

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {'vocab_file': 'sentencepiece.model'}

SCREAMING_SNAKE_CASE_            = {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

SCREAMING_SNAKE_CASE_            = {
    'google/rembert': 256,
}

class 			a					(		snake_case_			):
						_lowercase        =     VOCAB_FILES_NAMES
						_lowercase        =     PRETRAINED_VOCAB_FILES_MAP
						_lowercase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


						def __init__(    self  ,   A_  ,   A_=False  ,   A_=True  ,   A_=True  ,   A_="[CLS]"  ,   A_="[SEP]"  ,   A_="[UNK]"  ,   A_="[SEP]"  ,   A_="[PAD]"  ,   A_="[CLS]"  ,   A_="[MASK]"  ,   **A_  ,   ):







									'''simple docstring'''


									super().__init__(
									    do_lower_case=A_  ,   remove_space=A_  ,   keep_accents=A_  ,   bos_token=A_  ,   eos_token=A_  ,   unk_token=A_  ,   sep_token=A_  ,   pad_token=A_  ,   cls_token=A_  ,   mask_token=A_  ,   **A_  ,   )

									_UpperCAmelCase    :						List[str]							=      do_lower_case
									_UpperCAmelCase    :						Dict							=      remove_space
									_UpperCAmelCase    :						Optional[int]							=      keep_accents
									_UpperCAmelCase    :						Union[str, Any]							=      vocab_file

									_UpperCAmelCase    :						Any							=      spm.SentencePieceProcessor()
									self.sp_model.Load(A_		)


						@property
						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									return len(self.sp_model		)


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						List[Any]							=      {self.convert_ids_to_tokens(A_		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab


						def __getstate__(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						Any							=      self.__dict__.copy()
									_UpperCAmelCase    :						Any							=      None
									return state


						def __setstate__(    self  ,   A_		):







									'''simple docstring'''


									_UpperCAmelCase    :						List[str]							=      d
									_UpperCAmelCase    :						int							=      spm.SentencePieceProcessor()
									self.sp_model.Load(self.vocab_file		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=False		):







									'''simple docstring'''


									_UpperCAmelCase    :						Dict							=      self.sp_model.EncodeAsPieces(A_		)
									return pieces


						def     _UpperCAmelCase			(    self  ,   A_		):







									'''simple docstring'''


									return self.sp_model.PieceToId(A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







									'''simple docstring'''


									return self.sp_model.IdToPiece(A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







									'''simple docstring'''


									_UpperCAmelCase    :						str							=      self.sp_model.decode_pieces(A_		)
									return out_string


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







									'''simple docstring'''


									_UpperCAmelCase    :						Any							=      [self.sep_token_id]
									_UpperCAmelCase    :						Tuple							=      [self.cls_token_id]
									if token_ids_a is None:
												return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = False		):







									'''simple docstring'''


									if already_has_special_tokens:
												if token_ids_a is not None:
															raise ValueError(
															    "You should not supply a second sequence if the provided sequence of "
															    "ids is already formatted with special tokens for the model."		)
												return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
												return [1] + ([0] * len(A_		)) + [1] + ([0] * len(A_		)) + [1]
									return [1] + ([0] * len(A_		)) + [1]


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







									'''simple docstring'''


									_UpperCAmelCase    :						Union[str, Any]							=      [self.sep_token_id]
									_UpperCAmelCase    :						Union[str, Any]							=      [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		) * [0] + len(token_ids_a + sep		) * [1]





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







									'''simple docstring'''


									if not os.path.isdir(A_		):
												logger.error("Vocabulary path ({}) should be a directory".format(A_		)		)
												return
									_UpperCAmelCase    :						Optional[Any]							=      os.path.join(
									    A_  ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(A_		):
												copyfile(self.vocab_file  ,   A_		)

									return (out_vocab_file,)





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


