






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


A										=      logging.get_logger(__name__)

A										=      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A										=      {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

A										=      {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

A										=      {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    VOCAB_FILES_NAMES
		UpperCAmelCase__   :					Any					     =    PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__   :					Any					     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__   :					List[Any]					     =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
		UpperCAmelCase__   :					Optional[int]					     =    DebertaTokenizer


		def __init__(    self			,     UpperCamelCase__=None			,     UpperCamelCase__=None			,     UpperCamelCase__=None			,     UpperCamelCase__="replace"			,     UpperCamelCase__="[CLS]"			,     UpperCamelCase__="[SEP]"			,     UpperCamelCase__="[SEP]"			,     UpperCamelCase__="[CLS]"			,     UpperCamelCase__="[UNK]"			,     UpperCamelCase__="[PAD]"			,     UpperCamelCase__="[MASK]"			,     UpperCamelCase__=False			,     **UpperCamelCase__			,     ):
						super().__init__(
						    UpperCamelCase__			,     UpperCamelCase__			,     tokenizer_file=UpperCamelCase__			,     errors=UpperCamelCase__			,     bos_token=UpperCamelCase__			,     eos_token=UpperCamelCase__			,     unk_token=UpperCamelCase__			,     sep_token=UpperCamelCase__			,     cls_token=UpperCamelCase__			,     pad_token=UpperCamelCase__			,     mask_token=UpperCamelCase__			,     add_prefix_space=UpperCamelCase__			,     **UpperCamelCase__			,     )
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= kwargs.pop('add_bos_token'			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
						if pre_tok_state.get('add_prefix_space'			,     UpperCamelCase__							) != add_prefix_space:
										SCREAMING_SNAKE_CASE_ :						Tuple    				= getattr(UpperCamelCase__			,     pre_tok_state.pop('type'							)							)
										SCREAMING_SNAKE_CASE_ :						Tuple    				= add_prefix_space
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= pre_tok_class(**UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= add_prefix_space


		@property
		def 	__snake_case      (    self							):
						if self._mask_token is None:
										if self.verbose:
														logger.error('Using mask_token, but it is not set yet.'							)
										return None
						return str(self._mask_token							)


		@mask_token.setter
		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else value
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= value


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None							):
						if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= [self.cls_token_id]
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= [self.sep_token_id]
						SCREAMING_SNAKE_CASE_ :						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 	__snake_case      (    self			,     *UpperCamelCase__			,     **UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= kwargs.get('is_split_into_words'			,     UpperCamelCase__							)
						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*UpperCamelCase__			,     **UpperCamelCase__							)


		def 	__snake_case      (    self			,     *UpperCamelCase__			,     **UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= kwargs.get('is_split_into_words'			,     UpperCamelCase__							)

						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*UpperCamelCase__			,     **UpperCamelCase__							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= self._tokenizer.model.save(UpperCamelCase__			,     name=UpperCamelCase__							)
						return tuple(UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= []
						for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							) + [self.eos_token_id]							)

						if len(UpperCamelCase__							) > self.model_max_length:
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= input_ids[-self.model_max_length :]
						return input_ids







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 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					str					     =    """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__			,     ):
						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}'''							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= image_size
						SCREAMING_SNAKE_CASE_ :						int    				= spectrogram_length
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= frequency_length
						SCREAMING_SNAKE_CASE_ :						Any    				= image_patch_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= audio_patch_size
						SCREAMING_SNAKE_CASE_ :						int    				= num_image_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_audio_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_frames

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

						SCREAMING_SNAKE_CASE_ :						str    				= decoder_num_attention_heads
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= decoder_hidden_size
						SCREAMING_SNAKE_CASE_ :						Any    				= decoder_num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= decoder_intermediate_size
						SCREAMING_SNAKE_CASE_ :						List[str]    				= pixel_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= audio_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= audio_mask_type

						SCREAMING_SNAKE_CASE_ :						Any    				= task_matching
						SCREAMING_SNAKE_CASE_ :						Any    				= task_mae
						SCREAMING_SNAKE_CASE_ :						int    				= loss_type