


'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCAmelCase						=					logging.get_logger(__name__)

lowerCAmelCase						=					"""▁"""

lowerCAmelCase						=					{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCAmelCase						=					{
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

lowerCAmelCase						=					{
    """facebook/m2m100_418M""": 1_024,
}

# fmt: off
lowerCAmelCase						=					{
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=VOCAB_FILES_NAMES
       __UpperCAmelCase					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCAmelCase					=PRETRAINED_VOCAB_FILES_MAP
       __UpperCAmelCase					=['input_ids', 'attention_mask']

       __UpperCAmelCase					=[]
       __UpperCAmelCase					=[]






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase="<s>"			,     _UpperCamelCase="</s>"			,     _UpperCamelCase="</s>"			,     _UpperCamelCase="<pad>"			,     _UpperCamelCase="<unk>"			,     _UpperCamelCase="m2m100"			,     _UpperCamelCase = None			,     _UpperCamelCase=8			,     **_UpperCamelCase			,     )->      None:
            _A				=							{} if sp_model_kwargs is None else sp_model_kwargs

            _A				=							language_codes
            _A				=							FAIRSEQ_LANGUAGE_CODES[language_codes]
            _A				=							{lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

            _A				=							kwargs.get('additional_special_tokens'			,     []   )
            kwargs["additional_special_tokens"] += [
                self.get_lang_token(snake_case_   )
                for lang_code in fairseq_language_code
                if self.get_lang_token(snake_case_   ) not in kwargs["additional_special_tokens"]
            ]

            super().__init__(
                src_lang=snake_case_			,     tgt_lang=snake_case_			,     bos_token=snake_case_			,     eos_token=snake_case_			,     sep_token=snake_case_			,     unk_token=snake_case_			,     pad_token=snake_case_			,     language_codes=snake_case_			,     sp_model_kwargs=self.sp_model_kwargs			,     num_madeup_words=snake_case_			,     **snake_case_			,     )

            _A				=							vocab_file
            _A				=							load_json(snake_case_   )
            _A				=							{v: k for k, v in self.encoder.items()}
            _A				=							spm_file
            _A				=							load_spm(snake_case_			,     self.sp_model_kwargs   )

            _A				=							len(self.encoder   )

            _A				=							{
                self.get_lang_token(snake_case_   ): self.encoder_size + i for i, lang_code in enumerate(snake_case_   )
            }
            _A				=							{lang_code: self.encoder_size + i for i, lang_code in enumerate(snake_case_   )}
            _A				=							{v: k for k, v in self.lang_token_to_id.items()}

            _A				=							src_lang if src_lang is not None else 'en'
            _A				=							tgt_lang
            _A				=							self.get_lang_id(self._src_lang   )
            self.set_src_lang_special_tokens(self._src_lang   )

            _A				=							num_madeup_words






       @property
       def   UpperCamelCase						( self   )->      int:
            return len(self.encoder   ) + len(self.lang_token_to_id   )






       @property
       def   UpperCamelCase						( self   )->      str:
            return self._src_lang






       @src_lang.setter
       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      None:
            _A				=							new_src_lang
            self.set_src_lang_special_tokens(self._src_lang   )






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






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      str:
            if token in self.lang_token_to_id:
                 return self.lang_token_to_id[token]
            return self.encoder.get(snake_case_			,     self.encoder[self.unk_token]   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      str:
            if index in self.id_to_lang_token:
                 return self.id_to_lang_token[index]
            return self.decoder.get(snake_case_			,     self.unk_token   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      str:
            _A				=							[]
            _A				=							''
            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(snake_case_   ) + token
                      _A				=							[]
                 else:
                      current_sub_tokens.append(snake_case_   )
            out_string += self.sp_model.decode(snake_case_   )
            return out_string.strip()






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = False   )->      List[int]:

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

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






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






       def __getstate__( self   )->      Dict:
            _A				=							self.__dict__.copy()
            _A				=							None
            return state






       def __setstate__( self			,     _UpperCamelCase   )->      None:
            _A				=							d

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

            _A				=							load_spm(self.spm_file			,     self.sp_model_kwargs   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None   )->      Tuple[str]:
            _A				=							Path(snake_case_   )
            if not save_dir.is_dir():
                 raise OSError(F'{save_directory} should be a directory'   )
            _A				=							save_dir / (
                (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
            )
            _A				=							save_dir / (
                (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
            )

            save_json(self.encoder			,     snake_case_   )

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

            return (str(snake_case_   ), str(snake_case_   ))






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = "en"			,     _UpperCamelCase = None			,     _UpperCamelCase = "ro"			,     **_UpperCamelCase			,     )->      BatchEncoding:
            _A				=							src_lang
            _A				=							tgt_lang
            self.set_src_lang_special_tokens(self.src_lang   )
            return super().prepare_seqaseq_batch(snake_case_			,     snake_case_			,     **snake_case_   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     **_UpperCamelCase   )->      List[Any]:
            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(snake_case_			,     add_special_tokens=snake_case_			,     **snake_case_   )
            _A				=							self.get_lang_id(snake_case_   )
            _A				=							tgt_lang_id
            return inputs






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            self.set_src_lang_special_tokens(self.src_lang   )






       def   UpperCamelCase						( self   )->      Dict:
            self.set_tgt_lang_special_tokens(self.tgt_lang   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      None:
            _A				=							self.get_lang_token(snake_case_   )
            _A				=							self.lang_token_to_id[lang_token]
            _A				=							[self.cur_lang_id]
            _A				=							[self.eos_token_id]






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      None:
            _A				=							self.get_lang_token(snake_case_   )
            _A				=							self.lang_token_to_id[lang_token]
            _A				=							[self.cur_lang_id]
            _A				=							[self.eos_token_id]






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      str:
            return self.lang_code_to_token[lang]






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      int:
            _A				=							self.get_lang_token(snake_case_   )
            return self.lang_token_to_id[lang_token]






def 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       Dict[str, Any]		)					->	sentencepiece.SentencePieceProcessor:







     """simple docstring"""
     _A				=							sentencepiece.SentencePieceProcessor(**__lowerCamelCase		)
     spm.Load(str(__lowerCamelCase		)		)
     return spm

def 					lowerCamelCase_ ( __UpperCamelCase					:       str		)					->	Union[Dict, List]:







     """simple docstring"""
     with open(__lowerCamelCase							,    'r'		) as f:
          return json.load(__lowerCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any							,    __UpperCamelCase					:       str		)					->	None:







     """simple docstring"""
     with open(__lowerCamelCase							,    'w'		) as f:
          json.dump(__lowerCamelCase							,    __lowerCamelCase							,    indent=2		)










from __future__ import annotations

import time

import numpy as np

lowercase_													=      [8, 5, 9, 7]
lowercase_													=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase_													=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class     __a			:



	def __init__(    self				:     Tuple					,						snake_case_				:     list[int]					,						snake_case_				:     list[list[int]]					,						snake_case_				:     list[list[int]]					,						)->				None:
						__lowerCAmelCase						 =claim_vector
						__lowerCAmelCase						 =allocated_resources_table
						__lowerCAmelCase						 =maximum_claim_table



	def 		UpperCamelCase				(    self				:     Optional[int])->				list[int]:
						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table)
						    for i in range(len(self.__allocated_resources_table[0]))
						]



	def 		UpperCamelCase				(    self				:     Dict)->				list[int]:
						return np.array(self.__claim_vector) - np.array(
						    self.__processes_resource_summation())



	def 		UpperCamelCase				(    self				:     Optional[Any])->				list[list[int]]:
						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]



	def 		UpperCamelCase				(    self				:     Dict)->				dict[int, list[int]]:
						return {self.__need().index(snake_case_): i for i in self.__need()}



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						**snake_case_				:     Optional[Any])->				None:
						__lowerCAmelCase						 =self.__need()
						__lowerCAmelCase						 =self.__allocated_resources_table
						__lowerCAmelCase						 =self.__available_resources()
						__lowerCAmelCase						 =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:
											__lowerCAmelCase						 =False
											for each_need in need_list:
																__lowerCAmelCase						 =True
																for index, need in enumerate(snake_case_):
																					if need > available_resources[index]:
																										__lowerCAmelCase						 =False
																										break
																if execution:
																					__lowerCAmelCase						 =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:
																															__lowerCAmelCase						 =original_need_index
																					print(F"""Process {process_number + 1} is executing.""")
																					# remove the process run from stack
																					need_list.remove(snake_case_)
																					# update available/freed resources stack
																					__lowerCAmelCase						 =np.array(snake_case_) + np.array(
																					    alloc_resources_table[process_number])
																					print(
																					    """Updated available resource stack for processes: """
																					    + """ """.join([str(snake_case_) 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 		UpperCamelCase				(    self				:     List[str])->				Optional[Any]:
						print(""" """ * 9 + """Allocated Resource Table""")
						for item in self.__allocated_resources_table:
											print(
											    F"""P{self.__allocated_resources_table.index(snake_case_) + 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(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(
						    """Current Usage by Active Processes: """
						    + """ """.join(str(snake_case_) for x in self.__claim_vector))
						print(
						    """Initial Available Resources:       """
						    + """ """.join(str(snake_case_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




