



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

import sentencepiece as spm

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


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__A       					=		logging.get_logger(__name__)

__A       					=		{"""vocab_file""": """tokenizer.model"""}

__A       					=		{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
__A       					=		{
    """hf-internal-testing/llama-tokenizer""": 2048,
}
__A       					=		"""▁"""

__A       					=		"""[INST]""", """[/INST]"""
__A       					=		"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
__A       					=		"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""






class 	lowercase		(				UpperCamelCase__):






	"""simple docstring"""


	a__   :      Dict	     =							VOCAB_FILES_NAMES
	a__   :      Optional[Any]	     =							PRETRAINED_VOCAB_FILES_MAP
	a__   :      Optional[Any]	     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__   :      Dict	     =							["""input_ids""", """attention_mask"""]




	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Dict="<unk>"		,						__UpperCAmelCase			:		int="<s>"		,						__UpperCAmelCase			:		Any="</s>"		,						__UpperCAmelCase			:		Dict=None		,						__UpperCAmelCase			:		str = None		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Tuple=False		,						__UpperCAmelCase			:		Any=True		,						**__UpperCAmelCase			:		Tuple		,						)    ->				Optional[Any]:
							UpperCAmelCase_=     {} if sp_model_kwargs is None else sp_model_kwargs
							UpperCAmelCase_=     AddedToken(lowerCAmelCase__		,						lstrip=lowerCAmelCase__		,						rstrip=lowerCAmelCase__  ) if isinstance(lowerCAmelCase__		,						lowerCAmelCase__  ) else bos_token
							UpperCAmelCase_=     AddedToken(lowerCAmelCase__		,						lstrip=lowerCAmelCase__		,						rstrip=lowerCAmelCase__  ) if isinstance(lowerCAmelCase__		,						lowerCAmelCase__  ) else eos_token
							UpperCAmelCase_=     AddedToken(lowerCAmelCase__		,						lstrip=lowerCAmelCase__		,						rstrip=lowerCAmelCase__  ) if isinstance(lowerCAmelCase__		,						lowerCAmelCase__  ) else unk_token
							UpperCAmelCase_=     AddedToken(lowerCAmelCase__		,						lstrip=lowerCAmelCase__		,						rstrip=lowerCAmelCase__  ) if isinstance(lowerCAmelCase__		,						lowerCAmelCase__  ) else pad_token
							super().__init__(
							    bos_token=lowerCAmelCase__		,						eos_token=lowerCAmelCase__		,						unk_token=lowerCAmelCase__		,						pad_token=lowerCAmelCase__		,						add_bos_token=lowerCAmelCase__		,						add_eos_token=lowerCAmelCase__		,						sp_model_kwargs=self.sp_model_kwargs		,						clean_up_tokenization_spaces=lowerCAmelCase__		,						legacy=lowerCAmelCase__		,						**lowerCAmelCase__		,						)
							if legacy:
													logger.warning_once(
													    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
													    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""  )
							UpperCAmelCase_=     legacy
							UpperCAmelCase_=     vocab_file
							UpperCAmelCase_=     add_bos_token
							UpperCAmelCase_=     add_eos_token
							UpperCAmelCase_=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(lowerCAmelCase__  )




	def __getstate__(			self			:		int  )    ->				int:
							UpperCAmelCase_=     self.__dict__.copy()
							UpperCAmelCase_=     None
							UpperCAmelCase_=     self.sp_model.serialized_model_proto()
							return state




	def __setstate__(			self			:		List[Any]		,						__UpperCAmelCase			:		str  )    ->				Union[str, Any]:
							UpperCAmelCase_=     d
							UpperCAmelCase_=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )




	@property
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				str:
							return self.sp_model.get_piece_size()




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Any:
							UpperCAmelCase_=     {self.convert_ids_to_tokens(lowerCAmelCase__  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Union[str, Any]		,						**__UpperCAmelCase			:		Optional[int]  )    ->				List[str]:




							if not self.legacy:
													UpperCAmelCase_=     SPIECE_UNDERLINE + text.replace(lowerCAmelCase__		,						""" """  )
							return super().tokenize(lowerCAmelCase__		,						**lowerCAmelCase__  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple		,						__UpperCAmelCase			:		List[str]  )    ->				List[Any]:




							if not self.legacy:
													UpperCAmelCase_=     text.startswith(lowerCAmelCase__  )
													if is_first:
																			UpperCAmelCase_=     text[1:]

							UpperCAmelCase_=     self.sp_model.encode(lowerCAmelCase__		,						out_type=lowerCAmelCase__  )

							if not self.legacy and not is_first and not text.startswith(""" """  ) and tokens[0].startswith(lowerCAmelCase__  ):
													UpperCAmelCase_=     ([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
							return tokens




	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		List[Any]  )    ->				Optional[Any]:
							return self.sp_model.piece_to_id(lowerCAmelCase__  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		int  )    ->				Optional[Any]:
							UpperCAmelCase_=     self.sp_model.IdToPiece(lowerCAmelCase__  )
							return token




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		int  )    ->				Union[str, Any]:
							UpperCAmelCase_=     []
							UpperCAmelCase_=     ""
							UpperCAmelCase_=     False
							for i, token in enumerate(lowerCAmelCase__  ):
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			if not prev_is_special and i != 0:
																									out_string += " "
																			out_string += self.sp_model.decode(lowerCAmelCase__  ) + token
																			UpperCAmelCase_=     True
																			UpperCAmelCase_=     []
													else:
																			current_sub_tokens.append(lowerCAmelCase__  )
																			UpperCAmelCase_=     False
							out_string += self.sp_model.decode(lowerCAmelCase__  )
							return out_string




	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		str = None  )    ->				Tuple[str]:




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

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

							return (out_vocab_file,)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		str=None  )    ->				str:
							UpperCAmelCase_=     [self.bos_token_id] if self.add_bos_token else []
							UpperCAmelCase_=     [self.eos_token_id] if self.add_eos_token else []

							UpperCAmelCase_=     bos_token_id + token_ids_a + eos_token_id

							if token_ids_a is not None:
													UpperCAmelCase_=     output + bos_token_id + token_ids_a + eos_token_id

							return output




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Union[str, Any] = None		,						__UpperCAmelCase			:		Union[str, Any] = False  )    ->				List[int]:




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

							UpperCAmelCase_=     [1] if self.add_bos_token else []
							UpperCAmelCase_=     [1] if self.add_eos_token else []

							if token_ids_a is None:
													return bos_token_id + ([0] * len(lowerCAmelCase__  )) + eos_token_id
							return (
							    bos_token_id
							    + ([0] * len(lowerCAmelCase__  ))
							    + eos_token_id
							    + bos_token_id
							    + ([0] * len(lowerCAmelCase__  ))
							    + eos_token_id
							)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Dict = None  )    ->				List[int]:
							UpperCAmelCase_=     [self.bos_token_id] if self.add_bos_token else []
							UpperCAmelCase_=     [self.eos_token_id] if self.add_eos_token else []

							UpperCAmelCase_=     [0] * len(bos_token_id + token_ids_a + eos_token_id  )

							if token_ids_a is not None:
													output += [1] * len(bos_token_id + token_ids_a + eos_token_id  )

							return output




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		List[Any]  )    ->				List[int]:
							UpperCAmelCase_=     list(conversation.iter_texts()  )
							if not all([is_user for is_user, msg in dialogue[::2]]  ) or not all(
							    [not is_user for is_user, msg in dialogue[1::2]]  ):
													raise ValueError(
													    """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""  )

							UpperCAmelCase_=     []
							if len(conversation.past_user_inputs  ) > 0:
													if not conversation.past_user_inputs[0].startswith(lowerCAmelCase__  ) or E_SYS not in conversation.past_user_inputs[0]:
																			UpperCAmelCase_=     (
																			    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																			)
							elif not dialogue[0][1].startswith(lowerCAmelCase__  ) or E_SYS not in dialogue[0][1]:
													UpperCAmelCase_=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

							dialog_tokens += sum(
							    [
							        [self.bos_token_id]
							        + self.encode(
							            F"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """		,						add_special_tokens=lowerCAmelCase__  )
							        + [self.eos_token_id]
							        for prompt, answer in zip(dialogue[::2]		,						dialogue[1::2]  )
							    ]		,						[]		,						)
							if not (dialogue[-1][0]):
													raise ValueError(F"""Last message must be from user, got {dialogue[-1]["role"]}"""  )
							dialog_tokens += [self.bos_token_id] + self.encode(
							    F"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""		,						add_special_tokens=lowerCAmelCase__  )
							return dialog_tokens









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


__A       					=		logging.get_logger(__name__)

__A       					=		{
    '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
}






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Tuple	     =							"tvlt"




	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		List[str]=2_048		,						__UpperCAmelCase			:		Tuple=128		,						__UpperCAmelCase			:		Union[str, Any]=[16, 16]		,						__UpperCAmelCase			:		str=[16, 16]		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=1		,						__UpperCAmelCase			:		Tuple=8		,						__UpperCAmelCase			:		Optional[Any]=768		,						__UpperCAmelCase			:		List[Any]=12		,						__UpperCAmelCase			:		Tuple=12		,						__UpperCAmelCase			:		List[str]=3_072		,						__UpperCAmelCase			:		List[Any]="gelu"		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		List[Any]=0.02		,						__UpperCAmelCase			:		Optional[int]=1E-6		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		List[Any]=False		,						__UpperCAmelCase			:		Union[str, Any]=16		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=8		,						__UpperCAmelCase			:		Dict=2_048		,						__UpperCAmelCase			:		Dict=0.75		,						__UpperCAmelCase			:		Union[str, Any]=0.15		,						__UpperCAmelCase			:		Any="frame-level"		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Union[str, Any]="classification"		,						**__UpperCAmelCase			:		Any		,						)    ->				Tuple:
							super().__init__(**__UpperCAmelCase  )

							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}"""  )

							UpperCAmelCase_=     image_size
							UpperCAmelCase_=     spectrogram_length
							UpperCAmelCase_=     frequency_length
							UpperCAmelCase_=     image_patch_size
							UpperCAmelCase_=     audio_patch_size
							UpperCAmelCase_=     num_image_channels
							UpperCAmelCase_=     num_audio_channels
							UpperCAmelCase_=     num_frames

							UpperCAmelCase_=     hidden_size
							UpperCAmelCase_=     num_hidden_layers
							UpperCAmelCase_=     num_attention_heads
							UpperCAmelCase_=     intermediate_size
							UpperCAmelCase_=     hidden_act
							UpperCAmelCase_=     hidden_dropout_prob
							UpperCAmelCase_=     attention_probs_dropout_prob
							UpperCAmelCase_=     initializer_range
							UpperCAmelCase_=     layer_norm_eps
							UpperCAmelCase_=     qkv_bias
							UpperCAmelCase_=     use_mean_pooling

							UpperCAmelCase_=     decoder_num_attention_heads
							UpperCAmelCase_=     decoder_hidden_size
							UpperCAmelCase_=     decoder_num_hidden_layers
							UpperCAmelCase_=     decoder_intermediate_size
							UpperCAmelCase_=     pixel_mask_ratio
							UpperCAmelCase_=     audio_mask_ratio
							UpperCAmelCase_=     audio_mask_type

							UpperCAmelCase_=     task_matching
							UpperCAmelCase_=     task_mae
							UpperCAmelCase_=     loss_type




