





import math

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


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

  # scale embeddings
  _snake_case   :				Dict														=	scale * emb

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



class        _lowerCAmelCase    (			nn.Module							):


  '''simple docstring'''
  a_							:       int      					=32
  a_							:       jnp.dtype      					=jnp.floataa







  @nn.compact
  def __call__(      self      :					int      ,				UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	nn.Dense(self.time_embed_dim      ,				dtype=self.dtype      ,				name='linear_1'							)(UpperCamelCase							)
    _snake_case   :				Optional[int]														=	nn.silu(UpperCamelCase							)
    _snake_case   :				List[Any]														=	nn.Dense(self.time_embed_dim      ,				dtype=self.dtype      ,				name='linear_2'							)(UpperCamelCase							)
    return temb




class        _lowerCAmelCase    (			nn.Module							):


  '''simple docstring'''
  a_							:       int      					=32
  a_							:       bool      					=False
  a_							:       float      					=1







  @nn.compact
  def __call__(      self      :					Optional[int]      ,				UpperCamelCase      :					List[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							)











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
      'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
      'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




