




"""simple docstring"""




import math

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







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = 1	,				__UpperCamelCase = 1	,				__UpperCamelCase = 1.0e4	,				__UpperCamelCase = False	,				__UpperCamelCase = 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"""
			__lowercase			:  Dict								=       float(embedding_dim // 2	)
			__lowercase			:  Tuple								=       math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
			__lowercase			:  List[Any]								=       min_timescale * jnp.exp(jnp.arange(__UpperCamelCase	,				dtype=jnp.floataa	) * -log_timescale_increment	)
			__lowercase			:  Any								=       jnp.expand_dims(__UpperCamelCase	,				1	) * jnp.expand_dims(__UpperCamelCase	,				0	)

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

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

class   UpperCAmelCase_    (			nn.Module		):
		UpperCamelCase							=32
		UpperCamelCase							=jnp.floataa
		@nn.compact
		def __call__(						self				,						UpperCamelCase_       )						->       Optional[int]:
					__lowercase			:  Union[str, Any]								=       nn.Dense(self.time_embed_dim				,						dtype=self.dtype				,						name='''linear_1'''       )(UpperCamelCase_       )
					__lowercase			:  str								=       nn.silu(UpperCamelCase_       )
					__lowercase			:  Dict								=       nn.Dense(self.time_embed_dim				,						dtype=self.dtype				,						name='''linear_2'''       )(UpperCamelCase_       )
					return temb


class   UpperCAmelCase_    (			nn.Module		):
		UpperCamelCase							=32
		UpperCamelCase							=False
		UpperCamelCase							=1
		@nn.compact
		def __call__(						self				,						UpperCamelCase_       )						->       Optional[int]:
					return get_sinusoidal_embeddings(
					    UpperCamelCase_				,						embedding_dim=self.dim				,						flip_sin_to_cos=self.flip_sin_to_cos				,						freq_shift=self.freq_shift       )









'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    'split_dict'       ,[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42       ,dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]       ,)
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= split_dict._to_yaml_list()
			assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
			A_									= SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE_     )
			for split_name, split_info in split_dict.items():
						# dataset_name field is deprecated, and is therefore not part of the YAML dump
						A_									= None
						# the split name of split_dict takes over the name of the split info object
						A_									= split_name
			assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'       ,[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE_     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
			# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
			A_									= asdict(SplitDict({'train': split_info}     )     )
			assert "dataset_name" in split_dict_asdict["train"]
			assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name




