





import math

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


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a = 1				,	__a = 1				,	__a = 1.0E4				,	__a = False				,	__a = 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"""
	snake_case_						:     Optional[Any]     =		float(embedding_dim // 2				)
	snake_case_						:     Optional[Any]     =		math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
	snake_case_						:     Tuple     =		min_timescale * jnp.exp(jnp.arange(__a				,	dtype=jnp.floataa				) * -log_timescale_increment				)
	snake_case_						:     Union[str, Any]     =		jnp.expand_dims(__a				,	1				) * jnp.expand_dims(__a				,	0				)

	# scale embeddings
	snake_case_						:     Optional[int]     =		scale * emb

	if flip_sin_to_cos:
		snake_case_						:     Any     =		jnp.concatenate([jnp.cos(__a				), jnp.sin(__a				)]				,	axis=1				)
	else:
		snake_case_						:     Union[str, Any]     =		jnp.concatenate([jnp.sin(__a				), jnp.cos(__a				)]				,	axis=1				)
	snake_case_						:     List[str]     =		jnp.reshape(__a				,	[jnp.shape(__a				)[0], embedding_dim]				)
	return signal
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):
						__magic_name__:     int	 =    32
						__magic_name__:     jnp.dtype	 =    jnp.floataa






						@nn.compact
						def __call__( self : List[Any]						,				_A : int		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		nn.Dense(self.time_embed_dim						,				dtype=self.dtype						,				name='linear_1'		)(_A		)
							snake_case_						:     Optional[Any]     =		nn.silu(_A		)
							snake_case_						:     List[str]     =		nn.Dense(self.time_embed_dim						,				dtype=self.dtype						,				name='linear_2'		)(_A		)
							return temb
class SCREAMING_SNAKE_CASE_  (   nn.Module   ):
						__magic_name__:     int	 =    32
						__magic_name__:     bool	 =    False
						__magic_name__:     float	 =    1






						@nn.compact
						def __call__( self : List[str]						,				_A : Optional[Any]		)							->      int:







							"""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 OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MCTCTForCTC""",
						    """MCTCTModel""",
						    """MCTCTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

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

