



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


__lowerCamelCase         =			logging.get_logger(__name__)

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

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

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


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				VOCAB_FILES_NAMES
       _lowerCamelCase			    =				PRETRAINED_VOCAB_FILES_MAP
       _lowerCamelCase			    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(     self     ,lowercase     ,lowercase=False     ,lowercase=True     ,lowercase=True     ,lowercase="[CLS]"     ,lowercase="[SEP]"     ,lowercase="[UNK]"     ,lowercase="[SEP]"     ,lowercase="[PAD]"     ,lowercase="[CLS]"     ,lowercase="[MASK]"     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(
                 do_lower_case=lowercase     ,remove_space=lowercase     ,keep_accents=lowercase     ,bos_token=lowercase     ,eos_token=lowercase     ,unk_token=lowercase     ,sep_token=lowercase     ,pad_token=lowercase     ,cls_token=lowercase     ,mask_token=lowercase     ,**lowercase     ,)

             UpperCAmelCase_  :	Dict				      =		do_lower_case
             UpperCAmelCase_  :	int				      =		remove_space
             UpperCAmelCase_  :	Optional[Any]				      =		keep_accents
             UpperCAmelCase_  :	Dict				      =		vocab_file

             UpperCAmelCase_  :	List[str]				      =		spm.SentencePieceProcessor()
             self.sp_model.Load(lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             return len(self.sp_model)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		{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):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.__dict__.copy()
             UpperCAmelCase_  :	int				      =		None
             return state



       def __setstate__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		d
             UpperCAmelCase_  :	Optional[int]				      =		spm.SentencePieceProcessor()
             self.sp_model.Load(self.vocab_file)



       def  A_   (     self     ,lowercase     ,lowercase=False):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.sp_model.EncodeAsPieces(lowercase)
             return pieces



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return self.sp_model.PieceToId(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return self.sp_model.IdToPiece(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.sp_model.decode_pieces(lowercase)
             return out_string



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		[self.sep_token_id]
             UpperCAmelCase_  :	int				      =		[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  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = False):

             """simple docstring"""





             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(lowercase)) + [1] + ([0] * len(lowercase)) + [1]
             return [1] + ([0] * len(lowercase)) + [1]



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		[self.sep_token_id]
             UpperCAmelCase_  :	int				      =		[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  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             if not os.path.isdir(lowercase):
                   logger.error("Vocabulary path ({}) should be a directory".format(lowercase))
                   return
             UpperCAmelCase_  :	Any				      =		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):
                   copyfile(self.vocab_file     ,lowercase)

             return (out_vocab_file,)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""trocr"""
       _lowerCamelCase			    =				["""past_key_values"""]
       _lowerCamelCase			    =				{
           """num_attention_heads""": """decoder_attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """decoder_layers""",
       }



       def __init__(     self     ,lowercase=50265     ,lowercase=1024     ,lowercase=12     ,lowercase=16     ,lowercase=4096     ,lowercase="gelu"     ,lowercase=512     ,lowercase=0.1     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=2     ,lowercase=0.02     ,lowercase=0.0     ,lowercase=True     ,lowercase=False     ,lowercase=True     ,lowercase=True     ,lowercase=1     ,lowercase=0     ,lowercase=2     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		vocab_size
             UpperCAmelCase_  :	Tuple				      =		d_model
             UpperCAmelCase_  :	List[str]				      =		decoder_layers
             UpperCAmelCase_  :	Union[str, Any]				      =		decoder_attention_heads
             UpperCAmelCase_  :	Optional[Any]				      =		decoder_ffn_dim
             UpperCAmelCase_  :	Tuple				      =		activation_function
             UpperCAmelCase_  :	List[Any]				      =		max_position_embeddings
             UpperCAmelCase_  :	List[Any]				      =		dropout
             UpperCAmelCase_  :	List[str]				      =		attention_dropout
             UpperCAmelCase_  :	Union[str, Any]				      =		activation_dropout
             UpperCAmelCase_  :	Optional[int]				      =		init_std
             UpperCAmelCase_  :	Dict				      =		decoder_layerdrop
             UpperCAmelCase_  :	List[str]				      =		use_cache
             UpperCAmelCase_  :	Dict				      =		scale_embedding
             UpperCAmelCase_  :	Dict				      =		use_learned_position_embeddings
             UpperCAmelCase_  :	Optional[Any]				      =		layernorm_embedding

             super().__init__(
                 pad_token_id=lowercase     ,bos_token_id=lowercase     ,eos_token_id=lowercase     ,decoder_start_token_id=lowercase     ,**lowercase     ,)
