
'''simple docstring'''





import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   "▁"

UpperCAmelCase    					=   {"vocab_file": "prophetnet.tokenizer"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

UpperCAmelCase    					=   {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

UpperCAmelCase    					=   {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}

def      A (       A_							:					str       ):
						snake_case       :     List[str] =    collections.OrderedDict()
						with open(A_    ,							'''r'''    ,							encoding='''utf-8'''       ) as reader:
												snake_case       :     Optional[Any] =    reader.readlines()
						for index, token in enumerate(A_       ):
												snake_case       :     str =    token.rstrip('''\n'''       )
												snake_case       :     List[Any] =    index
						return vocab





class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']




				def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Tuple="[SEP]",    SCREAMING_SNAKE_CASE_	:				Tuple="[SEP]",    SCREAMING_SNAKE_CASE_	:				Tuple="[SEP]",    SCREAMING_SNAKE_CASE_	:				Any="[UNK]",    SCREAMING_SNAKE_CASE_	:				Dict="[PAD]",    SCREAMING_SNAKE_CASE_	:				str="[CLS]",    SCREAMING_SNAKE_CASE_	:				List[str]="[MASK]",    SCREAMING_SNAKE_CASE_	:				Optional[Dict[str, Any]] = None,    **SCREAMING_SNAKE_CASE_	:				Optional[Any],    ):
										snake_case       :     List[Any] =    {} if sp_model_kwargs is None else sp_model_kwargs

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

										try:
																import sentencepiece as spm
										except ImportError:
																logger.warning(
																    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																    ''' pip install sentencepiece'''      )
																raise

										snake_case       :     Any =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(str(SCREAMING_SNAKE_CASE_      )      )
										snake_case       :     Optional[Any] =    vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# put special tokens and [unused] tokens into the vocab
										snake_case       :     Optional[int] =    {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

										for i in range(10      ):
																snake_case       :     Tuple =    F"""[unused{i}]"""
																snake_case       :     str =    5 + i

										# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
										snake_case       :     str =    12
										snake_case       :     Optional[int] =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
										for k in self.fairseq_tokens_to_ids.keys():
																self.unique_no_split_tokens.append(SCREAMING_SNAKE_CASE_      )




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




				def __setstate__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     Tuple =    d
										try:
																import sentencepiece as spm
										except ImportError:
																logger.warning(
																    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																    ''' pip install sentencepiece'''      )
																raise

										# for backward compatibility
										if not hasattr(self,    '''sp_model_kwargs'''      ):
																snake_case       :     Union[str, Any] =    {}

										snake_case       :     Optional[int] =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(self.vocab_file      )




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None,    SCREAMING_SNAKE_CASE_	:				bool = False      ):

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

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




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None      ):
										snake_case       :     Union[str, Any] =    [self.sep_token_id]

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




				@property
				def 	__snake_case							(							self	:				str      ):
										return len(self.sp_model      ) + self.fairseq_offset




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     int =    {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_      ): i for i in range(self.vocab_size      )}
										vocab.update(self.added_tokens_encoder      )
										return vocab




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				str      ):
										return self.sp_model.encode(SCREAMING_SNAKE_CASE_,    out_type=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										snake_case       :     Tuple =    self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_      )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     List[Any] =    ''''''.join(SCREAMING_SNAKE_CASE_      ).replace(SCREAMING_SNAKE_CASE_,    ''' '''      ).strip()
										return out_string




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
																return
										snake_case       :     str =    os.path.join(
										    SCREAMING_SNAKE_CASE_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

										if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE_      ) and os.path.isfile(self.vocab_file      ):
																copyfile(self.vocab_file,    SCREAMING_SNAKE_CASE_      )
										elif not os.path.isfile(self.vocab_file      ):
																with open(SCREAMING_SNAKE_CASE_,    '''wb'''      ) as fi:
																						snake_case       :     Any =    self.sp_model.serialized_model_proto()
																						fi.write(SCREAMING_SNAKE_CASE_      )

										return (out_vocab_file,)




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None      ):

										if token_ids_a is None:
																return token_ids_a + [self.sep_token_id]
										snake_case       :     str =    [self.sep_token_id]
										return token_ids_a + sep + token_ids_a + sep



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

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

