


"""simple docstring"""





import math

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


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

      # scale embeddings
      lowerCAmelCase					:	Any       =   scale * emb

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



class 		snake_case_(       nn.Module			):
     __UpperCamelCase  =					32
     __UpperCamelCase  =					jnp.floataa






     @nn.compact
     def __call__(	self  :					Tuple					,				UpperCamelCase_  :					Optional[int]				):
           lowerCAmelCase					:	str       =   nn.Dense(self.time_embed_dim					,				dtype=self.dtype					,				name='''linear_1'''				)(UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   nn.silu(UpperCamelCase_				)
           lowerCAmelCase					:	List[Any]       =   nn.Dense(self.time_embed_dim					,				dtype=self.dtype					,				name='''linear_2'''				)(UpperCamelCase_				)
           return temb



class 		snake_case_(       nn.Module			):
     __UpperCamelCase  =					32
     __UpperCamelCase  =					False
     __UpperCamelCase  =					1






     @nn.compact
     def __call__(	self  :					Any					,				UpperCamelCase_  :					List[str]				):
           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 os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


snake_case__		:     Optional[Any]    				=				namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[str]			):
      return (abs(source - target			) / target) < 0.01






@pytest.mark.integration
def      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   _TestCommandArgs(dataset=_snake_case ,   all_configs=_snake_case ,   save_infos=_snake_case			)
      lowerCAmelCase					:	str       =   TestCommand(*_snake_case			)
      test_command.run()
      lowerCAmelCase					:	str       =   os.path.join(_snake_case ,   '''README.md'''			)
      assert os.path.exists(_snake_case			)
      lowerCAmelCase					:	Tuple       =   DatasetInfosDict.from_directory(_snake_case			)
      lowerCAmelCase					:	List[str]       =   DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''			)			),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']			)			),
                          '''langs''': Sequence(Value('''string'''			)			),
                          '''spans''': Sequence(Value('''string'''			)			),
                      }			) ,   splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ] ,   download_size=3940680 ,   dataset_size=2589981 ,   )
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   getattr(dataset_infos['''default'''] ,   _snake_case			), getattr(expected_dataset_infos['''default'''] ,   _snake_case			)
            if key == "num_bytes":
                  assert is_apercent_close(_snake_case ,   _snake_case			)
            elif key == "splits":
                  assert list(_snake_case			) == list(_snake_case			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes			)
            else:
                  result == expected


