





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Optional[Any]      					=(UniPCMultistepScheduler,)
  a_							:       Tuple      					=(("""num_inference_steps""", 25),)







  def UpperCamelCase_ (      self      :					int      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	{
        'num_train_timesteps': 10_00,
        'beta_start': 0.00_01,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
        'solver_order': 2,
        'solver_type': 'bh2',
    }

    config.update(**UpperCamelCase							)
    return config







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					str=0      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	dict(self.forward_default_kwargs							)
    _snake_case   :				Optional[int]														=	kwargs.pop('num_inference_steps'      ,				UpperCamelCase							)
    _snake_case   :				str														=	self.dummy_sample
    _snake_case   :				Tuple														=	0.1 * sample
    _snake_case   :				Dict														=	[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
      _snake_case   :				int														=	self.get_scheduler_config(**UpperCamelCase							)
      _snake_case   :				List[Any]														=	scheduler_class(**UpperCamelCase							)
      scheduler.set_timesteps(UpperCamelCase							)
      # copy over dummy past residuals
      _snake_case   :				Dict														=	dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCamelCase							)
        _snake_case   :				Optional[Any]														=	scheduler_class.from_pretrained(UpperCamelCase							)
        new_scheduler.set_timesteps(UpperCamelCase							)
        # copy over dummy past residuals
        _snake_case   :				List[Any]														=	dummy_past_residuals[: new_scheduler.config.solver_order]

      _snake_case							,					_snake_case   :				Optional[int]														=	sample, sample
      for t in range(UpperCamelCase      ,				time_step + scheduler.config.solver_order + 1							):
        _snake_case   :				int														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				**UpperCamelCase							).prev_sample
        _snake_case   :				List[Any]														=	new_scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				**UpperCamelCase							).prev_sample

        assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					int=0      ,				**UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				str														=	dict(self.forward_default_kwargs							)
    _snake_case   :				Optional[Any]														=	kwargs.pop('num_inference_steps'      ,				UpperCamelCase							)
    _snake_case   :				int														=	self.dummy_sample
    _snake_case   :				Dict														=	0.1 * sample
    _snake_case   :				int														=	[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
      _snake_case   :				List[Any]														=	self.get_scheduler_config()
      _snake_case   :				List[str]														=	scheduler_class(**UpperCamelCase							)
      scheduler.set_timesteps(UpperCamelCase							)

      # copy over dummy past residuals (must be after setting timesteps)
      _snake_case   :				str														=	dummy_past_residuals[: scheduler.config.solver_order]

      with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCamelCase							)
        _snake_case   :				List[str]														=	scheduler_class.from_pretrained(UpperCamelCase							)
        # copy over dummy past residuals
        new_scheduler.set_timesteps(UpperCamelCase							)

        # copy over dummy past residual (must be after setting timesteps)
        _snake_case   :				Optional[int]														=	dummy_past_residuals[: new_scheduler.config.solver_order]

      _snake_case   :				Dict														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				**UpperCamelCase							).prev_sample
      _snake_case   :				Tuple														=	new_scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				**UpperCamelCase							).prev_sample

      assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"







  def UpperCamelCase_ (      self      :					str      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    if scheduler is None:
      _snake_case   :				List[Any]														=	self.scheduler_classes[0]
      _snake_case   :				Optional[Any]														=	self.get_scheduler_config(**UpperCamelCase							)
      _snake_case   :				Optional[int]														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				int														=	self.scheduler_classes[0]
    _snake_case   :				Any														=	self.get_scheduler_config(**UpperCamelCase							)
    _snake_case   :				Any														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				List[Any]														=	10
    _snake_case   :				Optional[int]														=	self.dummy_model()
    _snake_case   :				List[str]														=	self.dummy_sample_deter
    scheduler.set_timesteps(UpperCamelCase							)

    for i, t in enumerate(scheduler.timesteps							):
      _snake_case   :				Union[str, Any]														=	model(UpperCamelCase      ,				UpperCamelCase							)
      _snake_case   :				Optional[int]														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							).prev_sample

    return sample







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	dict(self.forward_default_kwargs							)

    _snake_case   :				Optional[int]														=	kwargs.pop('num_inference_steps'      ,				UpperCamelCase							)

    for scheduler_class in self.scheduler_classes:
      _snake_case   :				str														=	self.get_scheduler_config()
      _snake_case   :				Tuple														=	scheduler_class(**UpperCamelCase							)

      _snake_case   :				Union[str, Any]														=	self.dummy_sample
      _snake_case   :				Dict														=	0.1 * sample

      if num_inference_steps is not None and hasattr(UpperCamelCase      ,				'set_timesteps'							):
        scheduler.set_timesteps(UpperCamelCase							)
      elif num_inference_steps is not None and not hasattr(UpperCamelCase      ,				'set_timesteps'							):
        _snake_case   :				Dict														=	num_inference_steps

      # copy over dummy past residuals (must be done after set_timesteps)
      _snake_case   :				Any														=	[residual + 0.2, residual + 0.15, residual + 0.10]
      _snake_case   :				int														=	dummy_past_residuals[: scheduler.config.solver_order]

      _snake_case   :				Union[str, Any]														=	scheduler.timesteps[5]
      _snake_case   :				str														=	scheduler.timesteps[6]

      _snake_case   :				Any														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				**UpperCamelCase							).prev_sample
      _snake_case   :				Any														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				**UpperCamelCase							).prev_sample

      self.assertEqual(output_a.shape      ,				sample.shape							)
      self.assertEqual(output_a.shape      ,				output_a.shape							)







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				str														=	UniPCMultistepScheduler(**self.get_scheduler_config()							)
    _snake_case   :				Optional[int]														=	self.full_loop(scheduler=UpperCamelCase							)
    _snake_case   :				Tuple														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_mean.item() - 0.24_64							) < 1e-3

    _snake_case   :				List[Any]														=	DPMSolverSinglestepScheduler.from_config(scheduler.config							)
    _snake_case   :				Union[str, Any]														=	DEISMultistepScheduler.from_config(scheduler.config							)
    _snake_case   :				List[str]														=	DPMSolverMultistepScheduler.from_config(scheduler.config							)
    _snake_case   :				Any														=	UniPCMultistepScheduler.from_config(scheduler.config							)

    _snake_case   :				int														=	self.full_loop(scheduler=UpperCamelCase							)
    _snake_case   :				List[Any]														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_mean.item() - 0.24_64							) < 1e-3







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    for timesteps in [25, 50, 1_00, 9_99, 10_00]:
      self.check_over_configs(num_train_timesteps=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    self.check_over_configs(thresholding=UpperCamelCase							)
    for order in [1, 2, 3]:
      for solver_type in ["bh1", "bh2"]:
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(
                thresholding=UpperCamelCase      ,				prediction_type=UpperCamelCase      ,				sample_max_value=UpperCamelCase      ,				solver_order=UpperCamelCase      ,				solver_type=UpperCamelCase      ,				)







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    for solver_type in ["bh1", "bh2"]:
      for order in [1, 2, 3]:
        for prediction_type in ["epsilon", "sample"]:
          self.check_over_configs(
              solver_order=UpperCamelCase      ,				solver_type=UpperCamelCase      ,				prediction_type=UpperCamelCase      ,				)
          _snake_case   :				Optional[int]														=	self.full_loop(
              solver_order=UpperCamelCase      ,				solver_type=UpperCamelCase      ,				prediction_type=UpperCamelCase      ,				)
          assert not torch.isnan(UpperCamelCase							).any(), "Samples have nan numbers"







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    self.check_over_configs(lower_order_final=UpperCamelCase							)
    self.check_over_configs(lower_order_final=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
      self.check_over_forward(num_inference_steps=UpperCamelCase      ,				time_step=0							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Dict														=	self.full_loop()
    _snake_case   :				Union[str, Any]														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_mean.item() - 0.24_64							) < 1e-3







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				str														=	self.full_loop(prediction_type='v_prediction'							)
    _snake_case   :				Dict														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_mean.item() - 0.10_14							) < 1e-3







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	self.scheduler_classes[0]
    _snake_case   :				int														=	self.get_scheduler_config(thresholding=UpperCamelCase      ,				dynamic_thresholding_ratio=0							)
    _snake_case   :				int														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Dict														=	10
    _snake_case   :				int														=	self.dummy_model()
    _snake_case   :				Optional[Any]														=	self.dummy_sample_deter.half()
    scheduler.set_timesteps(UpperCamelCase							)

    for i, t in enumerate(scheduler.timesteps							):
      _snake_case   :				Dict														=	model(UpperCamelCase      ,				UpperCamelCase							)
      _snake_case   :				int														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							).prev_sample

    assert sample.dtype == torch.floataa







  def UpperCamelCase_ (      self      :					int      ,				**UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    for scheduler_class in self.scheduler_classes:
      _snake_case   :				str														=	self.get_scheduler_config(**UpperCamelCase							)
      _snake_case   :				Dict														=	scheduler_class(**UpperCamelCase							)

      scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
      assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps











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							)




