






import math

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__ = 1	,snake_case__ = 1	,snake_case__ = 1.0e4	,snake_case__ = False	,snake_case__ = 1.0	,)							->  Any:






						"""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'
						_SCREAMING_SNAKE_CASE     				=  float(embedding_dim // 2		)
						_SCREAMING_SNAKE_CASE     				=  math.log(max_timescale / min_timescale		) / (num_timescales - freq_shift)
						_SCREAMING_SNAKE_CASE     				=  min_timescale * jnp.exp(jnp.arange(_a	,dtype=jnp.floataa		) * -log_timescale_increment		)
						_SCREAMING_SNAKE_CASE     				=  jnp.expand_dims(_a	,1		) * jnp.expand_dims(_a	,0		)

						# scale embeddings
						_SCREAMING_SNAKE_CASE     				=  scale * emb

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



class 				__UpperCAmelCase		(nn.Module   ):
			__snake_case    :				Optional[int]									=							32
			__snake_case    :				List[Any]									=							jnp.floataa







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







									'''simple docstring'''




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



class 				__UpperCAmelCase		(nn.Module   ):
			__snake_case    :				List[str]									=							32
			__snake_case    :				int									=							False
			__snake_case    :				Tuple									=							1







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







									'''simple docstring'''




									return get_sinusoidal_embeddings(
									    lowerCamelCase_				,     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


UpperCamelCase			    =     {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
		from .configuration_mmbt import MMBTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
		import sys

		UpperCamelCase			    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

