





"""simple docstring"""






import pytest

import datasets


# Import fixture modules as plugins
A			      =	['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']

def      __A   (      a_    :Dict ,							a_    :Optional[Any])		->		Optional[Any]:
				# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
				for item in items:
								if any(marker in item.keywords for marker in ['''integration''', '''unit''']):
												continue
								item.add_marker(pytest.mark.unit)

def      __A   (      a_    :Dict)		->		Union[str, Any]:
				config.addinivalue_line('''markers''' ,							'''torchaudio_latest: mark test to run with torchaudio>=0.12''')

@pytest.fixture(autouse=a_)
def      __A   (      a_    :Union[str, Any] ,							a_    :int)		->		Any:
				# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
				__a			:    List[str]      							=	tmp_path_factory.getbasetemp() / '''cache'''
				__a			:    Union[str, Any]      							=	test_hf_cache_home / '''datasets'''
				__a			:    Tuple      							=	test_hf_cache_home / '''metrics'''
				__a			:    Tuple      							=	test_hf_cache_home / '''modules'''
				monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' ,							str(a_))
				monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' ,							str(a_))
				monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' ,							str(a_))
				__a			:    List[str]      							=	test_hf_datasets_cache / '''downloads'''
				monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' ,							str(a_))
				__a			:    str      							=	test_hf_datasets_cache / '''downloads''' / '''extracted'''
				monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' ,							str(a_))

@pytest.fixture(autouse=a_ ,							scope='''session''')
def      __A   (      )		->		Optional[int]:
				datasets.disable_progress_bar()

@pytest.fixture(autouse=a_)
def      __A   (      a_    :int)		->		Any:
				# don't take tests into account when counting downloads
				monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' ,							a_)






@pytest.fixture
def      __A   (      a_    :Optional[int])		->		Optional[int]:
				# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
				# To be removed once SQLAlchemy 2.0 supported
				monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' ,							a_)


'''simple docstring'''





import math

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






def 				_lowerCAmelCase			(   __magic_name__						:     jnp.ndarray   , __magic_name__						:     int   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1.0E4   , __magic_name__						:     bool = False   , __magic_name__						:     float = 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'''
						lowercase :							int    		=float(embedding_dim // 2						)
						lowercase :							Optional[int]    		=math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						lowercase :							Any    		=min_timescale * jnp.exp(jnp.arange(__magic_name__   , dtype=jnp.floataa						) * -log_timescale_increment						)
						lowercase :							List[Any]    		=jnp.expand_dims(__magic_name__   , 1						) * jnp.expand_dims(__magic_name__   , 0						)

						# scale embeddings
						lowercase :							Tuple    		=scale * emb

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





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    jnp.floataa







				@nn.compact
				def __call__(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_1'''   )(UpperCAmelCase__   )
										lowercase :							Any    		=nn.silu(UpperCAmelCase__   )
										lowercase :							int    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_2'''   )(UpperCAmelCase__   )
										return temb





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    1







				@nn.compact
				def __call__(       self     :		int		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return get_sinusoidal_embeddings(
										    UpperCAmelCase__		,					embedding_dim=self.dim		,					flip_sin_to_cos=self.flip_sin_to_cos		,					freq_shift=self.freq_shift   )





