

import os
import re
import unicodedata
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							:     Tuple 						=	logging.get_logger(__name__)
__snake_case							:     Any 						=	{"""vocab_file""": """spiece.model"""}

__snake_case							:     int 						=	{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

__snake_case							:     Any 						=	{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

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



class    A__(a_    ):
						"""simple docstring"""






						_A  :							str   			=	VOCAB_FILES_NAMES
						_A  :							Optional[int]   			=	PRETRAINED_VOCAB_FILES_MAP
						_A  :							Dict   			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A  :							Optional[Any]   			=	['''input_ids''', '''token_type_ids''']





						def __init__(  self       ,     _lowercase       ,     _lowercase=False       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="<unk>"       ,     _lowercase="[SEP]"       ,     _lowercase="<pad>"       ,     _lowercase="[CLS]"       ,     _lowercase="[MASK]"       ,     _lowercase = None       ,     **_lowercase       ,     )      ->				None:
									# Mask token behave like a normal word, i.e. include the space before it and
									# is included in the raw text, there should be a match in a non-normalized sentence.
									a_		:		List[str]				    =				(
									    AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase       ,     normalized=_lowercase  )
									    if isinstance(_lowercase       ,     _lowercase  )
									    else mask_token
									)

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

									super().__init__(
									    do_lower_case=_lowercase       ,     remove_space=_lowercase       ,     keep_accents=_lowercase       ,     unk_token=_lowercase       ,     sep_token=_lowercase       ,     pad_token=_lowercase       ,     cls_token=_lowercase       ,     mask_token=_lowercase       ,     sp_model_kwargs=self.sp_model_kwargs       ,     **_lowercase       ,     )

									a_		:		Tuple				    =				do_lower_case
									a_		:		str				    =				remove_space
									a_		:		List[Any]				    =				keep_accents
									a_		:		Union[str, Any]				    =				vocab_file

									a_		:		Union[str, Any]				    =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(_lowercase  )





						@property
						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									return len(self.sp_model  )





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		str				    =				{self.convert_ids_to_tokens(_lowercase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab





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





						def __setstate__(  self       ,     _lowercase  )      ->				Dict:
									a_		:		int				    =				d

									# for backward compatibility
									if not hasattr(self       ,     """sp_model_kwargs"""  ):
												a_		:		List[str]				    =				{}

									a_		:		str				    =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(self.vocab_file  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[str]:
									if self.remove_space:
												a_		:		Tuple				    =				""" """.join(inputs.strip().split()  )
									else:
												a_		:		Union[str, Any]				    =				inputs
									a_		:		int				    =				outputs.replace("""``"""       ,     """\""""  ).replace("""''"""       ,     """\""""  )

									if not self.keep_accents:
												a_		:		Dict				    =				unicodedata.normalize("""NFKD"""       ,     _lowercase  )
												a_		:		Optional[int]				    =				"""""".join([c for c in outputs if not unicodedata.combining(_lowercase  )]  )
									if self.do_lower_case:
												a_		:		int				    =				outputs.lower()

									return outputs





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[str]:
									a_		:		Tuple				    =				self.preprocess_text(_lowercase  )
									a_		:		Optional[Any]				    =				self.sp_model.encode(_lowercase       ,     out_type=_lowercase  )
									a_		:		Optional[Any]				    =				[]
									for piece in pieces:
												if len(_lowercase  ) > 1 and piece[-1] == str(""","""  ) and piece[-2].isdigit():
															a_		:		List[Any]				    =				self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowercase       ,     """"""  )  )
															if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																		if len(cur_pieces[0]  ) == 1:
																					a_		:		Dict				    =				cur_pieces[1:]
																		else:
																					a_		:		Dict				    =				cur_pieces[0][1:]
															cur_pieces.append(piece[-1]  )
															new_pieces.extend(_lowercase  )
												else:
															new_pieces.append(_lowercase  )

									return new_pieces





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Dict:
									return self.sp_model.PieceToId(_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				int:
									return self.sp_model.IdToPiece(_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[Any]:
									a_		:		str				    =				[]
									a_		:		Any				    =				""""""
									a_		:		str				    =				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(_lowercase  ) + token
															a_		:		Optional[Any]				    =				True
															a_		:		Any				    =				[]
												else:
															current_sub_tokens.append(_lowercase  )
															a_		:		Optional[Any]				    =				False
									out_string += self.sp_model.decode(_lowercase  )
									return out_string.strip()





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = False       ,     _lowercase = None       ,     _lowercase = True       ,     **_lowercase       ,     )      ->				str:
									a_		:		Optional[Any]				    =				kwargs.pop("""use_source_tokenizer"""       ,     _lowercase  )

									a_		:		Tuple				    =				self.convert_ids_to_tokens(_lowercase       ,     skip_special_tokens=_lowercase  )

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									a_		:		List[str]				    =				[]
									a_		:		List[Any]				    =				[]
									for token in filtered_tokens:
												if skip_special_tokens and token in self.all_special_ids:
															continue
												if token in self.added_tokens_encoder:
															if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(_lowercase  )  )
																		a_		:		int				    =				[]
															sub_texts.append(_lowercase  )
												else:
															current_sub_text.append(_lowercase  )
									if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(_lowercase  )  )

									# Mimic the behavior of the Rust tokenizer:
									# No space after <unk>
									if spaces_between_special_tokens:
												a_		:		Any				    =				re.sub(r"""(<unk>) """       ,     r"""\1"""       ,     """ """.join(_lowercase  )  )
									else:
												a_		:		Optional[Any]				    =				"""""".join(_lowercase  )

									a_		:		Tuple				    =				(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
												a_		:		Optional[int]				    =				self.clean_up_tokenization(_lowercase  )
												return clean_text
									else:
												return text





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				List[int]:
									a_		:		List[str]				    =				[self.sep_token_id]
									a_		:		List[Any]				    =				[self.cls_token_id]
									if token_ids_a is None:
												return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = False  )      ->				List[int]:

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

									if token_ids_a is not None:
												return [1] + ([0] * len(_lowercase  )) + [1] + ([0] * len(_lowercase  )) + [1]
									return [1] + ([0] * len(_lowercase  )) + [1]





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				List[int]:
									a_		:		Dict				    =				[self.sep_token_id]
									a_		:		Tuple				    =				[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  ) * [0] + len(token_ids_a + sep  ) * [1]





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				Tuple[str]:
									if not os.path.isdir(_lowercase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
									a_		:		Optional[Any]				    =				os.path.join(
									    _lowercase       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(_lowercase  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file       ,     _lowercase  )
									elif not os.path.isfile(self.vocab_file  ):
												with open(_lowercase       ,     """wb"""  ) as fi:
															a_		:		int				    =				self.sp_model.serialized_model_proto()
															fi.write(_lowercase  )

									return (out_vocab_file,)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__snake_case							:     str 						=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	'''tvlt'''





						def __init__(  self       ,     _lowercase=224       ,     _lowercase=2_048       ,     _lowercase=128       ,     _lowercase=[16, 16]       ,     _lowercase=[16, 16]       ,     _lowercase=3       ,     _lowercase=1       ,     _lowercase=8       ,     _lowercase=768       ,     _lowercase=12       ,     _lowercase=12       ,     _lowercase=3_072       ,     _lowercase="gelu"       ,     _lowercase=0.0       ,     _lowercase=0.0       ,     _lowercase=0.0_2       ,     _lowercase=1e-6       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=16       ,     _lowercase=512       ,     _lowercase=8       ,     _lowercase=2_048       ,     _lowercase=0.7_5       ,     _lowercase=0.1_5       ,     _lowercase="frame-level"       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="classification"       ,     **_lowercase       ,     )      ->				Tuple:
									super().__init__(**_lowercase  )

									if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    F'''got {audio_mask_type}'''  )

									a_		:		int				    =				image_size
									a_		:		str				    =				spectrogram_length
									a_		:		int				    =				frequency_length
									a_		:		str				    =				image_patch_size
									a_		:		int				    =				audio_patch_size
									a_		:		Any				    =				num_image_channels
									a_		:		str				    =				num_audio_channels
									a_		:		int				    =				num_frames

									a_		:		Optional[int]				    =				hidden_size
									a_		:		Tuple				    =				num_hidden_layers
									a_		:		Any				    =				num_attention_heads
									a_		:		Union[str, Any]				    =				intermediate_size
									a_		:		Tuple				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		int				    =				attention_probs_dropout_prob
									a_		:		Any				    =				initializer_range
									a_		:		int				    =				layer_norm_eps
									a_		:		Union[str, Any]				    =				qkv_bias
									a_		:		str				    =				use_mean_pooling

									a_		:		List[Any]				    =				decoder_num_attention_heads
									a_		:		Optional[Any]				    =				decoder_hidden_size
									a_		:		Dict				    =				decoder_num_hidden_layers
									a_		:		Optional[Any]				    =				decoder_intermediate_size
									a_		:		Dict				    =				pixel_mask_ratio
									a_		:		int				    =				audio_mask_ratio
									a_		:		Optional[Any]				    =				audio_mask_type

									a_		:		Union[str, Any]				    =				task_matching
									a_		:		Dict				    =				task_mae
									a_		:		Union[str, Any]				    =				loss_type


