




"""simple docstring"""






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

import sentencepiece as spm

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{"""vocab_file""": """spiece.model"""}

lowercase__  		=		{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

lowercase__  		=		{"""bert_for_seq_generation""": 512}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    []
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase="<s>"						,   lowercase="</s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase="<::::>"						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    List[str]					=					{} if sp_model_kwargs is None else sp_model_kwargs

							# Add extra_ids to the special token list
							super().__init__(
							    bos_token=lowercase						,   eos_token=lowercase						,   unk_token=lowercase						,   pad_token=lowercase						,   sep_token=lowercase						,   sp_model_kwargs=self.sp_model_kwargs						,   **lowercase						,   )

							_lowerCamelCase  :    Union[str, Any]					=					vocab_file

							_lowerCamelCase  :    Optional[int]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(lowercase						)
			@property
			def       A_					(						self						):
							return self.sp_model.get_piece_size()
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					{self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab
			def __getstate__(						self						):
							_lowerCamelCase  :    Dict					=					self.__dict__.copy()
							_lowerCamelCase  :    Tuple					=					None
							return state
			def __setstate__(						self						,   lowercase						):
							_lowerCamelCase  :    Optional[Any]					=					d

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

							_lowerCamelCase  :    Optional[Any]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(self.vocab_file						)
			def       A_					(						self						,   lowercase						):
							return self.sp_model.encode(lowercase						,   out_type=lowercase						)
			def       A_					(						self						,   lowercase						):
							return self.sp_model.piece_to_id(lowercase						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					self.sp_model.IdToPiece(lowercase						)
							return token
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Dict					=					[]
							_lowerCamelCase  :    int					=					''
							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(lowercase						) + token
															_lowerCamelCase  :    Any					=					[]
											else:
															current_sub_tokens.append(lowercase						)
							out_string += self.sp_model.decode(lowercase						)
							return out_string.strip()






			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if not os.path.isdir(lowercase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
							_lowerCamelCase  :    Optional[int]					=					os.path.join(
							    lowercase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

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

							return (out_vocab_file,)





"""simple docstring"""






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{}
lowercase__  		=		{}
lowercase__  		=		{}







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None							,					):
				_lowerCamelCase  :    str					=					aliases if aliases is not None else []
				if format_type in _FORMAT_TYPES:
								logger.warning(
								    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''	)
				_lowerCamelCase  :    str					=					formatter_cls
				for alias in set(aliases + [format_type]	):
								if alias in _FORMAT_TYPES_ALIASES:
												logger.warning(
												    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''	)
								_lowerCamelCase  :    Dict					=					format_type







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None	):
				_lowerCamelCase  :    Dict					=					aliases if aliases is not None else []
				for alias in set(aliases + [format_type]	):
								_lowerCamelCase  :    Optional[Any]					=					unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
			from .torch_formatter import TorchFormatter

			_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
			lowercase__  		=		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
			_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
			from .tf_formatter import TFFormatter

			_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
			lowercase__  		=		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
			_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
			from .jax_formatter import JaxFormatter

			_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
			lowercase__  		=		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
			_register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def   _snake_case    (       lowercase__	):
				if format_type in _FORMAT_TYPES_ALIASES:
								return _FORMAT_TYPES_ALIASES[format_type]
				else:
								return format_type







def   _snake_case    (       lowercase__							,					**lowercase__	):
				_lowerCamelCase  :    List[str]					=					get_format_type_from_alias(lowercase__	)
				if format_type in _FORMAT_TYPES:
								return _FORMAT_TYPES[format_type](**lowercase__	)
				if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
								raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
				else:
								raise ValueError(
								    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got \'{format_type}\''''	)