





"""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


__A											=						logging.get_logger(__name__)

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

__A											=						{
    """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"""
        ),
    }
}

__A											=						{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:int						    =   VOCAB_FILES_NAMES
       __magic_name__	:Optional[int]						    =   PRETRAINED_VOCAB_FILES_MAP
       __magic_name__	:Optional[int]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __magic_name__	:List[str]						    =   ["""input_ids""", """attention_mask"""]
       __magic_name__	:List[int]						    =   []






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase="<unk>"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="<pad>"       ,					__UpperCAmelCase="[SEP]"       ,					__UpperCAmelCase="[MASK]"       ,					__UpperCAmelCase="[CLS]"       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else bos_token
         lowerCAmelCase__     :Any											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else eos_token
         lowerCAmelCase__     :Optional[int]											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else unk_token
         lowerCAmelCase__     :List[Any]											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else pad_token
         lowerCAmelCase__     :Tuple											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else cls_token
         lowerCAmelCase__     :str											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else sep_token

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

         lowerCAmelCase__     :List[Any]											=				{} if sp_model_kwargs is None else sp_model_kwargs

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

         lowerCAmelCase__     :Optional[int]											=				vocab_file

         lowerCAmelCase__     :Tuple											=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(__UpperCAmelCase )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.sp_model.get_piece_size()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				{self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






       def __getstate__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.__dict__.copy()
         lowerCAmelCase__     :Union[str, Any]											=				None
         return state






       def __setstate__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				d

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

         lowerCAmelCase__     :List[Any]											=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.sp_model.encode(__UpperCAmelCase       ,					out_type=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.sp_model.piece_to_id(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.sp_model.IdToPiece(__UpperCAmelCase )
         return token






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				[]
         lowerCAmelCase__     :Dict											=				''
         lowerCAmelCase__     :Tuple											=				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(__UpperCAmelCase ) + token
             lowerCAmelCase__     :str											=				True
             lowerCAmelCase__     :Any											=				[]
           else:
             current_sub_tokens.append(__UpperCAmelCase )
             lowerCAmelCase__     :List[str]											=				False
         out_string += self.sp_model.decode(__UpperCAmelCase )
         return out_string.strip()






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				kwargs.pop('use_source_tokenizer'       ,					__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				self.convert_ids_to_tokens(__UpperCAmelCase       ,					skip_special_tokens=__UpperCAmelCase )

         # 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
         lowerCAmelCase__     :List[str]											=				[]
         lowerCAmelCase__     :List[Any]											=				[]
         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(__UpperCAmelCase ) )
               lowerCAmelCase__     :List[Any]											=				[]
             sub_texts.append(__UpperCAmelCase )
           else:
             current_sub_text.append(__UpperCAmelCase )
         if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase ) )

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

         lowerCAmelCase__     :Any											=				(
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
           lowerCAmelCase__     :int											=				self.clean_up_tokenization(__UpperCAmelCase )
           return clean_text
         else:
           return text






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         if not os.path.isdir(__UpperCAmelCase ):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
           return
         lowerCAmelCase__     :Dict											=				os.path.join(
             __UpperCAmelCase       ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file       ,					__UpperCAmelCase )
         elif not os.path.isfile(self.vocab_file ):
           with open(__UpperCAmelCase       ,					'wb' ) as fi:
             lowerCAmelCase__     :Optional[int]											=				self.sp_model.serialized_model_proto()
             fi.write(__UpperCAmelCase )

         return (out_vocab_file,)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowerCAmelCase__     :Optional[int]											=				[self.cls_token_id]
         lowerCAmelCase__     :Any											=				[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False ):



         '''simple docstring'''

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

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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				[self.sep_token_id]
         lowerCAmelCase__     :Dict											=				[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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase   :    Tuple     =		{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		["BlenderbotSmallTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[str]     =		[
		    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BlenderbotSmallForCausalLM",
		    "BlenderbotSmallForConditionalGeneration",
		    "BlenderbotSmallModel",
		    "BlenderbotSmallPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Any     =		[
		    "TFBlenderbotSmallForConditionalGeneration",
		    "TFBlenderbotSmallModel",
		    "TFBlenderbotSmallPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		[
		    "FlaxBlenderbotSmallForConditionalGeneration",
		    "FlaxBlenderbotSmallModel",
		    "FlaxBlenderbotSmallPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase   :    List[Any]     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

