


'''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


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

A				:					int    						=			'''▁'''

A				:					List[str]    						=			{'''vocab_file''': '''sentencepiece.bpe.model'''}

A				:					List[Any]    						=			{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

A				:					List[Any]    						=			{
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
A				:					Union[str, Any]    						=			['''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''']


class 			__lowerCamelCase			(	_a	):
    """simple docstring"""
    a													=   VOCAB_FILES_NAMES
    a													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=   PRETRAINED_VOCAB_FILES_MAP
    a													=   ["input_ids", "attention_mask"]

    a													=   []
    a													=   []


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

           _A					:     Dict     = {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=snake_case_				,							eos_token=snake_case_				,							unk_token=snake_case_				,							sep_token=snake_case_				,							cls_token=snake_case_				,							pad_token=snake_case_				,							mask_token=snake_case_				,							tokenizer_file=snake_case_				,							src_lang=snake_case_				,							tgt_lang=snake_case_				,							additional_special_tokens=snake_case_				,							sp_model_kwargs=self.sp_model_kwargs				,							**snake_case_				,							)

           _A					:     List[str]     = spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(str(snake_case_))
           _A					:     Dict     = 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
           _A					:     Dict     = {"""<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
           _A					:     Optional[int]     = 1

           _A					:     Optional[Any]     = len(self.sp_model)
           _A					:     int     = {
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(snake_case_)
           }
           _A					:     List[str]     = {v: k for k, v in self.lang_code_to_id.items()}
           _A					:     Union[str, Any]     = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
           _A					:     Optional[int]     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
           _A					:     Optional[Any]     = list(self.lang_code_to_id.keys())

           if additional_special_tokens is not None:
                  # Only add those special tokens if they are not already there.
                  self._additional_special_tokens.extend(
                      [t for t in additional_special_tokens if t not in self._additional_special_tokens])

           _A					:     Tuple     = src_lang if src_lang is not None else """en_XX"""
           _A					:     Optional[Any]     = self.lang_code_to_id[self._src_lang]
           _A					:     Optional[int]     = tgt_lang
           self.set_src_lang_special_tokens(self._src_lang)


    def __getstate__(  self      :							Any):
           _A					:     Dict     = self.__dict__.copy()
           _A					:     Any     = None
           _A					:     Optional[Any]     = self.sp_model.serialized_model_proto()
           return state


    def __setstate__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     Dict     = d

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

           _A					:     List[Any]     = spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto)


    @property
    def        A   (  self      :							Optional[Any]):
           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      :							Optional[Any]):
           return self._src_lang


    @src_lang.setter
    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     int     = new_src_lang
           self.set_src_lang_special_tokens(self._src_lang)


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None				,							SCREAMING_SNAKE_CASE      :							bool = False):

           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					:     Dict     = [1] * len(self.prefix_tokens)
           _A					:     Optional[Any]     = [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        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None):
           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      :							str				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None):
           _A					:     Any     = [self.sep_token_id]
           _A					:     Tuple     = [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[str]				,							SCREAMING_SNAKE_CASE      :							Optional[str]				,							**SCREAMING_SNAKE_CASE      :							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')
           _A					:     int     = src_lang
           _A					:     str     = self(snake_case_				,							add_special_tokens=snake_case_				,							return_tensors=snake_case_				,							**snake_case_)
           _A					:     Any     = self.convert_tokens_to_ids(snake_case_)
           _A					:     List[Any]     = tgt_lang_id
           return inputs


    def        A   (  self      :							Optional[int]):
           _A					:     int     = {self.convert_ids_to_tokens(snake_case_): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							str):
           return self.sp_model.encode(snake_case_				,							out_type=snake_case_)


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
           _A					:     int     = self.sp_model.PieceToId(snake_case_)

           # 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      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           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      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     List[str]     = """""".join(snake_case_).replace(snake_case_				,							' ').strip()
           return out_string


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[str] = None):
           if not os.path.isdir(snake_case_):
                  logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                  return
           _A					:     Dict     = os.path.join(
               snake_case_				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

           return (out_vocab_file,)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str = "en_XX"				,							SCREAMING_SNAKE_CASE      :							Optional[List[str]] = None				,							SCREAMING_SNAKE_CASE      :							str = "ro_RO"				,							**SCREAMING_SNAKE_CASE      :							str				,							):
           _A					:     Optional[Any]     = src_lang
           _A					:     Tuple     = tgt_lang
           return super().prepare_seqaseq_batch(snake_case_				,							snake_case_				,							**snake_case_)


    def        A   (  self      :							List[str]):
           return self.set_src_lang_special_tokens(self.src_lang)


    def        A   (  self      :							List[Any]):
           return self.set_tgt_lang_special_tokens(self.tgt_lang)


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           _A					:     List[Any]     = self.lang_code_to_id[src_lang]
           _A					:     int     = []
           _A					:     Union[str, Any]     = [self.eos_token_id, self.cur_lang_code]


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     int     = self.lang_code_to_id[lang]
           _A					:     Any     = []
           _A					:     Union[str, Any]     = [self.eos_token_id, self.cur_lang_code]










'''simple docstring'''







from __future__ import annotations


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[list[int]]):
           _A					:     Dict     = TypeError(
               'Matrices must be formed from a list of zero or more lists containing at '
               'least one and the same number of values, each of which must be of type '
               'int or float.')
           if len(SCREAMING_SNAKE_CASE) != 0:
                  _A					:     str     = len(rows[0])
                  if cols == 0:
                         raise error
                  for row in rows:
                         if len(SCREAMING_SNAKE_CASE) != cols:
                                raise error
                         for value in row:
                                if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                                       raise error
                  _A					:     str     = rows
           else:
                  _A					:     Tuple     = []


    def        A   (  self      :							Optional[Any]):
           return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]


    @property
    def        A   (  self      :							List[str]):
           return len(self.rows)


    @property
    def        A   (  self      :							Optional[int]):
           return len(self.rows[0])


    @property
    def        A   (  self      :							Optional[int]):
           return (self.num_rows, self.num_columns)


    @property
    def        A   (  self      :							Any):
           return self.order[0] == self.order[1]


    def        A   (  self      :							int):
           _A					:     Union[str, Any]     = [
               [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
               for row_num in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]):
           if not self.is_square:
                  return 0
           if self.order == (0, 0):
                  return 1
           if self.order == (1, 1):
                  return int(self.rows[0][0])
           if self.order == (2, 2):
                  return int(
                      (self.rows[0][0] * self.rows[1][1])
                      - (self.rows[0][1] * self.rows[1][0]))
           else:
                  return sum(
                      self.rows[0][column] * self.cofactors().rows[0][column]
                      for column in range(self.num_columns))


    def        A   (  self      :							Tuple):
           return bool(self.determinant())


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           _A					:     str     = [
               [
                   self.rows[other_row][other_column]
                   for other_column in range(self.num_columns)
                   if other_column != column
               ]
               for other_row in range(self.num_rows)
               if other_row != row
           ]
           return Matrix(SCREAMING_SNAKE_CASE).determinant()


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           if (row + column) % 2 == 0:
                  return self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           return -1 * self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in range(self.num_columns)]
                   for row in range(self.num_rows)
               ])


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [
                       self.minors().rows[row][column]
                       if (row + column) % 2 == 0
                       else self.minors().rows[row][column] * -1
                       for column in range(self.minors().num_columns)
                   ]
                   for row in range(self.minors().num_rows)
               ])


    def        A   (  self      :							Tuple):
           _A					:     str     = [
               [self.cofactors().rows[column][row] for column in range(self.num_columns)]
               for row in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple):
           _A					:     str     = self.determinant()
           if not determinant:
                  raise TypeError('Only matrices with a non-zero determinant have an inverse')
           return self.adjugate() * (1 / determinant)


    def __repr__(  self      :							str):
           return str(self.rows)


    def __str__(  self      :							Optional[int]):
           if self.num_rows == 0:
                  return "[]"
           if self.num_rows == 1:
                  return "[[" + ". ".join(str(self.rows[0])) + "]]"
           return (
               "["
               + "\n ".join(
                   [
                       '[' + '. '.join([str(SCREAMING_SNAKE_CASE) for value in row]) + '.]'
                       for row in self.rows
                   ])
               + "]"
           )


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     Tuple     = TypeError('Row must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in row:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_columns:
                  raise ValueError(
                      'Row must be equal in length to the other rows in the matrix')
           if position is None:
                  self.rows.append(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     List[str]     = self.rows[0:position] + [row] + self.rows[position:]


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     int     = TypeError(
               'Column must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in column:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_rows:
                  raise ValueError(
                      'Column must be equal in length to the other columns in the matrix')
           if position is None:
                  _A					:     str     = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
           else:
                  _A					:     Dict     = [
                      self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                      for i in range(self.num_rows)
                  ]


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							object):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  return NotImplemented
           return self.rows == other.rows


    def __ne__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							object):
           return not self == other


    def __neg__(  self      :							Optional[Any]):
           return self * -1


    def __add__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Addition requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __sub__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Subtraction requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __mul__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Matrix | int | float):
           if isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                  return Matrix(
                      [[int(element * other) for element in row] for row in self.rows])
           elif isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  if self.num_columns != other.num_rows:
                         raise ValueError(
                             'The number of columns in the first matrix must '
                             'be equal to the number of rows in the second')
                  return Matrix(
                      [
                          [Matrix.dot_product(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in other.columns()]
                          for row in self.rows
                      ])
           else:
                  raise TypeError(
                      'A Matrix can only be multiplied by an int, float, or another matrix')


    def __pow__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise TypeError('A Matrix can only be raised to the power of an int')
           if not self.is_square:
                  raise ValueError('Only square matrices can be raised to a power')
           if other == 0:
                  return self.identity()
           if other < 0:
                  if self.is_invertable():
                         return self.inverse() ** (-other)
                  raise ValueError(
                      'Only invertable matrices can be raised to a negative power')
           _A					:     Union[str, Any]     = self
           for _ in range(other - 1):
                  result *= self
           return result


    @classmethod
    def        A   (  cls      :							List[str]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							list[int]):
           return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE)))


if __name__ == "__main__":
   import doctest

   doctest.testmod()






