


"""simple docstring"""





import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	int	):
      assert isinstance(_snake_case       ,      _snake_case	)
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''       ,      [False, True]	)
def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	Dict       ,      _snake_case :	Tuple       ,      _snake_case :	Optional[Any]	):
      lowerCAmelCase    :		Optional[Any]				=      tmp_path / '''cache'''
      lowerCAmelCase    :		str				=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            lowerCAmelCase    :		int				=      SqlDatasetReader(
                '''dataset'''       ,      '''sqlite:///''' + sqlite_path       ,      cache_dir=_snake_case       ,      keep_in_memory=_snake_case	).read()
      _check_sql_dataset(_snake_case       ,      _snake_case	)


@require_sqlalchemy
@pytest.mark.parametrize(
    '''features'''       ,      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]       ,      )
def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Optional[int]       ,      _snake_case :	Optional[int]       ,      _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      tmp_path / '''cache'''
      lowerCAmelCase    :		List[Any]				=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      lowerCAmelCase    :		str				=      features.copy() if features else default_expected_features
      lowerCAmelCase    :		Union[str, Any]				=      (
          Features({feature: Value(_snake_case	) for feature, dtype in features.items()}	) if features is not None else None
      )
      lowerCAmelCase    :		List[Any]				=      SqlDatasetReader('''dataset'''       ,      '''sqlite:///''' + sqlite_path       ,      features=_snake_case       ,      cache_dir=_snake_case	).read()
      _check_sql_dataset(_snake_case       ,      _snake_case	)


def    _snake_case  (  _snake_case :	Union[str, Any]	):
      with contextlib.closing(sqlitea.connect(_snake_case	)	) as con:
            lowerCAmelCase    :		List[str]				=      con.cursor()
            cur.execute('''SELECT * FROM dataset'''	)
            for row in cur:
                  yield row


@require_sqlalchemy
def    _snake_case  (  _snake_case :	List[str]       ,      _snake_case :	Tuple       ,      _snake_case :	Optional[int]	):
      lowerCAmelCase    :		Any				=      tmp_path / '''cache'''
      lowerCAmelCase    :		List[Any]				=      os.path.join(_snake_case       ,      '''tmp.sql'''	)
      lowerCAmelCase    :		Any				=      SqlDatasetReader('''dataset'''       ,      '''sqlite:///''' + sqlite_path       ,      cache_dir=_snake_case	).read()
      SqlDatasetWriter(_snake_case       ,      '''dataset'''       ,      '''sqlite:///''' + output_sqlite_path       ,      num_proc=1	).write()

      lowerCAmelCase    :		Optional[Any]				=      iter_sql_file(_snake_case	)
      lowerCAmelCase    :		Dict				=      iter_sql_file(_snake_case	)

      for rowa, rowa in zip(_snake_case       ,      _snake_case	):
            assert rowa == rowa


@require_sqlalchemy
def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Optional[Any]       ,      _snake_case :	int	):
      lowerCAmelCase    :		Union[str, Any]				=      tmp_path / '''cache'''
      lowerCAmelCase    :		Dict				=      os.path.join(_snake_case       ,      '''tmp.sql'''	)
      lowerCAmelCase    :		Union[str, Any]				=      SqlDatasetReader('''dataset'''       ,      '''sqlite:///''' + sqlite_path       ,      cache_dir=_snake_case	).read()
      SqlDatasetWriter(_snake_case       ,      '''dataset'''       ,      '''sqlite:///''' + output_sqlite_path       ,      num_proc=2	).write()

      lowerCAmelCase    :		Any				=      iter_sql_file(_snake_case	)
      lowerCAmelCase    :		str				=      iter_sql_file(_snake_case	)

      for rowa, rowa in zip(_snake_case       ,      _snake_case	):
            assert rowa == rowa






@require_sqlalchemy
def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Optional[Any]       ,      _snake_case :	str	):
      lowerCAmelCase    :		Union[str, Any]				=      tmp_path / '''cache'''
      lowerCAmelCase    :		List[Any]				=      os.path.join(_snake_case       ,      '''tmp.sql'''	)
      lowerCAmelCase    :		Union[str, Any]				=      SqlDatasetReader('''dataset'''       ,      '''sqlite:///''' + sqlite_path       ,      cache_dir=_snake_case	).read()
      with pytest.raises(_snake_case	):
            SqlDatasetWriter(_snake_case       ,      '''dataset'''       ,      '''sqlite:///''' + output_sqlite_path       ,      num_proc=0	).write()




"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		(("""eta""", 0.0), ("""num_inference_steps""", 50))







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    {
         "num_train_timesteps": 1000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**lowercase_  )
     return config







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_scheduler_config(**lowercase_  )
     UpperCAmelCase_					:					int			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    10, 0.0

     UpperCAmelCase_					:					Optional[int]			     =    self.dummy_model()
     UpperCAmelCase_					:					str			     =    self.dummy_sample_deter

     scheduler.set_timesteps(lowercase_  )

     for t in scheduler.timesteps:
        UpperCAmelCase_					:					Dict			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    scheduler.step(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     return sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config(steps_offset=1  )
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
     scheduler.set_timesteps(5  )
     assert torch.equal(scheduler.timesteps				,   torch.LongTensor([801, 601, 401, 201, 1]  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,   [0.0_02, 0.02, 0.2, 2]  ):
        self.check_over_configs(beta_start=lowercase_				,   beta_end=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.check_over_configs(thresholding=lowercase_  )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowercase_				,   prediction_type=lowercase_				,   sample_max_value=lowercase_				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 500]  ):
        self.check_over_forward(time_step=lowercase_				,   num_inference_steps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, eta in zip([1, 10, 49]				,   [0.0, 0.5, 1.0]  ):
        self.check_over_forward(time_step=lowercase_				,   eta=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420				,   400  ) - 0.1_47_71  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980				,   960  ) - 0.3_24_60  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487				,   486  ) - 0.0_09_79  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999				,   998  ) - 0.02  )  ) < 1E-5







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Optional[int]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    10, 0.0
     scheduler.set_timesteps(lowercase_  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.dummy_model()
     UpperCAmelCase_					:					List[str]			     =    self.dummy_sample_deter
     UpperCAmelCase_					:					Any			     =    self.dummy_sample_deter + 0.1
     UpperCAmelCase_					:					int			     =    self.dummy_sample_deter - 0.1

     UpperCAmelCase_					:					List[Any]			     =    samplea.shape[0]
     UpperCAmelCase_					:					int			     =    torch.stack([samplea, samplea, samplea]				,   dim=0  )
     UpperCAmelCase_					:					int			     =    torch.arange(lowercase_  )[0:3, None].repeat(1				,   lowercase_  )

     UpperCAmelCase_					:					int			     =    model(samples.flatten(0				,   1  )				,   timesteps.flatten(0				,   1  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    scheduler.batch_step_no_noise(lowercase_				,   timesteps.flatten(0				,   1  )				,   samples.flatten(0				,   1  )				,   lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
     assert abs(result_mean.item() - 0.49_82  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.full_loop()

     UpperCAmelCase_					:					int			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					List[str]			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
     assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(prediction_type="v_prediction"  )

     UpperCAmelCase_					:					str			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 52.53_02  ) < 1E-2
     assert abs(result_mean.item() - 0.06_84  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Dict			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Tuple			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
     assert abs(result_mean.item() - 0.19_51  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					int			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
     assert abs(result_mean.item() - 0.19_41  ) < 1E-3


