




import math

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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				jnp.ndarray      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				float = 1      ,      SCREAMING_SNAKE_CASE_							:				float = 1      ,      SCREAMING_SNAKE_CASE_							:				float = 1.0E4      ,      SCREAMING_SNAKE_CASE_							:				bool = False      ,      SCREAMING_SNAKE_CASE_							:				float = 1.0      ,      ):
					assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
					assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
					__lowerCAmelCase				   =       float(embedding_dim // 2     )
					__lowerCAmelCase				   =       math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
					__lowerCAmelCase				   =       min_timescale * jnp.exp(jnp.arange(SCREAMING_SNAKE_CASE_      ,      dtype=jnp.floataa     ) * -log_timescale_increment     )
					__lowerCAmelCase				   =       jnp.expand_dims(SCREAMING_SNAKE_CASE_      ,      1     ) * jnp.expand_dims(SCREAMING_SNAKE_CASE_      ,      0     )

					# scale embeddings
					__lowerCAmelCase				   =       scale * emb

					if flip_sin_to_cos:
										__lowerCAmelCase				   =       jnp.concatenate([jnp.cos(SCREAMING_SNAKE_CASE_     ), jnp.sin(SCREAMING_SNAKE_CASE_     )]      ,      axis=1     )
					else:
										__lowerCAmelCase				   =       jnp.concatenate([jnp.sin(SCREAMING_SNAKE_CASE_     ), jnp.cos(SCREAMING_SNAKE_CASE_     )]      ,      axis=1     )
					__lowerCAmelCase				   =       jnp.reshape(SCREAMING_SNAKE_CASE_      ,      [jnp.shape(SCREAMING_SNAKE_CASE_     )[0], embedding_dim]     )
					return signal
class 			a__    (					nn.Module      ):
						_a							:    int					=    3_2
						_a							:    jnp.dtype					=    jnp.floataa






						@nn.compact
						def __call__(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       nn.Dense(self.time_embed_dim		,			dtype=self.dtype		,			name="linear_1"			)(_A			)
											__lowerCAmelCase				   =       nn.silu(_A			)
											__lowerCAmelCase				   =       nn.Dense(self.time_embed_dim		,			dtype=self.dtype		,			name="linear_2"			)(_A			)
											return temb






class 			a__    (					nn.Module      ):
						_a							:    int					=    3_2
						_a							:    bool					=    False
						_a							:    float					=    1






						@nn.compact
						def __call__(   self		,			_A			):




											"""simple docstring"""



											return get_sinusoidal_embeddings(
											    _A		,			embedding_dim=self.dim		,			flip_sin_to_cos=self.flip_sin_to_cos		,			freq_shift=self.freq_shift			)










from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    			=			{"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					UpperCamelCase__    			=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




