




"""simple docstring"""




import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


a_            =   logging.get_logger(__name__)

a_            =   {
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_            =   {
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

a_            =   {'Helsinki-NLP/opus-mt-en-de': 5_1_2}
a_            =   {}

class        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									=VOCAB_FILES_NAMES
		UpperCamelCase									=PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase									=PRETRAINED_INIT_CONFIGURATION
		UpperCamelCase									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase									=["input_ids", "attention_mask"]
		UpperCamelCase									=re.compile(">>.+<<"						)  # type: re.Pattern
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_="<unk>"							,		UpperCamelCase_="</s>"							,		UpperCamelCase_="<pad>"							,		UpperCamelCase_=5_12							,		UpperCamelCase_ = None							,		UpperCamelCase_=False							,		**UpperCamelCase_							,		)		->   None:
					__lowercase	:       Any				=   {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
					    source_lang=UpperCamelCase_							,		target_lang=UpperCamelCase_							,		unk_token=UpperCamelCase_							,		eos_token=UpperCamelCase_							,		pad_token=UpperCamelCase_							,		model_max_length=UpperCamelCase_							,		sp_model_kwargs=self.sp_model_kwargs							,		target_vocab_file=UpperCamelCase_							,		separate_vocabs=UpperCamelCase_							,		**UpperCamelCase_							,		)
					assert Path(UpperCamelCase_   ).exists(), F"""cannot find spm source {source_spm}"""

					__lowercase	:       List[str]				=   separate_vocabs
					__lowercase	:       List[str]				=   load_json(UpperCamelCase_   )
					if self.unk_token not in self.encoder:
								raise KeyError('''<unk> token must be in vocab'''   )
					assert self.pad_token in self.encoder

					if separate_vocabs:
								__lowercase	:       List[str]				=   load_json(UpperCamelCase_   )
								__lowercase	:       int				=   {v: k for k, v in self.target_encoder.items()}
								__lowercase	:       int				=   []
					else:
								__lowercase	:       str				=   {v: k for k, v in self.encoder.items()}
								__lowercase	:       list				=   [k for k in self.encoder if k.startswith('''>>'''   ) and k.endswith('''<<'''   )]

					__lowercase	:       Union[str, Any]				=   source_lang
					__lowercase	:       int				=   target_lang
					__lowercase	:       Dict				=   [source_spm, target_spm]

					# load SentencePiece model for pre-processing
					__lowercase	:       int				=   load_spm(UpperCamelCase_							,		self.sp_model_kwargs   )
					__lowercase	:       int				=   load_spm(UpperCamelCase_							,		self.sp_model_kwargs   )
					__lowercase	:       int				=   self.spm_source
					__lowercase	:       str				=   self.encoder

					# Multilingual target side: default to using first supported language code.

					self._setup_normalizer()
		def 					_lowerCamelCase    (		self   )		->   Dict:
					try:
								from sacremoses import MosesPunctNormalizer

								__lowercase	:       List[str]				=   MosesPunctNormalizer(self.source_lang   ).normalize
					except (ImportError, FileNotFoundError):
								warnings.warn('''Recommended: pip install sacremoses.'''   )
								__lowercase	:       str				=   lambda UpperCamelCase_   : x
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   str:
					return self.punc_normalizer(UpperCamelCase_   ) if x else ""
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   List[str]:
					return self.current_encoder.get(UpperCamelCase_							,		self.current_encoder[self.unk_token]   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Dict:
					__lowercase	:       Union[str, Any]				=   self.language_code_re.match(UpperCamelCase_   )
					__lowercase	:       list				=   [match.group(0   )] if match else []
					return code, self.language_code_re.sub(''''''							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   List[str]:
					__lowercase	,__lowercase	:       Optional[int]				=   self.remove_language_code(UpperCamelCase_   )
					__lowercase	:       List[Any]				=   self.current_spm.encode(UpperCamelCase_							,		out_type=UpperCamelCase_   )
					return code + pieces
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   str:
					return self.decoder.get(UpperCamelCase_							,		self.unk_token   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		**UpperCamelCase_   )		->   str:
					return super().batch_decode(UpperCamelCase_							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		**UpperCamelCase_   )		->   Tuple:
					return super().decode(UpperCamelCase_							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   str:
					__lowercase	:       str				=   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
					__lowercase	:       Union[str, Any]				=   []
					__lowercase	:       List[Any]				=   ''''''
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											out_string += sp_model.decode_pieces(UpperCamelCase_   ) + token + " "
											__lowercase	:       str				=   []
								else:
											current_sub_tokens.append(UpperCamelCase_   )
					out_string += sp_model.decode_pieces(UpperCamelCase_   )
					return out_string.strip()
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   List[int]:
					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 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       Union[str, Any]				=   self.spm_source
					__lowercase	:       int				=   self.encoder
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       List[str]				=   self.spm_target
					if self.separate_vocabs:
								__lowercase	:       int				=   self.target_encoder
		@property
		def 					_lowerCamelCase    (		self   )		->   int:
					return len(self.encoder   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_ = None   )		->   Tuple[str]:
					if not os.path.isdir(UpperCamelCase_   ):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
								return
					__lowercase	:       Optional[Any]				=   []

					if self.separate_vocabs:
								__lowercase	:       Optional[int]				=   os.path.join(
								    UpperCamelCase_							,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']							,		)
								__lowercase	:       Optional[Any]				=   os.path.join(
								    UpperCamelCase_							,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']							,		)
								save_json(self.encoder							,		UpperCamelCase_   )
								save_json(self.target_encoder							,		UpperCamelCase_   )
								saved_files.append(UpperCamelCase_   )
								saved_files.append(UpperCamelCase_   )
					else:
								__lowercase	:       List[Any]				=   os.path.join(
								    UpperCamelCase_							,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']   )
								save_json(self.encoder							,		UpperCamelCase_   )
								saved_files.append(UpperCamelCase_   )

					for spm_save_filename, spm_orig_path, spm_model in zip(
					    [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]							,		self.spm_files							,		[self.spm_source, self.spm_target]							,		):
								__lowercase	:       Optional[Any]				=   os.path.join(
								    UpperCamelCase_							,		(filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename   )
								if os.path.abspath(UpperCamelCase_   ) != os.path.abspath(UpperCamelCase_   ) and os.path.isfile(UpperCamelCase_   ):
											copyfile(UpperCamelCase_							,		UpperCamelCase_   )
											saved_files.append(UpperCamelCase_   )
								elif not os.path.isfile(UpperCamelCase_   ):
											with open(UpperCamelCase_							,		'''wb'''   ) as fi:
														__lowercase	:       Tuple				=   spm_model.serialized_model_proto()
														fi.write(UpperCamelCase_   )
											saved_files.append(UpperCamelCase_   )

					return tuple(UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Dict:
					return self.get_src_vocab()
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					return dict(self.encoder							,		**self.added_tokens_encoder   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					return dict(self.target_encoder							,		**self.added_tokens_decoder   )
		def __getstate__(		self   )		->   Dict:
					__lowercase	:       Optional[int]				=   self.__dict__.copy()
					state.update(
					    {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}   )
					return state
		def __setstate__(		self							,		UpperCamelCase_   )		->   None:
					__lowercase	:       Union[str, Any]				=   d

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

					__lowercase	,__lowercase	:       str				=   (load_spm(UpperCamelCase_							,		self.sp_model_kwargs   ) for f in self.spm_files)
					__lowercase	:       Optional[int]				=   self.spm_source
					self._setup_normalizer()
		def 					_lowerCamelCase    (		self							,		*UpperCamelCase_							,		**UpperCamelCase_   )		->   Dict:
					return 1
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   List[Any]:
					__lowercase	:       Union[str, Any]				=   set(self.all_special_ids   )  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special
					return [1 if x in all_special_ids else 0 for x in seq]
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_ = None							,		UpperCamelCase_ = False   )		->   List[int]:
					if already_has_special_tokens:
								return self._special_token_mask(UpperCamelCase_   )
					elif token_ids_a is None:
								return self._special_token_mask(UpperCamelCase_   ) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			__lowercase	:       Tuple				=   sentencepiece.SentencePieceProcessor(**__UpperCamelCase					)
			spm.Load(__UpperCamelCase					)
			return spm







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			with open(__UpperCamelCase					,							'''w'''					) as f:
						json.dump(__UpperCamelCase					,							__UpperCamelCase					,							indent=2					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			with open(__UpperCamelCase					,							'''r'''					) as f:
						return json.load(__UpperCamelCase					)









"""simple docstring"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [1, 2, 3]

			# ensure that we actually have processes
			if len(processes) == 0:
						print('Zero amount of processes')
						raise SystemExit(0)

			# duration time of all processes
			a_            =   [1_9, 8, 9]

			# ensure we can match each id to a duration time
			if len(duration_times) != len(processes):
						print('Unable to match all id\'s with their duration time')
						raise SystemExit(0)

			# get the waiting times and the turnaround times
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   calculate_average_turnaround_time(turnaround_times)

			# print all the results
			print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
			for i, process in enumerate(processes):
						print(
						    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
						    F"{turnaround_times[i]}"
						)
			print(F"Average waiting time = {average_waiting_time}")
			print(F"Average turn around time = {average_turnaround_time}")



