





"""simple docstring"""


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_rembert import RemBertTokenizer
else:
    __lowerCamelCase      :Any	    =		None

__lowerCamelCase      :List[Any]	    =		logging.get_logger(__name__)
__lowerCamelCase      :int	    =		{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

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

__lowerCamelCase      :Optional[Any]	    =		{
    'google/rembert': 256,
}

__lowerCamelCase      :Tuple	    =		'▁'


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=VOCAB_FILES_NAMES
     snake_case__	:					Optional[int]													=PRETRAINED_VOCAB_FILES_MAP
     snake_case__	:					Union[str, Any]													=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__	:					Optional[int]													=RemBertTokenizer

     def __init__(	self:						Union[str, Any]     ,		__a:						Tuple=None     ,		__a:						Any=None     ,		__a:						str=True     ,		__a:						List[str]=True     ,		__a:						str=False     ,		__a:						Tuple="[CLS]"     ,		__a:						Optional[Any]="[SEP]"     ,		__a:						Union[str, Any]="<unk>"     ,		__a:						List[str]="[SEP]"     ,		__a:						str="<pad>"     ,		__a:						str="[CLS]"     ,		__a:						List[str]="[MASK]"     ,		**__a:						Dict     ,		)->    Optional[int]:
           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase					:					Dict        =	AddedToken(__a     ,		lstrip=__a     ,		rstrip=__a	) if isinstance(__a     ,		__a	) else mask_token

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

           lowerCamelCase					:					Optional[Any]        =	do_lower_case
           lowerCamelCase					:					str        =	remove_space
           lowerCamelCase					:					Any        =	keep_accents
           lowerCamelCase					:					Optional[Any]        =	vocab_file
           lowerCamelCase					:					Optional[int]        =	False if not self.vocab_file else True

     def 		a__						(	self:						str     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None	)->    List[int]:
           lowerCamelCase					:					Optional[Any]        =	[self.sep_token_id]
           lowerCamelCase					:					Dict        =	[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:						Optional[Any]     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None     ,		__a:						bool = False	)->    List[int]:

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

     def 		a__						(	self:						Dict     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None	)->    List[int]:
           lowerCamelCase					:					Union[str, Any]        =	[self.sep_token_id]
           lowerCamelCase					:					List[str]        =	[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:						List[str]     ,		__a:						str     ,		__a:						Optional[str] = None	)->    Tuple[str]:
           if not os.path.isdir(__a	):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(__a	)	)
                 return
           lowerCamelCase					:					Optional[Any]        =	os.path.join(
               __a     ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

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

           return (out_vocab_file,)








"""simple docstring"""


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


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

__lowerCamelCase      :List[str]	    =		{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													='''realm'''

     def __init__(	self:						Union[str, Any]     ,		__a:						List[Any]=30_522     ,		__a:						List[Any]=768     ,		__a:						List[Any]=128     ,		__a:						Union[str, Any]=12     ,		__a:						Union[str, Any]=12     ,		__a:						Optional[Any]=8     ,		__a:						Dict=3_072     ,		__a:						List[Any]="gelu_new"     ,		__a:						List[Any]=0.1     ,		__a:						Tuple=0.1     ,		__a:						Optional[Any]=512     ,		__a:						Optional[int]=2     ,		__a:						str=0.02     ,		__a:						int=1e-1_2     ,		__a:						Optional[Any]=256     ,		__a:						Any=10     ,		__a:						Dict=1e-3     ,		__a:						Optional[Any]=5     ,		__a:						Dict=320     ,		__a:						Tuple=13_353_718     ,		__a:						List[Any]=5_000     ,		__a:						Dict=1     ,		__a:						int=0     ,		__a:						Dict=2     ,		**__a:						List[str]     ,		)->    Any:
           super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

           # Common config
           lowerCamelCase					:					Optional[Any]        =	vocab_size
           lowerCamelCase					:					str        =	max_position_embeddings
           lowerCamelCase					:					Dict        =	hidden_size
           lowerCamelCase					:					Dict        =	retriever_proj_size
           lowerCamelCase					:					Optional[Any]        =	num_hidden_layers
           lowerCamelCase					:					List[str]        =	num_attention_heads
           lowerCamelCase					:					Tuple        =	num_candidates
           lowerCamelCase					:					int        =	intermediate_size
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					List[str]        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Optional[int]        =	initializer_range
           lowerCamelCase					:					Dict        =	type_vocab_size
           lowerCamelCase					:					Optional[Any]        =	layer_norm_eps

           # Reader config
           lowerCamelCase					:					List[str]        =	span_hidden_size
           lowerCamelCase					:					Dict        =	max_span_width
           lowerCamelCase					:					Optional[Any]        =	reader_layer_norm_eps
           lowerCamelCase					:					Optional[int]        =	reader_beam_size
           lowerCamelCase					:					List[Any]        =	reader_seq_len

           # Retrieval config
           lowerCamelCase					:					int        =	num_block_records
           lowerCamelCase					:					Dict        =	searcher_beam_size




