




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase     :		Optional[int]								=       logging.get_logger(__name__)

_lowerCamelCase     :		str								=       {
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}


class     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''bit'''
    UpperCAmelCase__							  =				['''preactivation''', '''bottleneck''']
    UpperCAmelCase__							  =				['''SAME''', '''VALID''']


    def __init__(      self				:     str ,			UpperCAmelCase__				:     Tuple=3 ,			UpperCAmelCase__				:     Any=64 ,			UpperCAmelCase__				:     int=[256, 512, 1_024, 2_048] ,			UpperCAmelCase__				:     List[Any]=[3, 4, 6, 3] ,			UpperCAmelCase__				:     List[str]="preactivation" ,			UpperCAmelCase__				:     Union[str, Any]="relu" ,			UpperCAmelCase__				:     List[Any]=None ,			UpperCAmelCase__				:     Dict=32 ,			UpperCAmelCase__				:     Union[str, Any]=0.0 ,			UpperCAmelCase__				:     Union[str, Any]=False ,			UpperCAmelCase__				:     Union[str, Any]=32 ,			UpperCAmelCase__				:     Dict=1 ,			UpperCAmelCase__				:     Optional[int]=None ,			UpperCAmelCase__				:     List[str]=None ,			**UpperCAmelCase__				:     Tuple ,			)						->Optional[int]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           if layer_type not in self.layer_types:
                  raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types)}""")
           if global_padding is not None:
                  if global_padding.upper() in self.supported_padding:
                         A__							=   global_padding.upper()
                  else:
                         raise ValueError(f"""Padding strategy {global_padding} not supported""")
           A__							=   num_channels
           A__							=   embedding_size
           A__							=   hidden_sizes
           A__							=   depths
           A__							=   layer_type
           A__							=   hidden_act
           A__							=   global_padding
           A__							=   num_groups
           A__							=   drop_path_rate
           A__							=   embedding_dynamic_padding
           A__							=   output_stride
           A__							=   width_factor

           A__							=   ['''stem'''] + [f"""stage{idx}""" for idx in range(1 ,			len(UpperCAmelCase__) + 1)]
           A__			,							A__							=   get_aligned_output_features_output_indices(
               out_features=UpperCAmelCase__ ,			out_indices=UpperCAmelCase__ ,			stage_names=self.stage_names)








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


_lowerCamelCase     :		Union[str, Any]								=       """▁"""

_lowerCamelCase     :		Optional[Any]								=       {"""vocab_file""": """spiece.model"""}

_lowerCamelCase     :		str								=       {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

_lowerCamelCase     :		List[str]								=       {
    """google/pegasus-xsum""": 512,
}


_lowerCamelCase     :		int								=       logging.get_logger(__name__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES

    UpperCAmelCase__							  =				VOCAB_FILES_NAMES
    UpperCAmelCase__							  =				PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =				['''input_ids''', '''attention_mask''']


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str="<pad>" ,			UpperCAmelCase__				:     Optional[Any]="</s>" ,			UpperCAmelCase__				:     Any="<unk>" ,			UpperCAmelCase__				:     Union[str, Any]="<mask_2>" ,			UpperCAmelCase__				:     List[str]="<mask_1>" ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Optional[Any]=103 ,			UpperCAmelCase__				:     Optional[Dict[str, Any]] = None ,			**UpperCAmelCase__				:     Dict ,			)						->None:





           '''simple docstring'''

           A__							=   offset
           if additional_special_tokens is not None:
                  if not isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         raise TypeError(
                             f"""additional_special_tokens should be of type {type(UpperCAmelCase__)}, but is"""
                             f""" {type(UpperCAmelCase__)}""")

                  A__							=   (
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f"""<unk_{i}>""" for i in range(len(UpperCAmelCase__) ,			self.offset - 1)
                  ]

                  if len(set(UpperCAmelCase__)) != len(UpperCAmelCase__):
                         raise ValueError(
                             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                             f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""")
                  A__							=   additional_special_tokens_extended
           else:
                  A__							=   [mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 ,			self.offset)]

           A__							=   {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__ ,			unk_token=UpperCAmelCase__ ,			mask_token=UpperCAmelCase__ ,			pad_token=UpperCAmelCase__ ,			mask_token_sent=UpperCAmelCase__ ,			offset=UpperCAmelCase__ ,			additional_special_tokens=UpperCAmelCase__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**UpperCAmelCase__ ,			)
           A__							=   mask_token_sent
           A__							=   vocab_file
           A__							=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)

           # add special tokens to encoder dict
           A__							=   {
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      })

           if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 ,			self.offset - 1)})

           A__							=   {v: k for k, v in self.encoder.items()}


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->int:





           '''simple docstring'''

           return len(self.sp_model) + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict[str, int]:





           '''simple docstring'''

           A__							=   {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				:     Any)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   self.__dict__.copy()
           A__							=   None
           return state


    def __setstate__(      self				:     int ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__							=   d

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

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str)						->List[str]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           if token in self.decoder:
                  return self.decoder[token]
           elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
           A__							=   self.sp_model.piece_to_id(UpperCAmelCase__)
           return sp_id + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     int)						->str:





           '''simple docstring'''

           if index in self.encoder:
                  return self.encoder[index]
           elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
           else:
                  A__							=   self.sp_model.IdToPiece(index - self.offset)
           return token


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->Optional[int]:





           '''simple docstring'''

           A__							=   []
           A__							=   ''''''
           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(UpperCAmelCase__) + token
                         A__							=   []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[int]=False)						->Union[str, Any]:





           '''simple docstring'''

           return 1


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     List ,			UpperCAmelCase__				:     Optional[List] = None ,			UpperCAmelCase__				:     bool = False)						->List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  return self._special_token_mask(UpperCAmelCase__)
           elif token_ids_a is None:
                  return self._special_token_mask(UpperCAmelCase__) + [1]
           else:
                  return self._special_token_mask(token_ids_a + token_ids_a) + [1]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     List[str]=None)						->List[int]:





           '''simple docstring'''

           if token_ids_a is None:
                  return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]





    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__							=   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:
                         A__							=   self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)


