
import math

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


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1						,    a__ = 1						,    a__ = 1.0e4						,    a__ = False						,    a__ = 1.0						,    )       ->  jnp.ndarray:
				assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
				assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
				__a										=    float(embedding_dim // 2  )
				__a										=    math.log(max_timescale / min_timescale  ) / (num_timescales - freq_shift)
				__a										=    min_timescale * jnp.exp(jnp.arange(a__						,    dtype=jnp.floataa  ) * -log_timescale_increment  )
				__a										=    jnp.expand_dims(a__						,    1  ) * jnp.expand_dims(a__						,    0  )

				# scale embeddings
				__a										=    scale * emb

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







class 			__A( nn.Module			):
				snake_case_						   =    3_2
				snake_case_						   =    jnp.floataa




				@nn.compact
				def __call__(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    nn.Dense(self.time_embed_dim		,     dtype=self.dtype		,     name='''linear_1'''				)(_snake_case				)
								__a										=    nn.silu(_snake_case				)
								__a										=    nn.Dense(self.time_embed_dim		,     dtype=self.dtype		,     name='''linear_2'''				)(_snake_case				)
								return temb







class 			__A( nn.Module			):
				snake_case_						   =    3_2
				snake_case_						   =    False
				snake_case_						   =    1




				@nn.compact
				def __call__(     self		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




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

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=a			)
class 			__A( a			):
				snake_case_						   =    field(default='''language-modeling'''  ,   metadata={'''include_in_asdict_even_if_is_default''': True}			)

				snake_case_						   =    Features({'''text''': Value('''string'''			)}			)
				snake_case_						   =    Features({}			)
				snake_case_						   =    "text"




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict[str, str]:


								'''simple docstring'''




								return {self.text_column: "text"}