






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case      :      str             =       logging.get_logger(__name__)

__snake_case      :      List[Any]             =       """▁"""

__snake_case      :      Tuple             =       {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

__snake_case      :      List[Any]             =       {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

__snake_case      :      Dict             =       {"""vinai/bartpho-syllable""": 10_24}


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    int						=   VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE		:    Tuple						=   ['''input_ids''', '''attention_mask''']






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="<unk>"       ,  _UpperCamelCase="<pad>"       ,  _UpperCamelCase="<mask>"       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												# Mask token behave like a normal word, i.e. include the space before it
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else mask_token

												lowerCAmelCase__        =  {} if sp_model_kwargs is None else sp_model_kwargs

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

												lowerCAmelCase__        =  vocab_file
												lowerCAmelCase__        =  monolingual_vocab_file
												lowerCAmelCase__        =  spm.SentencePieceProcessor(**self.sp_model_kwargs				)
												self.sp_model.Load(str(_UpperCamelCase				)				)

												# Load the reduced vocab

												# Keep order of special tokens for backward compatibility
												lowerCAmelCase__        =  {}
												lowerCAmelCase__        =  0
												for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
																		if str(_UpperCamelCase				) not in self.fairseq_tokens_to_ids:
																								lowerCAmelCase__        =  cnt
																								cnt += 1
												with open(_UpperCamelCase       ,  'r'       ,  encoding='utf-8'				) as f:
																		for line in f.readlines():
																								lowerCAmelCase__        =  line.strip().split()[0]
																								lowerCAmelCase__        =  len(self.fairseq_tokens_to_ids				)
												if str(_UpperCamelCase				) not in self.fairseq_tokens_to_ids:
																		lowerCAmelCase__        =  len(self.fairseq_tokens_to_ids				)

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






						def __getstate__(		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.__dict__.copy()
												lowerCAmelCase__        =  None
												lowerCAmelCase__        =  self.sp_model.serialized_model_proto()
												return state






						def __setstate__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  d

												# for backward compatibility
												if not hasattr(self       ,  'sp_model_kwargs'				):
																		lowerCAmelCase__        =  {}

												lowerCAmelCase__        =  spm.SentencePieceProcessor(**self.sp_model_kwargs				)
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""

												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCAmelCase__        =  [self.cls_token_id]
												lowerCAmelCase__        =  [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = False				):






												"""simple docstring"""

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

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  [self.sep_token_id]
												lowerCAmelCase__        =  [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 + sep + token_ids_a + sep				) * [0]






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return len(self.fairseq_ids_to_tokens				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {self.convert_ids_to_tokens(_UpperCamelCase				): i for i in range(self.vocab_size				)}
												vocab.update(self.added_tokens_encoder				)
												return vocab






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.sp_model.encode(_UpperCamelCase       ,  out_type=_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
												else:
																		return self.unk_token_id






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.fairseq_ids_to_tokens[index]






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  ''.join(_UpperCamelCase				).replace(_UpperCamelCase       ,  ' '				).strip()
												return out_string







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												if not os.path.isdir(_UpperCamelCase				):
																		logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
																		return
												lowerCAmelCase__        =  os.path.join(
												    _UpperCamelCase       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
												lowerCAmelCase__        =  os.path.join(
												    _UpperCamelCase       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']       ,  )

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

												if os.path.abspath(self.monolingual_vocab_file				) != os.path.abspath(
												    _UpperCamelCase				) and os.path.isfile(self.monolingual_vocab_file				):
																		copyfile(self.monolingual_vocab_file       ,  _UpperCamelCase				)
												elif not os.path.isfile(self.monolingual_vocab_file				):
																		with open(_UpperCamelCase       ,  'w'       ,  encoding='utf-8'				) as fp:
																								for token in self.fairseq_tokens_to_ids:
																														if token not in self.all_special_tokens:
																																				fp.write(F"{str(_UpperCamelCase				)} \n"				)

												return out_vocab_file, out_monolingual_vocab_file













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case      :      Dict             =       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      str             =       [
					    """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FocalNetForImageClassification""",
					    """FocalNetForMaskedImageModeling""",
					    """FocalNetBackbone""",
					    """FocalNetModel""",
					    """FocalNetPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_focalnet import (
										    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FocalNetBackbone,
										    FocalNetForImageClassification,
										    FocalNetForMaskedImageModeling,
										    FocalNetModel,
										    FocalNetPreTrainedModel,
										)

else:
					import sys

					__snake_case      :      List[Any]             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





