


'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase      :List[Any]    	=			logging.get_logger(__name__)

lowerCAmelCase      :List[str]    	=			{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase      :Any    	=			{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase      :List[Any]    	=			{
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}
class _lowerCamelCase       (						UpperCamelCase_							):



		'''simple docstring'''







		A_	:			str          =				VOCAB_FILES_NAMES
		A_	:			Dict          =				PRETRAINED_VOCAB_FILES_MAP
		A_	:			List[Any]          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_	:			Optional[Any]          =				['input_ids', 'attention_mask']
		A_	:			List[int]          =				[]







		def __init__(					self		:						Dict  ,     _A		:						Union[str, Any]  ,     _A		:						Union[str, Any]="<unk>"  ,     _A		:						Union[str, Any]="<s>"  ,     _A		:						Union[str, Any]="</s>"  ,     _A		:						List[Any]="<pad>"  ,     _A		:						Dict="[SEP]"  ,     _A		:						Optional[Any]="[MASK]"  ,     _A		:						Optional[Any]="[CLS]"  ,     _A		:						Optional[Dict[str, Any]] = None  ,     **_A		:						str  ,     )  -> None:
			__magic_name__       :    List[str]     	=     AddedToken(lowercase_  ,     lstrip=lowercase_  ,     rstrip=lowercase_			) if isinstance(lowercase_  ,     lowercase_			) else bos_token
			__magic_name__       :    Union[str, Any]     	=     AddedToken(lowercase_  ,     lstrip=lowercase_  ,     rstrip=lowercase_			) if isinstance(lowercase_  ,     lowercase_			) else eos_token
			__magic_name__       :    Optional[Any]     	=     AddedToken(lowercase_  ,     lstrip=lowercase_  ,     rstrip=lowercase_			) if isinstance(lowercase_  ,     lowercase_			) else unk_token
			__magic_name__       :    Union[str, Any]     	=     AddedToken(lowercase_  ,     lstrip=lowercase_  ,     rstrip=lowercase_			) if isinstance(lowercase_  ,     lowercase_			) else pad_token
			__magic_name__       :    Dict     	=     AddedToken(lowercase_  ,     lstrip=lowercase_  ,     rstrip=lowercase_			) if isinstance(lowercase_  ,     lowercase_			) else cls_token
			__magic_name__       :    Optional[int]     	=     AddedToken(lowercase_  ,     lstrip=lowercase_  ,     rstrip=lowercase_			) if isinstance(lowercase_  ,     lowercase_			) else sep_token

			# Mask token behave like a normal word, i.e. include the space before it
			__magic_name__       :    Dict     	=     AddedToken(lowercase_  ,     lstrip=lowercase_  ,     rstrip=lowercase_			) if isinstance(lowercase_  ,     lowercase_			) else mask_token

			__magic_name__       :    Optional[int]     	=     {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=lowercase_  ,     eos_token=lowercase_  ,     unk_token=lowercase_  ,     pad_token=lowercase_  ,     sep_token=lowercase_  ,     mask_token=lowercase_  ,     cls_token=lowercase_  ,     sp_model_kwargs=self.sp_model_kwargs  ,     **lowercase_  ,     )

			__magic_name__       :    List[Any]     	=     vocab_file

			__magic_name__       :    Optional[int]     	=     spm.SentencePieceProcessor(**self.sp_model_kwargs			)
			self.sp_model.Load(lowercase_			)







		@property
		def    __lowerCAmelCase		(					self		:						str			)  -> Union[str, Any]:
			return self.sp_model.get_piece_size()







		def    __lowerCAmelCase		(					self		:						Dict			)  -> List[Any]:
			__magic_name__       :    str     	=     {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		:						List[str]			)  -> List[Any]:
			__magic_name__       :    Tuple     	=     self.__dict__.copy()
			__magic_name__       :    int     	=     None
			return state







		def __setstate__(					self		:						List[str]  ,     _A		:						List[Any]			)  -> str:
			__magic_name__       :    int     	=     d

			# for backward compatibility
			if not hasattr(self  ,     'sp_model_kwargs'			):
				__magic_name__       :    List[str]     	=     {}

			__magic_name__       :    Union[str, Any]     	=     spm.SentencePieceProcessor(**self.sp_model_kwargs			)
			self.sp_model.Load(self.vocab_file			)







		def    __lowerCAmelCase		(					self		:						Union[str, Any]  ,     _A		:						str			)  -> List[str]:
			return self.sp_model.encode(lowercase_  ,     out_type=lowercase_			)







		def    __lowerCAmelCase		(					self		:						int  ,     _A		:						List[str]			)  -> str:
			return self.sp_model.piece_to_id(lowercase_			)







		def    __lowerCAmelCase		(					self		:						int  ,     _A		:						Dict			)  -> Optional[Any]:
			__magic_name__       :    str     	=     self.sp_model.IdToPiece(lowercase_			)
			return token







		def    __lowerCAmelCase		(					self		:						Union[str, Any]  ,     _A		:						str			)  -> Optional[Any]:
			__magic_name__       :    Any     	=     []
			__magic_name__       :    Optional[Any]     	=     ''
			__magic_name__       :    Optional[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
					__magic_name__       :    Any     	=     True
					__magic_name__       :    List[str]     	=     []
				else:
					current_sub_tokens.append(lowercase_			)
					__magic_name__       :    int     	=     False
			out_string += self.sp_model.decode(lowercase_			)
			return out_string.strip()







		def    __lowerCAmelCase		(					self		:						int  ,     _A		:						List[int]  ,     _A		:						bool = False  ,     _A		:						bool = None  ,     _A		:						bool = True  ,     **_A		:						Any  ,     )  -> str:
			__magic_name__       :    Optional[int]     	=     kwargs.pop('use_source_tokenizer'  ,     lowercase_			)

			__magic_name__       :    List[str]     	=     self.convert_ids_to_tokens(lowercase_  ,     skip_special_tokens=lowercase_			)

			# To avoid mixing byte-level and unicode for byte-level BPT
			# we need to build string separately for added tokens and byte-level tokens
			# cf. https://github.com/huggingface/transformers/issues/1133
			__magic_name__       :    Dict     	=     []
			__magic_name__       :    Tuple     	=     []
			for token in filtered_tokens:
				if skip_special_tokens and token in self.all_special_ids:
					continue
				if token in self.added_tokens_encoder:
					if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(lowercase_			)			)
						__magic_name__       :    str     	=     []
					sub_texts.append(lowercase_			)
				else:
					current_sub_text.append(lowercase_			)
			if current_sub_text:
				sub_texts.append(self.convert_tokens_to_string(lowercase_			)			)

			# Mimic the behavior of the Rust tokenizer:
			# No space before [MASK] and [SEP]
			if spaces_between_special_tokens:
				__magic_name__       :    List[Any]     	=     re.sub(R' (\[(MASK|SEP)\])'  ,     R'\1'  ,     ' '.join(lowercase_			)			)
			else:
				__magic_name__       :    Any     	=     ''.join(lowercase_			)

			__magic_name__       :    str     	=     (
			    clean_up_tokenization_spaces
			    if clean_up_tokenization_spaces is not None
			    else self.clean_up_tokenization_spaces
			)
			if clean_up_tokenization_spaces:
				__magic_name__       :    Union[str, Any]     	=     self.clean_up_tokenization(lowercase_			)
				return clean_text
			else:
				return text







		def    __lowerCAmelCase		(					self		:						Union[str, Any]  ,     _A		:						str  ,     _A		:						Optional[str] = None			)  -> Tuple[str]:




			if not os.path.isdir(lowercase_			):
				logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
				return
			__magic_name__       :    Dict     	=     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:
					__magic_name__       :    Tuple     	=     self.sp_model.serialized_model_proto()
					fi.write(lowercase_			)

			return (out_vocab_file,)







		def    __lowerCAmelCase		(					self		:						Optional[int]  ,     _A		:						List[int]  ,     _A		:						Optional[List[int]] = None			)  -> List[int]:




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







		def    __lowerCAmelCase		(					self		:						Optional[Any]  ,     _A		:						List[int]  ,     _A		:						Optional[List[int]] = None  ,     _A		:						bool = False			)  -> List[int]:




			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=lowercase_  ,     token_ids_a=lowercase_  ,     already_has_special_tokens=lowercase_			)

			if token_ids_a is None:
				return [1] + ([0] * len(lowercase_			)) + [1]
			return [1] + ([0] * len(lowercase_			)) + [1] + ([0] * len(lowercase_			)) + [1]







		def    __lowerCAmelCase		(					self		:						Optional[Any]  ,     _A		:						List[int]  ,     _A		:						Optional[List[int]] = None			)  -> List[int]:
			__magic_name__       :    Union[str, Any]     	=     [self.sep_token_id]
			__magic_name__       :    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]



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
 from ..ta.tokenization_ta import TaTokenizer
else:
 from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase											=       TaTokenizer

if is_tokenizers_available():
 from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
 from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase											=       TaTokenizerFast

lowerCamelCase											=       {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       [
     'MT5EncoderModel',
     'MT5ForConditionalGeneration',
     'MT5ForQuestionAnswering',
     'MT5Model',
     'MT5PreTrainedModel',
     'MT5Stack',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
 from .configuration_mta import MTaConfig, MTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mta import (
      MTaEncoderModel,
      MTaForConditionalGeneration,
      MTaForQuestionAnswering,
      MTaModel,
      MTaPreTrainedModel,
      MTaStack,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
 import sys

 lowerCamelCase											=       _LazyModule(
     __name__,
     globals()['__file__'],
     _import_structure,
     extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
     module_spec=__spec__,
 )


