'''simple docstring'''


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


__lowerCAmelCase	:       Dict						       =	logging.get_logger(__name__)

__lowerCAmelCase	:       List[Any]						       =	{"vocab_file": "sentencepiece.bpe.model"}

__lowerCAmelCase	:       str						       =	{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

__lowerCAmelCase	:       Optional[Any]						       =	{
    "camembert-base": 512,
}

__lowerCAmelCase	:       str						       =	"▁"






class 						A   (   UpperCAmelCase   ):
							a_        =      VOCAB_FILES_NAMES
							a_        =      PRETRAINED_VOCAB_FILES_MAP
							a_        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a_        =      ['''input_ids''', '''attention_mask''']





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

								__UpperCAmelCase							=			{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__a     ,		eos_token=__a     ,		unk_token=__a     ,		sep_token=__a     ,		cls_token=__a     ,		pad_token=__a     ,		mask_token=__a     ,		additional_special_tokens=__a     ,		sp_model_kwargs=self.sp_model_kwargs     ,		**__a     ,		)
								__UpperCAmelCase							=			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
								self.sp_model.Load(str(__a	)	)
								__UpperCAmelCase							=			vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								__UpperCAmelCase							=			{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
								__UpperCAmelCase							=			len(self.fairseq_tokens_to_ids	)
								__UpperCAmelCase							=			len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
								__UpperCAmelCase							=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							List[int]     ,		__a				:							Optional[List[int]] = None	)							->	List[int]:

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





							def       snake_case__						(      self				:							List[str]     ,		__a				:							List[int]     ,		__a				:							Optional[List[int]] = None     ,		__a				:							bool = False	)							->	List[int]:
								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 [1] + ([0] * len(__a	)) + [1]
								return [1] + ([0] * len(__a	)) + [1, 1] + ([0] * len(__a	)) + [1]





							def       snake_case__						(      self				:							int     ,		__a				:							List[int]     ,		__a				:							Optional[List[int]] = None	)							->	List[int]:
								__UpperCAmelCase							=			[self.sep_token_id]
								__UpperCAmelCase							=			[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       snake_case__						(      self				:							Dict	)							->	str:
								return len(self.fairseq_tokens_to_ids	) + len(self.sp_model	)





							def       snake_case__						(      self				:							int	)							->	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       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							str	)							->	List[str]:
								return self.sp_model.encode(__a     ,		out_type=__a	)





							def       snake_case__						(      self				:							int     ,		__a				:							Union[str, Any]	)							->	Union[str, Any]:
								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(__a	) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(__a	)





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							List[Any]	)							->	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				:							Dict     ,		__a				:							Union[str, Any]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			[]
								__UpperCAmelCase							=			''''''
								__UpperCAmelCase							=			False
								for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
										if not prev_is_special:
											out_string += " "
										out_string += self.sp_model.decode(__a	) + token
										__UpperCAmelCase							=			True
										__UpperCAmelCase							=			[]
									else:
										current_sub_tokens.append(__a	)
										__UpperCAmelCase							=			False
								out_string += self.sp_model.decode(__a	)
								return out_string.strip()





							def __getstate__(      self				:							Optional[Any]	)							->	Tuple:
								__UpperCAmelCase							=			self.__dict__.copy()
								__UpperCAmelCase							=			None
								return state





							def __setstate__(      self				:							List[Any]     ,		__a				:							Optional[int]	)							->	Tuple:
								__UpperCAmelCase							=			d

								# 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       snake_case__						(      self				:							Any     ,		__a				:							str     ,		__a				:							Optional[str] = None	)							->	Tuple[str]:
								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,)

'''simple docstring'''


from typing import TYPE_CHECKING

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


__lowerCAmelCase	:       List[Any]						       =	{
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	[
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

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