict		     =							VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE	:  List[Any]		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE	:  Dict		     =							PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE	:  Dict		     =							["input_ids", "attention_mask"]

			SCREAMING_SNAKE_CASE	:  List[int]		     =							[]
			SCREAMING_SNAKE_CASE	:  List[int]		     =							[]







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

									snake_case_  =      {} if sp_model_kwargs is None else sp_model_kwargs

									snake_case_  =      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				,  )

									snake_case_  =      spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.Load(str(_UpperCamelCase				)				)
									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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# 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(_UpperCamelCase				)
									}
									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_  =      src_lang if src_lang is not None else '''en_XX'''
									snake_case_  =      self.lang_code_to_id[self._src_lang]
									snake_case_  =      tgt_lang
									self.set_src_lang_special_tokens(self._src_lang				)







			@property
			def 							snake_case__(	self					:							Optional[int]				) ->int:
									return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token







			@property
			def 							snake_case__(	self					:							Optional[int]				) ->str:
									return self._src_lang







			@src_lang.setter
			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      new_src_lang
									self.set_src_lang_special_tokens(self._src_lang				)







			def __getstate__(	self					:							List[str]				) ->Dict:
									snake_case_  =      self.__dict__.copy()
									snake_case_  =      None
									return state







			def __setstate__(	self					:							List[Any]				,  _UpperCamelCase					:							Dict				) ->None:
									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.Load(self.vocab_file				)







			def 							snake_case__(	self					:							Any				) ->Dict:
									snake_case_  =      {self.convert_ids_to_tokens(_UpperCamelCase				): i for i in range(self.vocab_size				)}
									vocab.update(self.added_tokens_encoder				)
									return vocab







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							str				) ->List[str]:
									return self.sp_model.encode(_UpperCamelCase				,  out_type=_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							str				) ->int:
									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									snake_case_  =      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 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							int				) ->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 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Optional[int]				) ->Any:
									snake_case_  =      []
									snake_case_  =      ''''''
									snake_case_  =      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
																					snake_case_  =      True
																					snake_case_  =      []
															else:
																					current_sub_tokens.append(_UpperCamelCase				)
																					snake_case_  =      False
									out_string += self.sp_model.decode(_UpperCamelCase				)
									return out_string.strip()







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str] = None				) ->Tuple[str]:
									if not os.path.isdir(_UpperCamelCase				):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
															return
									snake_case_  =      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:
																					snake_case_  =      self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase				)

									return (out_vocab_file,)







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							Optional[List[int]] = None				,  _UpperCamelCase					:							bool = False				) ->List[int]:

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

									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(_UpperCamelCase				)) + suffix_ones
									return prefix_ones + ([0] * len(_UpperCamelCase				)) + ([0] * len(_UpperCamelCase				)) + suffix_ones







			def 							snake_case__(	self					:							Dict				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							Optional[List[int]] = None				) ->List[int]:
									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 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str]				,  _UpperCamelCase					:							Optional[str]				,  **_UpperCamelCase					:							Dict				) ->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(_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				,  return_tensors=_UpperCamelCase				,  **_UpperCamelCase				)
									snake_case_  =      self.convert_tokens_to_ids(_UpperCamelCase				)
									snake_case_  =      tgt_lang_id
									return inputs







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							str = "en_XX"				,  _UpperCamelCase					:							Optional[List[str]] = None				,  _UpperCamelCase					:							str = "ro_RO"				,  **_UpperCamelCase					:							int				,  ) ->BatchEncoding:
									snake_case_  =      src_lang
									snake_case_  =      tgt_lang
									return super().prepare_seqaseq_batch(_UpperCamelCase				,  _UpperCamelCase				,  **_UpperCamelCase				)







			def 							snake_case__(	self					:							Dict				) ->Optional[Any]:
									return self.set_src_lang_special_tokens(self.src_lang				)







			def 							snake_case__(	self					:							List[str]				) ->str:
									return self.set_tgt_lang_special_tokens(self.tgt_lang				)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      self.lang_code_to_id[src_lang]
									snake_case_  =      [self.cur_lang_code_id]
									snake_case_  =      [self.eos_token_id]







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      self.lang_code_to_id[tgt_lang]
									snake_case_  =      [self.cur_lang_code_id]
									snake_case_  =      [self.eos_token_id]

"""simple docstring"""




from collections import deque







class     lowercase:



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







         """simple docstring"""



         a__		=			process_name  # process name
         a__		=			arrival_time  # arrival time of the process
         # completion time of finished process or last interrupted time
         a__		=			arrival_time
         a__		=			burst_time  # remaining burst time
         a__		=			0  # total time of the process wait in ready queue
         a__		=			0  # time from arrival time to completion time







class     lowercase:



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







         """simple docstring"""



         a__		=			number_of_queues
         # time slice of queues that round robin algorithm applied
         a__		=			time_slices
         # unfinished process is in this ready_queue
         a__		=			queue
         # current time
         a__		=			current_time
         # finished process is in this sequence queue
         a__		=			deque()



     def 			lowercase__  (							self   )		->     list[str]:







         """simple docstring"""



         a__		=			[]
         for i in range(len(self.finish_queue   )   ):
             sequence.append(self.finish_queue[i].process_name   )
         return sequence



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



         a__		=			[]
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             waiting_times.append(queue[i].waiting_time   )
         return waiting_times



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



         a__		=			[]
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             turnaround_times.append(queue[i].turnaround_time   )
         return turnaround_times



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



         a__		=			[]
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             completion_times.append(queue[i].stop_time   )
         return completion_times



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



         return [q.burst_time for q in queue]



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         process.waiting_time += self.current_time - process.stop_time
         return process.waiting_time



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     deque[Process]:







         """simple docstring"""



         a__		=			deque()  # sequence deque of finished process
         while len(__SCREAMING_SNAKE_CASE   ) != 0:
             a__		=			ready_queue.popleft()  # current process

             # if process's arrival time is later than current time, update current time
             if self.current_time < cp.arrival_time:
                 self.current_time += cp.arrival_time

             # update waiting time of current process
             self.update_waiting_time(__SCREAMING_SNAKE_CASE   )
             # update current time
             self.current_time += cp.burst_time
             # finish the process and set the process's burst-time 0
             a__		=			0
             # set the process's turnaround time because it is finished
             a__		=			self.current_time - cp.arrival_time
             # set the completion time
             a__		=			self.current_time
             # add the process to queue that has finished queue
             finished.append(__SCREAMING_SNAKE_CASE   )

         self.finish_queue.extend(__SCREAMING_SNAKE_CASE   )  # add finished process to finish queue
         # FCFS will finish all remaining processes
         return finished



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     tuple[deque[Process], deque[Process]]:







         """simple docstring"""



         a__		=			deque()  # sequence deque of terminated process
         # just for 1 cycle and unfinished processes will go back to queue
         for _ in range(len(__SCREAMING_SNAKE_CASE   )   ):
             a__		=			ready_queue.popleft()  # current process

             # if process's arrival time is later than current time, update current time
             if self.current_time < cp.arrival_time:
                 self.current_time += cp.arrival_time

             # update waiting time of unfinished processes
             self.update_waiting_time(__SCREAMING_SNAKE_CASE   )
             # if the burst time of process is bigger than time-slice
             if cp.burst_time > time_slice:
                 # use CPU for only time-slice
                 self.current_time += time_slice
                 # update remaining burst time
                 cp.burst_time -= time_slice
                 # update end point time
                 a__		=			self.current_time
                 # locate the process behind the queue because it is not finished
                 ready_queue.append(__SCREAMING_SNAKE_CASE   )
             else:
                 # use CPU for remaining burst time
                 self.current_time += cp.burst_time
                 # set burst time 0 because the process is finished
                 a__		=			0
                 # set the finish time
                 a__		=			self.current_time
                 # update the process' turnaround time because it is finished
                 a__		=			self.current_time - cp.arrival_time
                 # add the process to queue that has finished queue
                 finished.append(__SCREAMING_SNAKE_CASE   )

         self.finish_queue.extend(__SCREAMING_SNAKE_CASE   )  # add finished process to finish queue
         # return finished processes queue and remaining processes queue
         return finished, ready_queue




     def 			lowercase__  (							self   )		->     deque[Process]:







         """simple docstring"""



         for i in range(self.number_of_queues - 1   ):
             a__	,     a__		=			self.round_robin(
                 self.ready_queue     ,				self.time_slices[i]   )
         #  the last queue has first_come_first_served algorithm
         self.first_come_first_served(self.ready_queue   )

         return self.finish_queue


if __name__ == "__main__":
    import doctest

    a		:       List[str]     						=       Process('P1', 0, 53)
    a		:       Optional[int]     						=       Process('P2', 0, 17)
    a		:       Union[str, Any]     						=       Process('P3', 0, 68)
    a		:       Optional[int]     						=       Process('P4', 0, 24)
    a		:       Optional[int]     						=       3
    a		:       Optional[Any]     						=       [17, 25]
    a		:       Union[str, Any]     						=       deque([Pa, Pa, Pa, Pa])

    if len(time_slices) != number_of_queues - 1:
        raise SystemExit(0)

    doctest.testmod(extraglobs={'queue': deque([Pa, Pa, Pa, Pa])})

    a		:       Union[str, Any]     						=       Process('P1', 0, 53)
    a		:       Optional[int]     						=       Process('P2', 0, 17)
    a		:       Optional[Any]     						=       Process('P3', 0, 68)
    a		:       str     						=       Process('P4', 0, 24)
    a		:       Optional[Any]     						=       3
    a		:       Tuple     						=       [17, 25]
    a		:       int     						=       deque([Pa, Pa, Pa, Pa])
    a		:       Tuple     						=       MLFQ(number_of_queues, time_slices, queue, 0)
    a		:       Optional[int]     						=       mlfq.multi_level_feedback_queue()

    # print total waiting times of processes(P1, P2, P3, P4)
    print(
        F'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
    )
    # print completion times of processes(P1, P2, P3, P4)
    print(
        F'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
    )
    # print total turnaround times of processes(P1, P2, P3, P4)
    print(
        F'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
    )
    # print sequence of finished processes
    print(
        F'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
    )


