


'''simple docstring'''

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						(DPMSolverSinglestepScheduler,)
  _lowerCamelCase						=						(("num_inference_steps", 25),)






  def 						snake_case	(					self    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{
       "num_train_timesteps": 1000,
       "beta_start": 0.0_001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "solver_order": 2,
       "prediction_type": "epsilon",
       "thresholding": False,
       "sample_max_value": 1.0,
       "algorithm_type": "dpmsolver++",
       "solver_type": "midpoint",
       "lambda_min_clipped": -float("inf"  ),
       "variance_type": None,
   }

   config.update(**UpperCamelCase  )
   return config






  def 						snake_case	(					self    ,						UpperCamelCase=0    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						dict(self.forward_default_kwargs  )
   lowerCamelCase_  				=						kwargs.pop("num_inference_steps"    ,						UpperCamelCase  )
   lowerCamelCase_  				=						self.dummy_sample
   lowerCamelCase_  				=						0.1 * sample
   lowerCamelCase_  				=						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

    lowerCamelCase_				,lowerCamelCase_  				=						sample, sample
    for t in range(UpperCamelCase    ,						time_step + scheduler.config.solver_order + 1  ):
     lowerCamelCase_  				=						scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase  ).prev_sample
     lowerCamelCase_  				=						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 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self    ,						UpperCamelCase=0    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						dict(self.forward_default_kwargs  )
   lowerCamelCase_  				=						kwargs.pop("num_inference_steps"    ,						UpperCamelCase  )
   lowerCamelCase_  				=						self.dummy_sample
   lowerCamelCase_  				=						0.1 * sample
   lowerCamelCase_  				=						[residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
    lowerCamelCase_  				=						self.get_scheduler_config()
    lowerCamelCase_  				=						scheduler_class(**UpperCamelCase  )
    scheduler.set_timesteps(UpperCamelCase  )

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

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(UpperCamelCase  )
     lowerCamelCase_  				=						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)
     lowerCamelCase_  				=						dummy_past_residuals[: new_scheduler.config.solver_order]

    lowerCamelCase_  				=						scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase  ).prev_sample
    lowerCamelCase_  				=						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 						snake_case	(					self    ,						UpperCamelCase=None    ,						**UpperCamelCase  ):







   """simple docstring"""
   if scheduler is None:
    lowerCamelCase_  				=						self.scheduler_classes[0]
    lowerCamelCase_  				=						self.get_scheduler_config(**UpperCamelCase  )
    lowerCamelCase_  				=						scheduler_class(**UpperCamelCase  )

   lowerCamelCase_  				=						self.scheduler_classes[0]
   lowerCamelCase_  				=						self.get_scheduler_config(**UpperCamelCase  )
   lowerCamelCase_  				=						scheduler_class(**UpperCamelCase  )

   lowerCamelCase_  				=						10
   lowerCamelCase_  				=						self.dummy_model()
   lowerCamelCase_  				=						self.dummy_sample_deter
   scheduler.set_timesteps(UpperCamelCase  )

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

   return sample






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
   lowerCamelCase_  				=						50
   lowerCamelCase_  				=						self.dummy_model()
   lowerCamelCase_  				=						self.dummy_sample_deter
   scheduler.set_timesteps(UpperCamelCase  )

   # make sure that the first t is uneven
   for i, t in enumerate(scheduler.timesteps[3:]  ):
    lowerCamelCase_  				=						model(UpperCamelCase    ,						UpperCamelCase  )
    lowerCamelCase_  				=						scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ).prev_sample

   lowerCamelCase_  				=						torch.mean(torch.abs(UpperCamelCase  )  )

   assert abs(result_mean.item() - 0.2_574  ) < 1e-3






  def 						snake_case	(					self  ):







   """simple docstring"""
   for timesteps in [25, 50, 100, 999, 1000]:
    self.check_over_configs(num_train_timesteps=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   # make sure that iterating over schedulers with same config names gives same results
   # for defaults
   lowerCamelCase_  				=						DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
   lowerCamelCase_  				=						self.full_loop(scheduler=UpperCamelCase  )
   lowerCamelCase_  				=						torch.mean(torch.abs(UpperCamelCase  )  )

   assert abs(result_mean.item() - 0.2_791  ) < 1e-3

   lowerCamelCase_  				=						DEISMultistepScheduler.from_config(scheduler.config  )
   lowerCamelCase_  				=						DPMSolverMultistepScheduler.from_config(scheduler.config  )
   lowerCamelCase_  				=						UniPCMultistepScheduler.from_config(scheduler.config  )
   lowerCamelCase_  				=						DPMSolverSinglestepScheduler.from_config(scheduler.config  )

   lowerCamelCase_  				=						self.full_loop(scheduler=UpperCamelCase  )
   lowerCamelCase_  				=						torch.mean(torch.abs(UpperCamelCase  )  )

   assert abs(result_mean.item() - 0.2_791  ) < 1e-3






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.check_over_configs(thresholding=UpperCamelCase  )
   for order in [1, 2, 3]:
    for solver_type in ["midpoint", "heun"]:
     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    ,						algorithm_type="dpmsolver++"    ,						solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						)






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







   """simple docstring"""
   for algorithm_type in ["dpmsolver", "dpmsolver++"]:
    for solver_type in ["midpoint", "heun"]:
     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    ,						algorithm_type=UpperCamelCase    ,						)
       lowerCamelCase_  				=						self.full_loop(
           solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						prediction_type=UpperCamelCase    ,						algorithm_type=UpperCamelCase    ,						)
       assert not torch.isnan(UpperCamelCase  ).any(), "Samples have nan numbers"






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.check_over_configs(lower_order_final=UpperCamelCase  )
   self.check_over_configs(lower_order_final=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.check_over_configs(lambda_min_clipped=-float("inf"  )  )
   self.check_over_configs(lambda_min_clipped=-5.1  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.check_over_configs(variance_type=UpperCamelCase  )
   self.check_over_configs(variance_type="learned_range"  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
    self.check_over_forward(num_inference_steps=UpperCamelCase    ,						time_step=0  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.full_loop()
   lowerCamelCase_  				=						torch.mean(torch.abs(UpperCamelCase  )  )

   assert abs(result_mean.item() - 0.2_791  ) < 1e-3






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.full_loop(use_karras_sigmas=UpperCamelCase  )
   lowerCamelCase_  				=						torch.mean(torch.abs(UpperCamelCase  )  )

   assert abs(result_mean.item() - 0.2_248  ) < 1e-3






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.full_loop(prediction_type="v_prediction"  )
   lowerCamelCase_  				=						torch.mean(torch.abs(UpperCamelCase  )  )

   assert abs(result_mean.item() - 0.1_453  ) < 1e-3






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.full_loop(prediction_type="v_prediction"    ,						use_karras_sigmas=UpperCamelCase  )
   lowerCamelCase_  				=						torch.mean(torch.abs(UpperCamelCase  )  )

   assert abs(result_mean.item() - 0.0_649  ) < 1e-3







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.scheduler_classes[0]
   lowerCamelCase_  				=						self.get_scheduler_config(thresholding=UpperCamelCase    ,						dynamic_thresholding_ratio=0  )
   lowerCamelCase_  				=						scheduler_class(**UpperCamelCase  )

   lowerCamelCase_  				=						10
   lowerCamelCase_  				=						self.dummy_model()
   lowerCamelCase_  				=						self.dummy_sample_deter.half()
   scheduler.set_timesteps(UpperCamelCase  )

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

   assert sample.dtype == torch.floataa







'''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      (   UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Dict	):
 assert isinstance(UpperCAmelCase_      ,       UpperCAmelCase_	)
 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      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Union[str, Any]	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						{"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_  				=						SqlDatasetReader(
      "dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_      ,       keep_in_memory=UpperCAmelCase_	).read()
 _check_sql_dataset(UpperCAmelCase_      ,       UpperCAmelCase_	)





@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      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		str	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCamelCase_  				=						features.copy() if features else default_expected_features
 lowerCamelCase_  				=						(
     Features({feature: Value(UpperCAmelCase_	) for feature, dtype in features.items()}	) if features is not None else None
 )
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       features=UpperCAmelCase_      ,       cache_dir=UpperCAmelCase_	).read()
 _check_sql_dataset(UpperCAmelCase_      ,       UpperCAmelCase_	)





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]	):
 with contextlib.closing(sqlitea.connect(UpperCAmelCase_	)	) as con:
  lowerCamelCase_  				=						con.cursor()
  cur.execute("SELECT * FROM dataset"	)
  for row in cur:
   yield row





@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=1	).write()

 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)
 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)

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





@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=2	).write()

 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)
 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)

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







@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 with pytest.raises(UpperCAmelCase_	):
  SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=0	).write()



