




"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
			from ...tokenization_utils_base import TextInput
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

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

lowercase__  		=		{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase__  		=		{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

lowercase__  		=		"""▁"""






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase="</s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase=100						,   lowercase=None						,   lowercase = None						,   lowercase=True						,   **lowercase						,   ):
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
											_lowerCamelCase  :    Dict					=					[F'''<extra_id_{i}>''' for i in range(lowercase						)]
							elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra_id special tokens
											_lowerCamelCase  :    Optional[int]					=					len(set(filter(lambda lowercase						: bool('extra_id' in str(lowercase						)						)						,   lowercase						)						)						)
											if extra_tokens != extra_ids:
															raise ValueError(
															    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
															    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
															    ' tokens'						)
							if legacy:
											logger.warning_once(
											    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
											    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'						)

							_lowerCamelCase  :    Any					=					legacy
							_lowerCamelCase  :    Optional[Any]					=					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=lowercase						,   unk_token=lowercase						,   pad_token=lowercase						,   extra_ids=lowercase						,   additional_special_tokens=lowercase						,   sp_model_kwargs=self.sp_model_kwargs						,   legacy=lowercase						,   **lowercase						,   )

							_lowerCamelCase  :    Optional[Any]					=					vocab_file
							_lowerCamelCase  :    List[Any]					=					extra_ids

							_lowerCamelCase  :    Union[str, Any]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(lowercase						)
			@staticmethod
			def       A_					(						lowercase						,   lowercase						,   lowercase						):
							if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
											_lowerCamelCase  :    Any					=					TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
															return init_max_model_length
											elif init_max_model_length is None:
															warnings.warn(
															    'This tokenizer was incorrectly instantiated with a model max length of'
															    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
															    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
															    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
															    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
															    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
															    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
															    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
															    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'						,   lowercase						,   )

							return max_model_length
			@property
			def       A_					(						self						):
							return self.sp_model.get_piece_size() + self._extra_ids
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					{self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab
			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = False						):
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase						,   token_ids_a=lowercase						,   already_has_special_tokens=lowercase						)

							# normal case: some special tokens
							if token_ids_a is None:
											return ([0] * len(lowercase						)) + [1]
							return ([0] * len(lowercase						)) + [1] + ([0] * len(lowercase						)) + [1]
			def       A_					(						self						):
							return list(
							    set(filter(lambda lowercase						: bool(re.search(r'<extra_id_\d+>'						,   lowercase						)						) is not None						,   self.additional_special_tokens						)						)						)
			def       A_					(						self						):
							return [self._convert_token_to_id(lowercase						) for token in self.get_sentinel_tokens()]
			def       A_					(						self						,   lowercase						):
							if len(lowercase						) > 0 and token_ids[-1] == self.eos_token_id:
											warnings.warn(
											    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
											    ' eos tokens being added.'						)
											return token_ids
							else:
											return token_ids + [self.eos_token_id]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    List[Any]					=					[self.eos_token_id]

							if token_ids_a is None:
											return len(token_ids_a + eos						) * [0]
							return len(token_ids_a + eos + token_ids_a + eos						) * [0]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    Dict					=					self._add_eos_if_not_present(lowercase						)
							if token_ids_a is None:
											return token_ids_a
							else:
											_lowerCamelCase  :    Optional[Any]					=					self._add_eos_if_not_present(lowercase						)
											return token_ids_a + token_ids_a
			def __getstate__(						self						):
							_lowerCamelCase  :    str					=					self.__dict__.copy()
							_lowerCamelCase  :    Tuple					=					None
							return state
			def __setstate__(						self						,   lowercase						):
							_lowerCamelCase  :    Dict					=					d

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

							_lowerCamelCase  :    int					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(self.vocab_file						)
			def       A_					(						self						,   lowercase						,   **lowercase						):
							# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
							# the beginning of the text
							if not self.legacy:
											_lowerCamelCase  :    Any					=					SPIECE_UNDERLINE + text.replace(lowercase						,   ' '						)
							return super().tokenize(lowercase						,   **lowercase						)
			def       A_					(						self						,   lowercase						,   **lowercase						):
							if not self.legacy:
											_lowerCamelCase  :    Dict					=					text.startswith(lowercase						)
											if is_first:
															_lowerCamelCase  :    Optional[Any]					=					text[1:]

							_lowerCamelCase  :    Optional[int]					=					self.sp_model.encode(lowercase						,   out_type=lowercase						)

							if not self.legacy and not is_first and not text.startswith(' '						) and tokens[0].startswith(lowercase						):
											_lowerCamelCase  :    Optional[int]					=					([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
							return tokens
			def       A_					(						self						,   lowercase						):
							if token.startswith('<extra_id_'						):
											_lowerCamelCase  :    Optional[Any]					=					re.match(r'<extra_id_(\d+)>'						,   lowercase						)
											_lowerCamelCase  :    int					=					int(match.group(1						)						)
											return self.vocab_size - num - 1
							return self.sp_model.piece_to_id(lowercase						)
			def       A_					(						self						,   lowercase						):
							if index < self.sp_model.get_piece_size():
											_lowerCamelCase  :    str					=					self.sp_model.IdToPiece(lowercase						)
							else:
											_lowerCamelCase  :    Any					=					F'''<extra_id_{self.vocab_size - 1 - index}>'''
							return token
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Optional[Any]					=					[]
							_lowerCamelCase  :    List[str]					=					''
							_lowerCamelCase  :    List[Any]					=					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(lowercase						) + token
															_lowerCamelCase  :    Dict					=					True
															_lowerCamelCase  :    Union[str, Any]					=					[]
											else:
															current_sub_tokens.append(lowercase						)
															_lowerCamelCase  :    List[Any]					=					False
							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  :    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}\''''	)