




import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows




def     __magic_name__       (				A	:      Union[str, Any]     ):  # picklable for multiprocessing






			'''simple docstring'''







			return i + 1




@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def     __magic_name__       (				):






			'''simple docstring'''







			with parallel_backend("spark"     ):
						assert ParallelBackendConfig.backend_name == "spark"

			a					=    [1, 2, 3]
			with pytest.raises(_lowerCamelCase     ):
						with parallel_backend("unsupported backend"     ):
									map_nested(_lowerCamelCase,						_lowerCamelCase,						num_proc=2     )

			with pytest.raises(_lowerCamelCase     ):
						with parallel_backend("unsupported backend"     ):
									map_nested(_lowerCamelCase,						_lowerCamelCase,						num_proc=-1     )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc",						[2, -1]     )
def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			a					=    [1, 2]
			a					=    {"""a""": 1, """b""": 2}
			a					=    {"""a""": [1, 2], """b""": [3, 4]}
			a					=    {"""a""": {"""1""": 1}, """b""": 2}
			a					=    {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
			a					=    [2, 3]
			a					=    {"""a""": 2, """b""": 3}
			a					=    {"""a""": [2, 3], """b""": [4, 5]}
			a					=    {"""a""": {"""1""": 2}, """b""": 3}
			a					=    {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

			with parallel_backend("spark"     ):
						assert map_nested(_lowerCamelCase,						_lowerCamelCase,						num_proc=_lowerCamelCase     ) == expected_map_nested_sa
						assert map_nested(_lowerCamelCase,						_lowerCamelCase,						num_proc=_lowerCamelCase     ) == expected_map_nested_sa
						assert map_nested(_lowerCamelCase,						_lowerCamelCase,						num_proc=_lowerCamelCase     ) == expected_map_nested_sa
						assert map_nested(_lowerCamelCase,						_lowerCamelCase,						num_proc=_lowerCamelCase     ) == expected_map_nested_sa
						assert map_nested(_lowerCamelCase,						_lowerCamelCase,						num_proc=_lowerCamelCase     ) == expected_map_nested_sa






import math

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




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      float = 1.0,						):






			'''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"""
			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 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






