






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__A					=	logging.get_logger(__name__)


__A					=	{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
__A					=	{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

__A					=	{
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							List[Any]				= VOCAB_FILES_NAMES
    lowerCamelCase	:							Tuple				= PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase	:							Optional[int]				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase	:							Union[str, Any]				= ['input_ids', 'attention_mask']
    lowerCamelCase	:							str				= BartTokenizer



    def __init__(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     str=None ,     __SCREAMING_SNAKE_CASE      :     Dict=None ,     __SCREAMING_SNAKE_CASE      :     List[str]=None ,     __SCREAMING_SNAKE_CASE      :     List[Any]="replace" ,     __SCREAMING_SNAKE_CASE      :     str="<s>" ,     __SCREAMING_SNAKE_CASE      :     List[Any]="</s>" ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]="</s>" ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]="<s>" ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]="<unk>" ,     __SCREAMING_SNAKE_CASE      :     Dict="<pad>" ,     __SCREAMING_SNAKE_CASE      :     Optional[int]="<mask>" ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=False ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=True ,     **__SCREAMING_SNAKE_CASE      :     List[str] ,     )      ->							Dict:
           super().__init__(
               __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     tokenizer_file=__SCREAMING_SNAKE_CASE ,     errors=__SCREAMING_SNAKE_CASE ,     bos_token=__SCREAMING_SNAKE_CASE ,     eos_token=__SCREAMING_SNAKE_CASE ,     sep_token=__SCREAMING_SNAKE_CASE ,     cls_token=__SCREAMING_SNAKE_CASE ,     unk_token=__SCREAMING_SNAKE_CASE ,     pad_token=__SCREAMING_SNAKE_CASE ,     mask_token=__SCREAMING_SNAKE_CASE ,     add_prefix_space=__SCREAMING_SNAKE_CASE ,     trim_offsets=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     )

           __UpperCAmelCase     				=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
           if pre_tok_state.get("""add_prefix_space""" ,     __SCREAMING_SNAKE_CASE      ) != add_prefix_space:
                  __UpperCAmelCase     				=getattr(__SCREAMING_SNAKE_CASE ,     pre_tok_state.pop("""type"""      )      )
                  __UpperCAmelCase     				=add_prefix_space
                  __UpperCAmelCase     				=pre_tok_class(**__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           __UpperCAmelCase     				="""post_processor"""
           __UpperCAmelCase     				=getattr(self.backend_tokenizer ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           if tokenizer_component_instance:
                  __UpperCAmelCase     				=json.loads(tokenizer_component_instance.__getstate__()      )

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                         __UpperCAmelCase     				=tuple(state["""sep"""]      )
                  if "cls" in state:
                         __UpperCAmelCase     				=tuple(state["""cls"""]      )

                  __UpperCAmelCase     				=False

                  if state.get("""add_prefix_space""" ,     __SCREAMING_SNAKE_CASE      ) != add_prefix_space:
                         __UpperCAmelCase     				=add_prefix_space
                         __UpperCAmelCase     				=True

                  if state.get("""trim_offsets""" ,     __SCREAMING_SNAKE_CASE      ) != trim_offsets:
                         __UpperCAmelCase     				=trim_offsets
                         __UpperCAmelCase     				=True

                  if changes_to_apply:
                         __UpperCAmelCase     				=getattr(__SCREAMING_SNAKE_CASE ,     state.pop("""type"""      )      )
                         __UpperCAmelCase     				=component_class(**__SCREAMING_SNAKE_CASE      )
                         setattr(self.backend_tokenizer ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    @property
    def _a							(				self      :     Any      )      ->							str:
           if self._mask_token is None:
                  if self.verbose:
                         logger.error("""Using mask_token, but it is not set yet."""      )
                  return None
           return str(self._mask_token      )



    @mask_token.setter
    def _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=AddedToken(__SCREAMING_SNAKE_CASE ,     lstrip=__SCREAMING_SNAKE_CASE ,     rstrip=__SCREAMING_SNAKE_CASE      ) if isinstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) else value
           __UpperCAmelCase     				=value



    def _a							(				self      :     Tuple ,     *__SCREAMING_SNAKE_CASE      :     str ,     **__SCREAMING_SNAKE_CASE      :     Union[str, Any]      )      ->							BatchEncoding:
           __UpperCAmelCase     				=kwargs.get("""is_split_into_words""" ,     __SCREAMING_SNAKE_CASE      )

           if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                      """to use it with pretokenized inputs."""      )

           return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[str] ,     *__SCREAMING_SNAKE_CASE      :     str ,     **__SCREAMING_SNAKE_CASE      :     List[Any]      )      ->							BatchEncoding:
           __UpperCAmelCase     				=kwargs.get("""is_split_into_words""" ,     __SCREAMING_SNAKE_CASE      )

           if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                      """to use it with pretokenized inputs."""      )

           return super()._encode_plus(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Optional[str] = None      )      ->							Tuple[str]:
           __UpperCAmelCase     				=self._tokenizer.model.save(__SCREAMING_SNAKE_CASE ,     name=__SCREAMING_SNAKE_CASE      )
           return tuple(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=None      )      ->							List[Any]:
           __UpperCAmelCase     				=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                  return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     List[int] ,     __SCREAMING_SNAKE_CASE      :     Optional[List[int]] = None      )      ->							List[int]:
           __UpperCAmelCase     				=[self.sep_token_id]
           __UpperCAmelCase     				=[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]






from __future__ import annotations





class     lowerCamelCase      :







	def __init__(     self      :List[Any]   ,     lowercase      :list[list[int]]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= 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(lowercase			) != 0:
							SCREAMING_SNAKE_CASE     						= len(rows[0]			)
							if cols == 0:
										raise error
							for row in rows:
										if len(lowercase			) != cols:
													raise error
										for value in row:
													if not isinstance(lowercase   ,     (int, float)			):
																raise error
							SCREAMING_SNAKE_CASE     						= rows
				else:
							SCREAMING_SNAKE_CASE     						= []







	def  snake_case__     (     self      :List[str]			)		->      list[list[int]]:







				"""simple docstring"""






				return [[row[i] for row in self.rows] for i in range(len(self.rows[0]			)			)]







	@property
	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				return len(self.rows			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      int:







				"""simple docstring"""






				return len(self.rows[0]			)







	@property
	def  snake_case__     (     self      :Dict			)		->      tuple[int, int]:







				"""simple docstring"""






				return (self.num_rows, self.num_columns)







	@property
	def  snake_case__     (     self      :Any			)		->      bool:







				"""simple docstring"""






				return self.order[0] == self.order[1]







	def  snake_case__     (     self      :Tuple			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [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(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      int:







				"""simple docstring"""






				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  snake_case__     (     self      :int			)		->      bool:







				"""simple docstring"""






				return bool(self.determinant()			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [
				        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(lowercase			).determinant()







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				if (row + column) % 2 == 0:
							return self.get_minor(lowercase   ,     lowercase			)
				return -1 * self.get_minor(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Matrix:







				"""simple docstring"""






				return Matrix(
				    [
				        [self.get_minor(lowercase   ,     lowercase			) for column in range(self.num_columns			)]
				        for row in range(self.num_rows			)
				    ]			)







	def  snake_case__     (     self      :Dict			)		->      Matrix:







				"""simple docstring"""






				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  snake_case__     (     self      :str			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [self.cofactors().rows[column][row] for column in range(self.num_columns			)]
				    for row in range(self.num_rows			)
				]
				return Matrix(lowercase			)







	def  snake_case__     (     self      :Any			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= 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      :Union[str, Any]			)		->      str:







				"""simple docstring"""






				return str(self.rows			)







	def __str__(     self      :List[str]			)		->      str:







				"""simple docstring"""






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







	def  snake_case__     (     self      :int   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError('''Row must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in row:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != 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(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= self.rows[0:position] + [row] + self.rows[position:]







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






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







	def __eq__(     self      :Union[str, Any]   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							return NotImplemented
				return self.rows == other.rows







	def __ne__(     self      :int   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				return not self == other







	def __neg__(     self      :int			)		->      Matrix:







				"""simple docstring"""






				return self * -1







	def __add__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				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      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				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      :Optional[int]   ,     lowercase      :Matrix | int | float			)		->      Matrix:







				"""simple docstring"""






				if isinstance(lowercase   ,     (int, float)			):
							return Matrix(
							    [[int(element * other			) for element in row] for row in self.rows]			)
				elif isinstance(lowercase   ,     lowercase			):
							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(lowercase   ,     lowercase			) 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      :str   ,     lowercase      :int			)		->      Matrix:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							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'''			)
				SCREAMING_SNAKE_CASE     						= self
				for _ in range(other - 1			):
							result *= self
				return result







	@classmethod
	def  snake_case__     (     cls      :List[Any]   ,     lowercase      :list[int]   ,     lowercase      :list[int]			)		->      int:







				"""simple docstring"""






				return sum(row[i] * column[i] for i in range(len(lowercase			)			)			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()