





'''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


a__							:							Dict =logging.get_logger(__name__)

a__							:							Optional[Any] ='''▁'''

a__							:							List[str] ={'''vocab_file''': '''prophetnet.tokenizer'''}

a__							:							List[str] ={
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

a__							:							List[Any] ={
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

a__							:							Any ={
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}
def 				lowercase__   (   __lowercase						:  List[str]     ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       =  collections.OrderedDict()
       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as reader:
              __UpperCamelCase       =  reader.readlines()
       for index, token in enumerate(__lowercase     ):
              __UpperCamelCase       =  token.rstrip('\n'     )
              __UpperCamelCase       =  index
       return vocab


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					Dict					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					str					=["input_ids", "attention_mask"]






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

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

        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

        __UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(__A	)	)
        __UpperCamelCase       =  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
        __UpperCamelCase       =  {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

        for i in range(1_0	):
               __UpperCamelCase       =  f'''[unused{i}]'''
               __UpperCamelCase       =  5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        __UpperCamelCase       =  1_2
        __UpperCamelCase       =  {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(__A	)






 def __getstate__(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  self.__dict__.copy()
        __UpperCamelCase       =  None
        return state






 def __setstate__(       self       :							Any							,	__A       :							Dict	):
        __UpperCamelCase       =  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'	):
               __UpperCamelCase       =  {}

        __UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(self.vocab_file	)






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

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

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






 def        _lowerCamelCase					(       self       :							int							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):
        __UpperCamelCase       =  [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        _lowerCamelCase					(       self       :							Optional[Any]	):
        return len(self.sp_model	) + self.fairseq_offset






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  {self.convert_ids_to_tokens(__A	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab






 def        _lowerCamelCase					(       self       :							int							,	__A       :							str	):
        return self.sp_model.encode(__A							,	out_type=__A	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							Tuple	):
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        __UpperCamelCase       =  self.sp_model.PieceToId(__A	)

        # 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        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							List[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        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Optional[int]	):
        __UpperCamelCase       =  ''.join(__A	).replace(__A							,	' '	).strip()
        return out_string






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							str							,	__A       :							Optional[str] = None	):
        if not os.path.isdir(__A	):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
               return
        __UpperCamelCase       =  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	) and os.path.isfile(self.vocab_file	):
               copyfile(self.vocab_file							,	__A	)
        elif not os.path.isfile(self.vocab_file	):
               with open(__A							,	'wb'	) as fi:
                      __UpperCamelCase       =  self.sp_model.serialized_model_proto()
                      fi.write(__A	)

        return (out_vocab_file,)






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCAmelCase		:  int	=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  List[str]	=   [
         "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "SwinForImageClassification",
         "SwinForMaskedImageModeling",
         "SwinModel",
         "SwinPreTrainedModel",
         "SwinBackbone",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFSwinForImageClassification",
         "TFSwinForMaskedImageModeling",
         "TFSwinModel",
         "TFSwinPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swin import (
              SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
              SwinModel,
              SwinPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_swin import (
              TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
              TFSwinModel,
              TFSwinPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  int	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



