"""simple docstring"""


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


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

lowerCAmelCase_:       Union[str, Any]			     =       {
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}






class  a__     ( _a						):
   snake_case_				=   "tvlt"


   def __init__(  self,					_UpperCAmelCase=224,					_UpperCAmelCase=2048,					_UpperCAmelCase=128,					_UpperCAmelCase=[16, 16],					_UpperCAmelCase=[16, 16],					_UpperCAmelCase=3,					_UpperCAmelCase=1,					_UpperCAmelCase=8,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-6,					_UpperCAmelCase=True,					_UpperCAmelCase=False,					_UpperCAmelCase=16,					_UpperCAmelCase=512,					_UpperCAmelCase=8,					_UpperCAmelCase=2048,					_UpperCAmelCase=0.75,					_UpperCAmelCase=0.15,					_UpperCAmelCase="frame-level",					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase="classification",					**_UpperCAmelCase,					):
        '''simple docstring'''



        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}'''	)

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

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

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

        lowercase__								=				task_matching
        lowercase__								=				task_mae
        lowercase__								=				loss_type





"""simple docstring"""


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

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

lowerCAmelCase_:       Optional[int]			     =       {"vocab_file": "tokenizer.model"}

lowerCAmelCase_:       Optional[int]			     =       {
    "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",
    },
}
lowerCAmelCase_:       Optional[int]			     =       {
    "hf-internal-testing/llama-tokenizer": 2_0_4_8,
}
lowerCAmelCase_:       str			     =       "▁"

lowerCAmelCase_     ,				lowerCAmelCase_:       str			     =       "[INST]", "[/INST]"
lowerCAmelCase_     ,				lowerCAmelCase_:       Dict			     =       "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowerCAmelCase_:       List[Any]			     =       "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."






class  a__     ( _a						):
   snake_case_				=   VOCAB_FILES_NAMES
   snake_case_				=   PRETRAINED_VOCAB_FILES_MAP
   snake_case_				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_				=   ["input_ids", "attention_mask"]


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase="<unk>",					_UpperCAmelCase="<s>",					_UpperCAmelCase="</s>",					_UpperCAmelCase=None,					_UpperCAmelCase = None,					_UpperCAmelCase=True,					_UpperCAmelCase=False,					_UpperCAmelCase=False,					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				{} if sp_model_kwargs is None else sp_model_kwargs
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else bos_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else eos_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else unk_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else pad_token
        super().__init__(
            bos_token=_UpperCAmelCase,					eos_token=_UpperCAmelCase,					unk_token=_UpperCAmelCase,					pad_token=_UpperCAmelCase,					add_bos_token=_UpperCAmelCase,					add_eos_token=_UpperCAmelCase,					sp_model_kwargs=self.sp_model_kwargs,					clean_up_tokenization_spaces=_UpperCAmelCase,					legacy=_UpperCAmelCase,					**_UpperCAmelCase,					)
        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"	)
        lowercase__								=				legacy
        lowercase__								=				vocab_file
        lowercase__								=				add_bos_token
        lowercase__								=				add_eos_token
        lowercase__								=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(_UpperCAmelCase	)


   def __getstate__(  self	):
        '''simple docstring'''



        lowercase__								=				self.__dict__.copy()
        lowercase__								=				None
        lowercase__								=				self.sp_model.serialized_model_proto()
        return state


   def __setstate__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



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


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.sp_model.get_piece_size()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				{self.convert_ids_to_tokens(_UpperCAmelCase	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab


   def 						snake_case__						(  self,					_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if not self.legacy:
             lowercase__								=				text.startswith(_UpperCAmelCase	)
             if is_first:
                  lowercase__								=				text[1:]

        lowercase__								=				self.sp_model.encode(_UpperCAmelCase,					out_type=_UpperCAmelCase	)

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


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        return self.sp_model.piece_to_id(_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.sp_model.IdToPiece(_UpperCAmelCase	)
        return token


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				[]
        lowercase__								=				""
        lowercase__								=				False
        for i, token in enumerate(_UpperCAmelCase	):
             # 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(_UpperCAmelCase	) + token
                  lowercase__								=				True
                  lowercase__								=				[]
             else:
                  current_sub_tokens.append(_UpperCAmelCase	)
                  lowercase__								=				False
        out_string += self.sp_model.decode(_UpperCAmelCase	)
        return out_string


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



        if not os.path.isdir(_UpperCAmelCase	):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
             return
        lowercase__								=				os.path.join(
            _UpperCAmelCase,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
                  lowercase__								=				self.sp_model.serialized_model_proto()
                  fi.write(_UpperCAmelCase	)

        return (out_vocab_file,)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase=None	):
        '''simple docstring'''



        lowercase__								=				[self.bos_token_id] if self.add_bos_token else []
        lowercase__								=				[self.eos_token_id] if self.add_eos_token else []

        lowercase__								=				bos_token_id + token_ids_a + eos_token_id

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

        return output


   def 						snake_case__						(  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	)

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

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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



        lowercase__								=				[self.bos_token_id] if self.add_bos_token else []
        lowercase__								=				[self.eos_token_id] if self.add_eos_token else []

        lowercase__								=				[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 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				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...)"	)

        lowercase__								=				[]
        if len(conversation.past_user_inputs	) > 0:
             if not conversation.past_user_inputs[0].startswith(_UpperCAmelCase	) or E_SYS not in conversation.past_user_inputs[0]:
                  lowercase__								=				(
                      B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                  )
        elif not dialogue[0][1].startswith(_UpperCAmelCase	) or E_SYS not in dialogue[0][1]:
             lowercase__								=				(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=_UpperCAmelCase	)
                + [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=_UpperCAmelCase	)
        return dialog_tokens




