






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case    :    Union[str, Any]         =     logging.get_logger(__name__)

__snake_case    :    str         =     {"""vocab_file""": """spm_char.model"""}

__snake_case    :    Tuple         =     {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

__snake_case    :    str         =     {
    """microsoft/speecht5_asr""": 10_24,
    """microsoft/speecht5_tts""": 10_24,
    """microsoft/speecht5_vc""": 10_24,
}


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Any   =					VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE	:       Any   =					PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE	:       int   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					['''input_ids''', '''attention_mask''']






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase="<s>"     ,						_UpperCamelCase="</s>"     ,						_UpperCamelCase="<unk>"     ,						_UpperCamelCase="<pad>"     ,						_UpperCamelCase = None     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=_UpperCamelCase     ,						eos_token=_UpperCamelCase     ,						unk_token=_UpperCamelCase     ,						pad_token=_UpperCamelCase     ,						sp_model_kwargs=self.sp_model_kwargs     ,						**_UpperCamelCase     ,						)

												lowerCAmelCase__    						=						vocab_file

												lowerCAmelCase__    						=						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(_UpperCamelCase   )






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.sp_model.get_piece_size()






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{self.convert_ids_to_tokens(_UpperCamelCase   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab






						def __getstate__(	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.__dict__.copy()
												lowerCAmelCase__    						=						None
												return state






						def __setstate__(	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						d

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

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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return self.sp_model.encode(_UpperCamelCase     ,						out_type=_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return self.sp_model.piece_to_id(_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.sp_model.IdToPiece(_UpperCamelCase   )
												return token






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						[]
												lowerCAmelCase__    						=						''
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(_UpperCamelCase   ) + token
																								lowerCAmelCase__    						=						[]
																		else:
																								current_sub_tokens.append(_UpperCamelCase   )
												out_string += self.sp_model.decode(_UpperCamelCase   )
												return out_string.strip()






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if token_ids_a is None:
																		return token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return token_ids_a + token_ids_a + [self.eos_token_id]






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = False   ):






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

												lowerCAmelCase__    						=						[1]
												if token_ids_a is None:
																		return ([0] * len(_UpperCamelCase   )) + suffix_ones
												return ([0] * len(_UpperCamelCase   )) + ([0] * len(_UpperCamelCase   )) + suffix_ones







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												if not os.path.isdir(_UpperCamelCase   ):
																		logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
																		return
												lowerCAmelCase__    						=						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:
																								lowerCAmelCase__    						=						self.sp_model.serialized_model_proto()
																								fi.write(_UpperCamelCase   )

												return (out_vocab_file,)













from __future__ import annotations

import pandas as pd







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   list[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * no_of_processes
						lowerCAmelCase__    						=						[0] * no_of_processes
						# Copy the burst time into remaining_time[]
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						burst_time[i]

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						9_9999_9999
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						False

						# Process until all processes are completed
						while complete != no_of_processes:
												for j in range(UpperCamelCase_		):
																		if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																								if remaining_time[j] < minm:
																														lowerCAmelCase__    						=						remaining_time[j]
																														lowerCAmelCase__    						=						j
																														lowerCAmelCase__    						=						True

												if not check:
																		increment_time += 1
																		continue
												remaining_time[short] -= 1

												lowerCAmelCase__    						=						remaining_time[short]
												if minm == 0:
																		lowerCAmelCase__    						=						9_9999_9999

												if remaining_time[short] == 0:
																		complete += 1
																		lowerCAmelCase__    						=						False

																		# Find finish time of current process
																		lowerCAmelCase__    						=						increment_time + 1

																		# Calculate waiting time
																		lowerCAmelCase__    						=						finish_time - arrival_time[short]
																		lowerCAmelCase__    						=						finar - burst_time[short]

																		if waiting_time[short] < 0:
																								lowerCAmelCase__    						=						0

        # Increment time
												increment_time += 1
						return waiting_time







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					list[int]		)  ->   list[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * no_of_processes
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						burst_time[i] + waiting_time[i]
						return turn_around_time







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   None:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						total_waiting_time + waiting_time[i]
												lowerCAmelCase__    						=						total_turn_around_time + turn_around_time[i]
						print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}"		)
						print('Average turn around time ='    ,       total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
					print("""Enter how many process you want to analyze""")
					__snake_case    :    Dict         =     int(input())
					__snake_case    :    List[Any]         =     [0] * no_of_processes
					__snake_case    :    str         =     [0] * no_of_processes
					__snake_case    :    Optional[int]         =     list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
										__snake_case     , __snake_case    :    Any         =     map(int, input().split())

					__snake_case    :    str         =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					__snake_case    :    Dict         =     burst_time
					__snake_case    :    Optional[int]         =     no_of_processes
					__snake_case    :    str         =     waiting_time
					__snake_case    :    int         =     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					__snake_case    :    Dict         =     pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        """Process""",
					        """BurstTime""",
					        """ArrivalTime""",
					        """WaitingTime""",
					        """TurnAroundTime""",
					    ],
					)

					# Printing the dataFrame
					pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
					print(fcfs)





