import math

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







def  __UpperCamelCase    (						A ,							A ,							A = 1 ,							A = 1 ,							A = 1.0e4 ,							A = False ,							A = 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"
     UpperCamelCase__										=      float(embedding_dim // 2							)
     UpperCamelCase__										=      math.log(max_timescale / min_timescale							) / (num_timescales - freq_shift)
     UpperCamelCase__										=      min_timescale * jnp.exp(jnp.arange(A ,							dtype=jnp.floataa							) * -log_timescale_increment							)
     UpperCamelCase__										=      jnp.expand_dims(A ,							1							) * jnp.expand_dims(A ,							0							)

     # scale embeddings
     UpperCamelCase__										=      scale * emb

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



class     _A   (    nn.Module							):
  SCREAMING_SNAKE_CASE_	:	int						=32
  SCREAMING_SNAKE_CASE_	:	jnp.dtype						=jnp.floataa






  @nn.compact
  def __call__(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





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



class     _A   (    nn.Module							):
  SCREAMING_SNAKE_CASE_	:	int						=32
  SCREAMING_SNAKE_CASE_	:	bool						=False
  SCREAMING_SNAKE_CASE_	:	float						=1






  @nn.compact
  def __call__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





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




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__magic_name__								='''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
     raise ImportWarning(
         '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
     )

if version.parse(pyarrow.__version__).major < 8:
     raise ImportWarning(
         '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
         '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
     )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__magic_name__								=concatenate_datasets
__magic_name__								=DownloadConfig
__magic_name__								=DownloadManager
__magic_name__								=DownloadMode
__magic_name__								=DownloadConfig
__magic_name__								=DownloadMode
__magic_name__								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



