


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


_lowercase       		= logging.get_logger(__name__)

_lowercase       		= '''▁'''

_lowercase       		= {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase       		= {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

_lowercase       		= {
    '''facebook/xglm-564M''': 2048,
}

class __snake_case		( __lowerCamelCase ):





		"""simple docstring"""







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



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




								'''simple docstring'''

								lowerCAmelCase_	:     Optional[int]          = {} if sp_model_kwargs is None else sp_model_kwargs

								# Compatibility with the original tokenizer
								lowerCAmelCase_	:     Tuple          = 7
								lowerCAmelCase_	:     int          = [f'''<madeupword{i}>''' for i in range(self.num_madeup_words      )]

								lowerCAmelCase_	:     Optional[Any]          = kwargs.get("additional_special_tokens"						,[]      )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

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

								lowerCAmelCase_	:     List[str]          = spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(lowerCAmelCase__      )      )
								lowerCAmelCase_	:     Dict          = 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'

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								lowerCAmelCase_	:     List[str]          = 1

								# Mimic fairseq token-to-id alignment for the first 4 token
								lowerCAmelCase_	:     List[Any]          = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								lowerCAmelCase_	:     List[str]          = len(self.sp_model      )
								lowerCAmelCase_	:     Optional[int]          = {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words      )}
								self.fairseq_tokens_to_ids.update(lowerCAmelCase__      )

								lowerCAmelCase_	:     str          = {v: k for k, v in self.fairseq_tokens_to_ids.items()}



		def __getstate__(							self    :   Any      )      ->							int:




								'''simple docstring'''

								lowerCAmelCase_	:     Dict          = self.__dict__.copy()
								lowerCAmelCase_	:     Optional[int]          = None
								lowerCAmelCase_	:     int          = self.sp_model.serialized_model_proto()
								return state



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




								'''simple docstring'''

								lowerCAmelCase_	:     Tuple          = d

								# for backward compatibility
								if not hasattr(self						,"sp_model_kwargs"      ):
														lowerCAmelCase_	:     Any          = {}

								lowerCAmelCase_	:     Union[str, Any]          = spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )



		def  UpperCAmelCase_							(							self    :   int						,lowerCAmelCase__    :   List[int]						,lowerCAmelCase__    :   Optional[List[int]] = None      )      ->							List[int]:




								'''simple docstring'''

								if token_ids_a is None:
														return [self.sep_token_id] + token_ids_a
								lowerCAmelCase_	:     int          = [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a



		def  UpperCAmelCase_							(							self    :   List[Any]						,lowerCAmelCase__    :   List[int]						,lowerCAmelCase__    :   Optional[List[int]] = None						,lowerCAmelCase__    :   bool = False      )      ->							List[int]:




								'''simple docstring'''

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

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



		def  UpperCAmelCase_							(							self    :   Dict						,lowerCAmelCase__    :   List[int]						,lowerCAmelCase__    :   Optional[List[int]] = None      )      ->							List[int]:




								'''simple docstring'''

								lowerCAmelCase_	:     Optional[Any]          = [self.sep_token_id]

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



		@property
		def  UpperCAmelCase_							(							self    :   Any      )      ->							str:




								'''simple docstring'''

								return len(self.sp_model      ) + self.fairseq_offset + self.num_madeup_words



		def  UpperCAmelCase_							(							self    :   Dict      )      ->							Tuple:




								'''simple docstring'''

								lowerCAmelCase_	:     Optional[Any]          = {self.convert_ids_to_tokens(lowerCAmelCase__      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab



		def  UpperCAmelCase_							(							self    :   Union[str, Any]						,lowerCAmelCase__    :   str      )      ->							List[str]:




								'''simple docstring'''

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



		def  UpperCAmelCase_							(							self    :   Optional[Any]						,lowerCAmelCase__    :   Any      )      ->							List[Any]:




								'''simple docstring'''

								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								lowerCAmelCase_	:     Tuple          = self.sp_model.PieceToId(lowerCAmelCase__      )

								# 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  UpperCAmelCase_							(							self    :   Tuple						,lowerCAmelCase__    :   Tuple      )      ->							Tuple:




								'''simple docstring'''

								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  UpperCAmelCase_							(							self    :   List[Any]						,lowerCAmelCase__    :   List[str]      )      ->							List[Any]:




								'''simple docstring'''

								lowerCAmelCase_	:     Union[str, Any]          = "".join(lowerCAmelCase__      ).replace(lowerCAmelCase__						," "      ).strip()
								return out_string



		def  UpperCAmelCase_							(							self    :   Tuple						,lowerCAmelCase__    :   str						,lowerCAmelCase__    :   Optional[str] = None      )      ->							Tuple[str]:




								'''simple docstring'''

								if not os.path.isdir(lowerCAmelCase__      ):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
														return
								lowerCAmelCase_	:     Optional[Any]          = 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__      ) and os.path.isfile(self.vocab_file      ):
														copyfile(self.vocab_file						,lowerCAmelCase__      )
								elif not os.path.isfile(self.vocab_file      ):
														with open(lowerCAmelCase__						,"wb"      ) as fi:
																				lowerCAmelCase_	:     int          = self.sp_model.serialized_model_proto()
																				fi.write(lowerCAmelCase__      )

								return (out_vocab_file,)




from typing import TYPE_CHECKING

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


__lowerCAmelCase					   =       {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['DebertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'DebertaForMaskedLM',
							    'DebertaForQuestionAnswering',
							    'DebertaForSequenceClassification',
							    'DebertaForTokenClassification',
							    'DebertaModel',
							    'DebertaPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFDebertaForMaskedLM',
							    'TFDebertaForQuestionAnswering',
							    'TFDebertaForSequenceClassification',
							    'TFDebertaForTokenClassification',
							    'TFDebertaModel',
							    'TFDebertaPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)