





'''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


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						str    =			{"""vocab_file""": """spiece.model"""}

__magic_name__   :						Union[str, Any]    =			{
    """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
__magic_name__   :						Union[str, Any]    =			{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

__magic_name__   :						Optional[Any]    =			"""▁"""


class       __SCREAMING_SNAKE_CASE	(						_a			):



					'''simple docstring'''






					UpperCAmelCase__       :						Dict      =   VOCAB_FILES_NAMES
					UpperCAmelCase__       :						str      =   PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__       :						Union[str, Any]      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__       :						List[str]      =   ["""input_ids""", """attention_mask"""]

					def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="</s>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase=100						,   lowerCamelCase=None						,   lowerCamelCase = None						,   lowerCamelCase=True						,   **lowerCamelCase						,   ):
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
									_snake_case     		=				[F'''<extra_id_{i}>''' for i in range(snake_case_	)]
							elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra_id special tokens
									_snake_case     		=				len(set(filter(lambda lowerCamelCase	: bool("extra_id" in str(snake_case_	)	)						,   snake_case_	)	)	)
									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"	)

							_snake_case     		=				legacy
							_snake_case     		=				{} if sp_model_kwargs is None else sp_model_kwargs

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

							_snake_case     		=				vocab_file
							_snake_case     		=				extra_ids

							_snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.Load(snake_case_	)

					@staticmethod
					def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
							if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
									_snake_case     		=				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."						,   snake_case_						,   )

							return max_model_length

					@property
					def 	UpperCamelCase(					self	):
							return self.sp_model.get_piece_size() + self._extra_ids

					def 	UpperCamelCase(					self	):
							_snake_case     		=				{self.convert_ids_to_tokens(snake_case_	): i for i in range(self.vocab_size	)}
							vocab.update(self.added_tokens_encoder	)
							return vocab

					def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = 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_	)

							# normal case: some special tokens
							if token_ids_a is None:
									return ([0] * len(snake_case_	)) + [1]
							return ([0] * len(snake_case_	)) + [1] + ([0] * len(snake_case_	)) + [1]

					def 	UpperCamelCase(					self	):
							return list(
							    set(filter(lambda lowerCamelCase	: bool(re.search(R"<extra_id_\d+>"						,   snake_case_	)	) is not None						,   self.additional_special_tokens	)	)	)

					def 	UpperCamelCase(					self	):
							return [self._convert_token_to_id(snake_case_	) for token in self.get_sentinel_tokens()]

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							if len(snake_case_	) > 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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
							_snake_case     		=				[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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
							_snake_case     		=				self._add_eos_if_not_present(snake_case_	)
							if token_ids_a is None:
									return token_ids_a
							else:
									_snake_case     		=				self._add_eos_if_not_present(snake_case_	)
									return token_ids_a + token_ids_a

					def __getstate__(					self	):
							_snake_case     		=				self.__dict__.copy()
							_snake_case     		=				None
							return state

					def __setstate__(					self						,   lowerCamelCase	):
							_snake_case     		=				d

							# for backward compatibility
							if not hasattr(self						,   "sp_model_kwargs"	):
									_snake_case     		=				{}

							_snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.Load(self.vocab_file	)

					def 	UpperCamelCase(					self						,   lowerCamelCase						,   **lowerCamelCase	):
							# 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:
									_snake_case     		=				SPIECE_UNDERLINE + text.replace(snake_case_						,   " "	)
							return super().tokenize(snake_case_						,   **snake_case_	)

					def 	UpperCamelCase(					self						,   lowerCamelCase						,   **lowerCamelCase	):
							if not self.legacy:
									_snake_case     		=				text.startswith(snake_case_	)
									if is_first:
											_snake_case     		=				text[1:]

							_snake_case     		=				self.sp_model.encode(snake_case_						,   out_type=snake_case_	)

							if not self.legacy and not is_first and not text.startswith(" "	) and tokens[0].startswith(snake_case_	):
									_snake_case     		=				([tokens[0][1:]] if len(tokens[0]	) > 1 else []) + tokens[1:]
							return tokens

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							if token.startswith("<extra_id_"	):
									_snake_case     		=				re.match(R"<extra_id_(\d+)>"						,   snake_case_	)
									_snake_case     		=				int(match.group(1	)	)
									return self.vocab_size - num - 1
							return self.sp_model.piece_to_id(snake_case_	)

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							if index < self.sp_model.get_piece_size():
									_snake_case     		=				self.sp_model.IdToPiece(snake_case_	)
							else:
									_snake_case     		=				F'''<extra_id_{self.vocab_size - 1 - index}>'''
							return token

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							_snake_case     		=				[]
							_snake_case     		=				""
							_snake_case     		=				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(snake_case_	) + token
											_snake_case     		=				True
											_snake_case     		=				[]
									else:
											current_sub_tokens.append(snake_case_	)
											_snake_case     		=				False
							out_string += self.sp_model.decode(snake_case_	)
							return out_string.strip()






					def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
							if not os.path.isdir(snake_case_	):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
									return
							_snake_case     		=				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:
											_snake_case     		=				self.sp_model.serialized_model_proto()
											fi.write(snake_case_	)

							return (out_vocab_file,)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__magic_name__   :						Any    =			{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 __magic_name__   :						Union[str, Any]    =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


