




'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""ClapFeatureExtractor"""
					_UpperCamelCase							=		("""RobertaTokenizer""", """RobertaTokenizerFast""")



					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						int)    ->							List[Any]:
								super().__init__(_lowercase      ,       _lowercase)



					def __call__(	self     :						int      ,       _lowercase     :						int=None      ,       _lowercase     :						List[Any]=None      ,       _lowercase     :						str=None      ,       **_lowercase     :						Optional[int])    ->							Any:
								A_									= kwargs.pop('sampling_rate'      ,       _lowercase)

								if text is None and audios is None:
											raise ValueError('You have to specify either text or audios. Both cannot be none.')

								if text is not None:
											A_									= self.tokenizer(_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase)

								if audios is not None:
											A_									= self.feature_extractor(
											    _lowercase      ,       sampling_rate=_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase)

								if text is not None and audios is not None:
											A_									= audio_features.input_features
											return encoding
								elif text is not None:
											return encoding
								else:
											return BatchEncoding(data=dict(**_lowercase)      ,       tensor_type=_lowercase)



					def     __snake_case						(	self     :						int      ,       *_lowercase     :						Tuple      ,       **_lowercase     :						Optional[int])    ->							Any:
								return self.tokenizer.batch_decode(*_lowercase      ,       **_lowercase)



					def     __snake_case						(	self     :						Tuple      ,       *_lowercase     :						Union[str, Any]      ,       **_lowercase     :						Optional[int])    ->							Any:
								return self.tokenizer.decode(*_lowercase      ,       **_lowercase)



					@property
					def     __snake_case						(	self     :						Optional[int])    ->							int:
								A_									= self.tokenizer.model_input_names
								A_									= self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))










'''simple docstring'''
import warnings

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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""mvp"""
					_UpperCamelCase							=		["""past_key_values"""]
					_UpperCamelCase							=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



					def __init__(	self     :						List[str]      ,       _lowercase     :						int=50_267      ,       _lowercase     :						str=1_024      ,       _lowercase     :						int=12      ,       _lowercase     :						int=4_096      ,       _lowercase     :						Tuple=16      ,       _lowercase     :						Optional[Any]=12      ,       _lowercase     :						Union[str, Any]=4_096      ,       _lowercase     :						List[Any]=16      ,       _lowercase     :						str=0.0      ,       _lowercase     :						List[str]=0.0      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Union[str, Any]=1_024      ,       _lowercase     :						str=0.1      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						Any=0.0      ,       _lowercase     :						List[str]=0.02      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						List[str]=False      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						Any=1      ,       _lowercase     :						Optional[Any]=0      ,       _lowercase     :						Dict=2      ,       _lowercase     :						int=True      ,       _lowercase     :						Any=2      ,       _lowercase     :						Tuple=2      ,       _lowercase     :						Optional[Any]=False      ,       _lowercase     :						List[str]=100      ,       _lowercase     :						List[str]=800      ,       **_lowercase     :						Dict      ,       )    ->							Dict:
								A_									= vocab_size
								A_									= max_position_embeddings
								A_									= d_model
								A_									= encoder_ffn_dim
								A_									= encoder_layers
								A_									= encoder_attention_heads
								A_									= decoder_ffn_dim
								A_									= decoder_layers
								A_									= decoder_attention_heads
								A_									= dropout
								A_									= attention_dropout
								A_									= activation_dropout
								A_									= activation_function
								A_									= init_std
								A_									= encoder_layerdrop
								A_									= decoder_layerdrop
								A_									= classifier_dropout
								A_									= use_cache
								A_									= encoder_layers
								A_									= scale_embedding  # scale factor will be sqrt(d_model) if True
								A_									= use_prompt
								A_									= prompt_length
								A_									= prompt_mid_dim

								super().__init__(
								    pad_token_id=_lowercase      ,       bos_token_id=_lowercase      ,       eos_token_id=_lowercase      ,       is_encoder_decoder=_lowercase      ,       decoder_start_token_id=_lowercase      ,       forced_eos_token_id=_lowercase      ,       **_lowercase      ,       )

								if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'      ,       _lowercase):
											A_									= self.bos_token_id
											warnings.warn(
											    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
											    'The config can simply be saved and uploaded again to be fixed.')




