"""simple docstring"""


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


__UpperCamelCase									=			logging.get_logger(__name__)

__UpperCamelCase									=			'''▁'''

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

__UpperCamelCase									=			{
    '''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'''
        ),
    }
}

__UpperCamelCase									=			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
__UpperCamelCase									=			['''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  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_			=      PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_			=      ["input_ids", "attention_mask"]

       SCREAMING_SNAKE_CASE_			=      []
       SCREAMING_SNAKE_CASE_			=      []







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__="</s>",							lowerCAmelCase__="</s>",							lowerCAmelCase__="<s>",							lowerCAmelCase__="<unk>",							lowerCAmelCase__="<pad>",							lowerCAmelCase__="<mask>",							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      None:
            # Mask token behave like a normal word, i.e. include the space before it
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) 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=lowerCAmelCase__,							tgt_lang=lowerCAmelCase__,							eos_token=lowerCAmelCase__,							unk_token=lowerCAmelCase__,							sep_token=lowerCAmelCase__,							cls_token=lowerCAmelCase__,							pad_token=lowerCAmelCase__,							mask_token=lowerCAmelCase__,							sp_model_kwargs=self.sp_model_kwargs,							**lowerCAmelCase__,							)

            snake_case_					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.Load(str(lowerCAmelCase__))
            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(lowerCAmelCase__)
            }
            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 		a_						( self)      ->      int:
            return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token







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







       @src_lang.setter
       def 		a_						( self,							lowerCAmelCase__)      ->      None:
            snake_case_					       =					new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)







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







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







       def 		a_						( self,							lowerCAmelCase__)      ->      List[str]:
            return self.sp_model.encode(lowerCAmelCase__,							out_type=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__)      ->      int:
            if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
            snake_case_					       =					self.sp_model.PieceToId(lowerCAmelCase__)

            # 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 		a_						( self,							lowerCAmelCase__)      ->      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 		a_						( self,							lowerCAmelCase__)      ->      Tuple:
            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(lowerCAmelCase__) + token
                      snake_case_					       =					True
                      snake_case_					       =					[]
                 else:
                      current_sub_tokens.append(lowerCAmelCase__)
                      snake_case_					       =					False
            out_string += self.sp_model.decode(lowerCAmelCase__)
            return out_string.strip()







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      Tuple[str]:
            if not os.path.isdir(lowerCAmelCase__):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory')
                 return
            snake_case_					       =					os.path.join(
                lowerCAmelCase__,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

            return (out_vocab_file,)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = False)      ->      List[int]:

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

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







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = 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 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__)      ->      Optional[int]:
            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(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__,							return_tensors=lowerCAmelCase__,							**lowerCAmelCase__)
            snake_case_					       =					self.convert_tokens_to_ids(lowerCAmelCase__)
            snake_case_					       =					tgt_lang_id
            return inputs







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = "en_XX",							lowerCAmelCase__ = None,							lowerCAmelCase__ = "ro_RO",							**lowerCAmelCase__,							)      ->      BatchEncoding:
            snake_case_					       =					src_lang
            snake_case_					       =					tgt_lang
            return super().prepare_seqaseq_batch(lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            return self.set_src_lang_special_tokens(self.src_lang)







       def 		a_						( self)      ->      Tuple:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)







       def 		a_						( self,							lowerCAmelCase__)      ->      None:
            snake_case_					       =					self.lang_code_to_id[src_lang]
            snake_case_					       =					[self.cur_lang_code_id]
            snake_case_					       =					[self.eos_token_id]







       def 		a_						( self,							lowerCAmelCase__)      ->      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 __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 				snake_case      (   A__	,A__	,A__						):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			UpperCAmelCase_     : Dict			     =					(low + high) // 2
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Any			     =					max_subarray(A__	,A__	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					max_subarray(A__	,mid + 1	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					max_cross_sum(A__	,A__	,A__	,A__						)
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum

def 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					float("-inf"						), -1
			UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					float("-inf"						), -1

			UpperCAmelCase_     : int | float			     =					0
			for i in range(A__	,low - 1	,-1						):
						summ += arr[i]
						if summ > left_sum:
									UpperCAmelCase_     : str			     =					summ
									UpperCAmelCase_     : Any			     =					i

			UpperCAmelCase_     : Dict			     =					0
			for i in range(mid + 1	,high + 1						):
						summ += arr[i]
						if summ > right_sum:
									UpperCAmelCase_     : List[Any]			     =					summ
									UpperCAmelCase_     : Optional[Any]			     =					i

			return max_left, max_right, (left_sum + right_sum)

def 				snake_case      (   A__						):
			UpperCAmelCase_     : str			     =					[randint(1	,A__						) for _ in range(A__						)]
			UpperCAmelCase_     : str			     =					time.time()
			max_subarray(A__	,0	,input_size - 1						)
			UpperCAmelCase_     : int			     =					time.time()
			return end - start

def 				snake_case      (   ):
			UpperCAmelCase_     : int			     =					[10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
			UpperCAmelCase_     : List[str]			     =					[time_max_subarray(A__						) for input_size in input_sizes]
			print("No of Inputs\t\tTime Taken"						)
			for input_size, runtime in zip(A__	,A__						):
						print(A__	,"\t\t"	,A__						)
			plt.plot(A__	,A__						)
			plt.xlabel("Number of Inputs"						)
			plt.ylabel("Time taken in seconds"						)
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



