


'''simple docstring'''







import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""
    a									=					(ScoreSdeVeScheduler,)
    a									=					()


    @property
    def      A					(						self    :							Optional[Any]):
           _A  :			Dict											=      4
           _A  :			List[Any]											=      3
           _A  :			Optional[Any]											=      8
           _A  :			Optional[int]											=      8

           _A  :			List[str]											=      torch.rand((batch_size, num_channels, height, width))

           return sample


    @property
    def      A					(						self    :							Dict):
           _A  :			Optional[int]											=      4
           _A  :			List[str]											=      3
           _A  :			Tuple											=      8
           _A  :			List[Any]											=      8

           _A  :			str											=      batch_size * num_channels * height * width
           _A  :			str											=      torch.arange(__A)
           _A  :			Union[str, Any]											=      sample.reshape(__A   ,							__A   ,							__A   ,							__A)
           _A  :			Dict											=      sample / num_elems
           _A  :			Union[str, Any]											=      sample.permute(3   ,							0   ,							1   ,							2)

           return sample


    def      A					(						self    :							Optional[Any]):







           def model(SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							*SCREAMING_SNAKE_CASE    :							List[Any]):
                  return sample * t / (t + 1)

           return model


    def      A					(						self    :							Union[str, Any]   ,							**SCREAMING_SNAKE_CASE    :							Tuple):
           _A  :			Any											=      {
               "num_train_timesteps": 2000,
               "snr": 0.15,
               "sigma_min": 0.01,
               "sigma_max": 1348,
               "sampling_eps": 1e-5,
           }

           config.update(**__A)
           return config


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Any=0   ,							**SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			str											=      dict(self.forward_default_kwargs)

           for scheduler_class in self.scheduler_classes:
                  _A  :			int											=      self.dummy_sample
                  _A  :			str											=      0.1 * sample

                  _A  :			str											=      self.get_scheduler_config(**__A)
                  _A  :			int											=      scheduler_class(**__A)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(__A)
                         _A  :			Optional[int]											=      scheduler_class.from_pretrained(__A)

                  _A  :			Any											=      scheduler.step_pred(
                      __A   ,							__A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample
                  _A  :			Dict											=      new_scheduler.step_pred(
                      __A   ,							__A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample

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

                  _A  :			Optional[Any]											=      scheduler.step_correct(__A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample
                  _A  :			Any											=      new_scheduler.step_correct(
                      __A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample

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


    def      A					(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Any=0   ,							**SCREAMING_SNAKE_CASE    :							Optional[Any]):
           _A  :			Any											=      dict(self.forward_default_kwargs)
           kwargs.update(__A)

           for scheduler_class in self.scheduler_classes:
                  _A  :			Tuple											=      self.dummy_sample
                  _A  :			Union[str, Any]											=      0.1 * sample

                  _A  :			List[str]											=      self.get_scheduler_config()
                  _A  :			Optional[Any]											=      scheduler_class(**__A)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(__A)
                         _A  :			Tuple											=      scheduler_class.from_pretrained(__A)

                  _A  :			List[str]											=      scheduler.step_pred(
                      __A   ,							__A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample
                  _A  :			Optional[int]											=      new_scheduler.step_pred(
                      __A   ,							__A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample

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

                  _A  :			int											=      scheduler.step_correct(__A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample
                  _A  :			int											=      new_scheduler.step_correct(
                      __A   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample

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


    def      A					(						self    :							str):







           for timesteps in [10, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=__A)


    def      A					(						self    :							Optional[Any]):







           for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]   ,							[1, 100, 1000]):
                  self.check_over_configs(sigma_min=__A   ,							sigma_max=__A)


    def      A					(						self    :							str):







           for t in [0.1, 0.5, 0.75]:
                  self.check_over_forward(time_step=__A)


    def      A					(						self    :							Optional[Any]):
           _A  :			List[str]											=      dict(self.forward_default_kwargs)

           _A  :			Union[str, Any]											=      self.scheduler_classes[0]
           _A  :			Any											=      self.get_scheduler_config()
           _A  :			int											=      scheduler_class(**__A)

           _A  :			Tuple											=      3

           _A  :			List[Any]											=      self.dummy_model()
           _A  :			Dict											=      self.dummy_sample_deter

           scheduler.set_sigmas(__A)
           scheduler.set_timesteps(__A)
           _A  :			List[str]											=      torch.manual_seed(0)

           for i, t in enumerate(scheduler.timesteps):
                  _A  :			str											=      scheduler.sigmas[i]

                  for _ in range(scheduler.config.correct_steps):
                         with torch.no_grad():
                                _A  :			Any											=      model(__A   ,							__A)
                         _A  :			Optional[Any]											=      scheduler.step_correct(__A   ,							__A   ,							generator=__A   ,							**__A).prev_sample

                  with torch.no_grad():
                         _A  :			Union[str, Any]											=      model(__A   ,							__A)

                  _A  :			Tuple											=      scheduler.step_pred(__A   ,							__A   ,							__A   ,							generator=__A   ,							**__A)
                  _A  :			int											=      output.prev_sample, output.prev_sample_mean

           _A  :			List[str]											=      torch.sum(torch.abs(__A))
           _A  :			Tuple											=      torch.mean(torch.abs(__A))

           assert np.isclose(result_sum.item()   ,							14372758528.0)
           assert np.isclose(result_mean.item()   ,							18714530.0)


    def      A					(						self    :							List[Any]):
           _A  :			List[str]											=      dict(self.forward_default_kwargs)

           _A  :			str											=      kwargs.pop('num_inference_steps'   ,							__A)

           for scheduler_class in self.scheduler_classes:
                  _A  :			Optional[Any]											=      self.get_scheduler_config()
                  _A  :			Optional[int]											=      scheduler_class(**__A)

                  _A  :			int											=      self.dummy_sample
                  _A  :			Union[str, Any]											=      0.1 * sample

                  if num_inference_steps is not None and hasattr(__A   ,							'set_timesteps'):
                         scheduler.set_timesteps(__A)
                  elif num_inference_steps is not None and not hasattr(__A   ,							'set_timesteps'):
                         _A  :			Union[str, Any]											=      num_inference_steps

                  _A  :			Tuple											=      scheduler.step_pred(__A   ,							0   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample
                  _A  :			Optional[Any]											=      scheduler.step_pred(__A   ,							1   ,							__A   ,							generator=torch.manual_seed(0)   ,							**__A).prev_sample

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










'''simple docstring'''







from unittest.mock import patch

import datasets
from datasets import Dataset






def    lowerCAmelCase__ (       ):
       _A  :			Optional[int]											=      Dataset.from_dict({'col_1': [3, 2, 0, 1]}     )

       with patch('tqdm.auto.tqdm'     ) as mock_tqdm:
              datasets.disable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_not_called()

              mock_tqdm.reset_mock()

              datasets.enable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_called()






