
"""simple docstring"""



import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

UpperCAmelCase__          = logging.get_logger(__name__)

class       lowerCAmelCase__				(						A_	):
     __a						=					"""vision-encoder-decoder"""
     __a						=					True







     def __init__(							self			:			Dict    ,      **_lowerCamelCase			:			Dict			):
       super().__init__(**_lowerCamelCase			)
       if "encoder" not in kwargs or "decoder" not in kwargs:
         raise ValueError(
             f'''A configuraton of type {self.model_type} cannot be instantiated because '''
             f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''			)

       _snake_case					     =       kwargs.pop('''encoder'''			)
       _snake_case					     =       encoder_config.pop('''model_type'''			)
       _snake_case					     =       kwargs.pop('''decoder'''			)
       _snake_case					     =       decoder_config.pop('''model_type'''			)

       _snake_case					     =       AutoConfig.for_model(_lowerCamelCase    ,      **_lowerCamelCase			)
       _snake_case					     =       AutoConfig.for_model(_lowerCamelCase    ,      **_lowerCamelCase			)
       _snake_case					     =       True







     @classmethod
     def      lowercase	(							cls			:			Tuple    ,      _lowerCamelCase			:			PretrainedConfig    ,      _lowerCamelCase			:			PretrainedConfig    ,      **_lowerCamelCase			:			Dict			):
       logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
       _snake_case					     =       True
       _snake_case					     =       True

       return cls(encoder=encoder_config.to_dict()    ,      decoder=decoder_config.to_dict()    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       copy.deepcopy(self.__dict__			)
       _snake_case					     =       self.encoder.to_dict()
       _snake_case					     =       self.decoder.to_dict()
       _snake_case					     =       self.__class__.model_type
       return output






class       lowerCAmelCase__				(						A_	):
     __a						=					version.parse("""1.11"""	)







     @property
     def      lowercase	(							self			:			str			):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]			)







     @property
     def      lowercase	(							self			:			List[Any]			):
       return 1e-4







     @property
     def      lowercase	(							self			:			Optional[Any]			):
       return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}			)






class       lowerCAmelCase__				(						A_	):







     @property
     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       OrderedDict()
       _snake_case					     =       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       _snake_case					     =       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       _snake_case					     =       {0: '''batch''', 1: '''encoder_sequence'''}

       return common_inputs







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			"PreTrainedTokenizerBase"    ,      _lowerCamelCase			:			int = -1    ,      _lowerCamelCase			:			int = -1    ,      _lowerCamelCase			:			bool = False    ,      _lowerCamelCase			:			Optional["TensorType"] = None    ,      ):
       import torch

       _snake_case					     =       OrderedDict()

       _snake_case					     =       super().generate_dummy_inputs(
           _lowerCamelCase    ,      batch_size=_lowerCamelCase    ,      seq_length=_lowerCamelCase    ,      is_pair=_lowerCamelCase    ,      framework=_lowerCamelCase			)

       _snake_case     ,					_snake_case					     =       dummy_input['''input_ids'''].shape
       _snake_case					     =       (batch, encoder_sequence, self._config.encoder_hidden_size)
       _snake_case					     =       dummy_input.pop('''input_ids'''			)
       _snake_case					     =       dummy_input.pop('''attention_mask'''			)
       _snake_case					     =       torch.zeros(_lowerCamelCase			)

       return common_inputs






class       lowerCAmelCase__				(						A_	):







     @property
     def      lowercase	(							self			:			Optional[int]			):
       pass







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			PretrainedConfig			):
       return VisionEncoderDecoderEncoderOnnxConfig(_lowerCamelCase			)







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			PretrainedConfig    ,      _lowerCamelCase			:			PretrainedConfig    ,      _lowerCamelCase			:			str = "default"			):
       _snake_case					     =       encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(_lowerCamelCase    ,      _lowerCamelCase			)





"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCAmelCase__          = {
    'google/rembert': 256,
}

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			Any=True    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			int="[CLS]"    ,      _lowerCamelCase			:			Optional[int]="[SEP]"    ,      _lowerCamelCase			:			Optional[int]="[UNK]"    ,      _lowerCamelCase			:			Optional[Any]="[SEP]"    ,      _lowerCamelCase			:			str="[PAD]"    ,      _lowerCamelCase			:			List[Any]="[CLS]"    ,      _lowerCamelCase			:			Any="[MASK]"    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       super().__init__(
           do_lower_case=_lowerCamelCase    ,      remove_space=_lowerCamelCase    ,      keep_accents=_lowerCamelCase    ,      bos_token=_lowerCamelCase    ,      eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      sep_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      cls_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      **_lowerCamelCase    ,      )

       _snake_case					     =       do_lower_case
       _snake_case					     =       remove_space
       _snake_case					     =       keep_accents
       _snake_case					     =       vocab_file

       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			int			):
       return len(self.sp_model			)







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







     def __getstate__(							self			:			List[str]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			List[str]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       d
       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Tuple=False			):
       _snake_case					     =       self.sp_model.EncodeAsPieces(_lowerCamelCase			)
       return pieces







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str			):
       return self.sp_model.PieceToId(_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			int			):
       return self.sp_model.IdToPiece(_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       self.sp_model.decode_pieces(_lowerCamelCase			)
       return out_string







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''			)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [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]







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowerCamelCase			)			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			):
         copyfile(self.vocab_file    ,      _lowerCamelCase			)

       return (out_vocab_file,)



