






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


lowercase  :							Optional[Any]			  =    logging.get_logger(__name__)

lowercase  :							Dict			  =    {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowercase  :							Optional[Any]			  =    {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

lowercase  :							Tuple			  =    {
    '''camembert-base''': 5_12,
}

lowercase  :							List[Any]			  =    '''▁'''




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					VOCAB_FILES_NAMES
						A     :   Optional[Any]						=					PRETRAINED_VOCAB_FILES_MAP
						A     :   Dict						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A     :   str						=					['input_ids', 'attention_mask']






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

								snake_case_				:							List[str]						 =       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_SCREAMING_SNAKE_CASE		,				eos_token=_SCREAMING_SNAKE_CASE		,				unk_token=_SCREAMING_SNAKE_CASE		,				sep_token=_SCREAMING_SNAKE_CASE		,				cls_token=_SCREAMING_SNAKE_CASE		,				pad_token=_SCREAMING_SNAKE_CASE		,				mask_token=_SCREAMING_SNAKE_CASE		,				additional_special_tokens=_SCREAMING_SNAKE_CASE		,				sp_model_kwargs=self.sp_model_kwargs		,				**_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							int						 =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(str(_SCREAMING_SNAKE_CASE				)				)
								snake_case_				:							Dict						 =       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>
								snake_case_				:							Optional[int]						 =       {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
								snake_case_				:							Optional[Any]						 =       len(self.fairseq_tokens_to_ids				)
								snake_case_				:							str						 =       len(self.sp_model				) + len(self.fairseq_tokens_to_ids				)
								snake_case_				:							str						 =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:

								if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								snake_case_				:							Optional[Any]						 =       [self.cls_token_id]
								snake_case_				:							Union[str, Any]						 =       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False				) ->				List[int]:
								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 [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
								return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:
								snake_case_				:							Dict						 =       [self.sep_token_id]
								snake_case_				:							Any						 =       [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       _lowerCAmelCase       (	self				) ->				Dict:
								return len(self.fairseq_tokens_to_ids				) + len(self.sp_model				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Dict						 =       {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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.sp_model.encode(_SCREAMING_SNAKE_CASE		,				out_type=_SCREAMING_SNAKE_CASE				)






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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								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		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								snake_case_				:							Dict						 =       []
								snake_case_				:							int						 =       ""
								snake_case_				:							List[Any]						 =       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(_SCREAMING_SNAKE_CASE				) + token
												snake_case_				:							Optional[Any]						 =       True
												snake_case_				:							List[str]						 =       []
										else:
												current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
												snake_case_				:							Union[str, Any]						 =       False
								out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
								return out_string.strip()






						def __getstate__(	self				) ->				int:
								snake_case_				:							List[str]						 =       self.__dict__.copy()
								snake_case_				:							List[Any]						 =       None
								return state






						def __setstate__(	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								snake_case_				:							Tuple						 =       d

								# for backward compatibility
								if not hasattr(self		,				"sp_model_kwargs"				):
										snake_case_				:							Optional[int]						 =       {}

								snake_case_				:							int						 =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(self.vocab_file				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				Tuple[str]:
								if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
										return
								snake_case_				:							Tuple						 =       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,)








from typing import TYPE_CHECKING

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


lowercase  :							int			  =    {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''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

							lowercase  :							Optional[int]			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
