


'''simple docstring'''




import math

import flax.linen as nn
import jax.numpy as jnp


def    UpperCAmelCase_  (       A		,	A		,	A = 1		,	A = 1		,	A = 1.0E4		,	A = False		,	A = 1.0		,	):
   '''simple docstring'''





   assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
   assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
   _a					:  Dict						=  float(embedding_dim // 2      )
   _a					:  Any						=  math.log(max_timescale / min_timescale      ) / (num_timescales - freq_shift)
   _a					:  List[Any]						=  min_timescale * jnp.exp(jnp.arange(A		,	dtype=jnp.floataa      ) * -log_timescale_increment      )
   _a					:  Any						=  jnp.expand_dims(A		,	1      ) * jnp.expand_dims(A		,	0      )

   # scale embeddings
   _a					:  Union[str, Any]						=  scale * emb

   if flip_sin_to_cos:
      _a					:  Any						=  jnp.concatenate([jnp.cos(A      ), jnp.sin(A      )]		,	axis=1      )
   else:
      _a					:  Union[str, Any]						=  jnp.concatenate([jnp.sin(A      ), jnp.cos(A      )]		,	axis=1      )
   _a					:  List[str]						=  jnp.reshape(A		,	[jnp.shape(A      )[0], embedding_dim]      )
   return signal







class 					a			(				nn.Module  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		32
 __lowerCAmelCase						: jnp.dtype							  =		jnp.floataa




 @nn.compact
 def __call__(    self		,      lowerCamelCase_      )      ->    Any:
    _a					:  List[str]						=  nn.Dense(self.time_embed_dim		,      dtype=self.dtype		,      name='linear_1'      )(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  nn.silu(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  nn.Dense(self.time_embed_dim		,      dtype=self.dtype		,      name='linear_2'      )(lowerCamelCase_      )
    return temb







class 					a			(				nn.Module  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		32
 __lowerCAmelCase						: bool							  =		False
 __lowerCAmelCase						: float							  =		1




 @nn.compact
 def __call__(    self		,      lowerCamelCase_      )      ->    Any:
    return get_sinusoidal_embeddings(
        lowerCamelCase_		,      embedding_dim=self.dim		,      flip_sin_to_cos=self.flip_sin_to_cos		,      freq_shift=self.freq_shift      )






'''simple docstring'''




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		"""ClapFeatureExtractor"""
 __lowerCAmelCase						: str							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Union[str, Any]:
    super().__init__(lowerCamelCase_		,      lowerCamelCase_      )




 def __call__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      **lowerCamelCase_      )      ->    List[Any]:
    _a					:  Any						=  kwargs.pop('sampling_rate'		,      lowerCamelCase_      )

    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					:  Union[str, Any]						=  self.tokenizer(lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if audios is not None:
       _a					:  List[str]						=  self.feature_extractor(
           lowerCamelCase_		,      sampling_rate=lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if text is not None and audios is not None:
       _a					:  List[Any]						=  audio_features.input_features
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**lowerCamelCase_      )		,      tensor_type=lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Tuple:
    return self.tokenizer.batch_decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[str]:
    return self.tokenizer.decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 @property
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Dict						=  self.tokenizer.model_input_names
    _a					:  Tuple						=  self.feature_extractor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )


