



'''simple docstring'''

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 						a__  (					UpperCAmelCase__						):
	__magic_name__		:     Tuple						     =  (DPMSolverSinglestepScheduler,)
	__magic_name__		:     List[str]						     =  (('num_inference_steps', 25),)


	def 					lowercase__	(self			:			List[Any],       **__UpperCAmelCase			:			Optional[int]   )  ->      Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Tuple          =			{
					    "num_train_timesteps": 1000,
					    "beta_start": 0.0001,
					    "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(**lowerCamelCase__   )
					return config


	def 					lowercase__	(self			:			Tuple,       __UpperCAmelCase			:			Union[str, Any]=0,       **__UpperCAmelCase			:			Tuple   )  ->      int:

					"""simple docstring"""

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

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE	:    int          =			self.get_scheduler_config(**lowerCamelCase__   )
									SCREAMING_SNAKE_CASE	:    str          =			scheduler_class(**lowerCamelCase__   )
									scheduler.set_timesteps(lowerCamelCase__   )
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE	:    Tuple          =			dummy_past_residuals[: scheduler.config.solver_order]

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

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

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


	def 					lowercase__	(self			:			Tuple   )  ->      Tuple:

					"""simple docstring"""

					pass


	def 					lowercase__	(self			:			Dict,       __UpperCAmelCase			:			str=0,       **__UpperCAmelCase			:			Any   )  ->      Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    List[Any]          =			dict(self.forward_default_kwargs   )
					SCREAMING_SNAKE_CASE	:    Dict          =			kwargs.pop('''num_inference_steps''',       lowerCamelCase__   )
					SCREAMING_SNAKE_CASE	:    int          =			self.dummy_sample
					SCREAMING_SNAKE_CASE	:    List[Any]          =			0.1 * sample
					SCREAMING_SNAKE_CASE	:    int          =			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(lowerCamelCase__   )
													SCREAMING_SNAKE_CASE	:    int          =			scheduler_class.from_pretrained(lowerCamelCase__   )
													# copy over dummy past residuals
													new_scheduler.set_timesteps(lowerCamelCase__   )

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

									SCREAMING_SNAKE_CASE	:    List[str]          =			scheduler.step(lowerCamelCase__,       lowerCamelCase__,       lowerCamelCase__,       **lowerCamelCase__   ).prev_sample
									SCREAMING_SNAKE_CASE	:    Optional[int]          =			new_scheduler.step(lowerCamelCase__,       lowerCamelCase__,       lowerCamelCase__,       **lowerCamelCase__   ).prev_sample

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


	def 					lowercase__	(self			:			List[str],       __UpperCAmelCase			:			Optional[Any]=None,       **__UpperCAmelCase			:			Optional[int]   )  ->      Any:

					"""simple docstring"""

					if scheduler is None:
									SCREAMING_SNAKE_CASE	:    str          =			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE	:    Optional[int]          =			self.get_scheduler_config(**lowerCamelCase__   )
									SCREAMING_SNAKE_CASE	:    Optional[int]          =			scheduler_class(**lowerCamelCase__   )

					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE	:    List[str]          =			self.get_scheduler_config(**lowerCamelCase__   )
					SCREAMING_SNAKE_CASE	:    List[Any]          =			scheduler_class(**lowerCamelCase__   )

					SCREAMING_SNAKE_CASE	:    List[Any]          =			10
					SCREAMING_SNAKE_CASE	:    Dict          =			self.dummy_model()
					SCREAMING_SNAKE_CASE	:    List[Any]          =			self.dummy_sample_deter
					scheduler.set_timesteps(lowerCamelCase__   )

					for i, t in enumerate(scheduler.timesteps   ):
									SCREAMING_SNAKE_CASE	:    Tuple          =			model(lowerCamelCase__,       lowerCamelCase__   )
									SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			scheduler.step(lowerCamelCase__,       lowerCamelCase__,       lowerCamelCase__   ).prev_sample

					return sample


	def 					lowercase__	(self			:			Any   )  ->      Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Any          =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()   )
					SCREAMING_SNAKE_CASE	:    Dict          =			50
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			self.dummy_model()
					SCREAMING_SNAKE_CASE	:    List[str]          =			self.dummy_sample_deter
					scheduler.set_timesteps(lowerCamelCase__   )

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

					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			torch.mean(torch.abs(lowerCamelCase__   )   )

					assert abs(result_mean.item() - 0.2574   ) < 1e-3


	def 					lowercase__	(self			:			List[Any]   )  ->      int:

					"""simple docstring"""

					for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase__   )


	def 					lowercase__	(self			:			Tuple   )  ->      Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    List[Any]          =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()   )
					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			self.full_loop(scheduler=lowerCamelCase__   )
					SCREAMING_SNAKE_CASE	:    Dict          =			torch.mean(torch.abs(lowerCamelCase__   )   )

					assert abs(result_mean.item() - 0.2791   ) < 1e-3

					SCREAMING_SNAKE_CASE	:    int          =			DEISMultistepScheduler.from_config(scheduler.config   )
					SCREAMING_SNAKE_CASE	:    Any          =			DPMSolverMultistepScheduler.from_config(scheduler.config   )
					SCREAMING_SNAKE_CASE	:    List[Any]          =			UniPCMultistepScheduler.from_config(scheduler.config   )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			DPMSolverSinglestepScheduler.from_config(scheduler.config   )

					SCREAMING_SNAKE_CASE	:    Any          =			self.full_loop(scheduler=lowerCamelCase__   )
					SCREAMING_SNAKE_CASE	:    int          =			torch.mean(torch.abs(lowerCamelCase__   )   )

					assert abs(result_mean.item() - 0.2791   ) < 1e-3


	def 					lowercase__	(self			:			Any   )  ->      Dict:

					"""simple docstring"""

					self.check_over_configs(thresholding=lowerCamelCase__   )
					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=lowerCamelCase__,       prediction_type=lowerCamelCase__,       sample_max_value=lowerCamelCase__,       algorithm_type='''dpmsolver++''',       solver_order=lowerCamelCase__,       solver_type=lowerCamelCase__,       )


	def 					lowercase__	(self			:			Tuple   )  ->      Optional[Any]:

					"""simple docstring"""

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


	def 					lowercase__	(self			:			List[str]   )  ->      Union[str, Any]:

					"""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=lowerCamelCase__,       solver_type=lowerCamelCase__,       prediction_type=lowerCamelCase__,       algorithm_type=lowerCamelCase__,       )
																					SCREAMING_SNAKE_CASE	:    str          =			self.full_loop(
																					    solver_order=lowerCamelCase__,       solver_type=lowerCamelCase__,       prediction_type=lowerCamelCase__,       algorithm_type=lowerCamelCase__,       )
																					assert not torch.isnan(lowerCamelCase__   ).any(), "Samples have nan numbers"


	def 					lowercase__	(self			:			Optional[int]   )  ->      Dict:

					"""simple docstring"""

					self.check_over_configs(lower_order_final=lowerCamelCase__   )
					self.check_over_configs(lower_order_final=lowerCamelCase__   )


	def 					lowercase__	(self			:			str   )  ->      List[Any]:

					"""simple docstring"""

					self.check_over_configs(lambda_min_clipped=-float('''inf'''   )   )
					self.check_over_configs(lambda_min_clipped=-5.1   )


	def 					lowercase__	(self			:			Tuple   )  ->      Optional[int]:

					"""simple docstring"""

					self.check_over_configs(variance_type=lowerCamelCase__   )
					self.check_over_configs(variance_type='''learned_range'''   )


	def 					lowercase__	(self			:			Union[str, Any]   )  ->      int:

					"""simple docstring"""

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


	def 					lowercase__	(self			:			int   )  ->      int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    List[str]          =			self.full_loop()
					SCREAMING_SNAKE_CASE	:    List[Any]          =			torch.mean(torch.abs(lowerCamelCase__   )   )

					assert abs(result_mean.item() - 0.2791   ) < 1e-3


	def 					lowercase__	(self			:			int   )  ->      Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Any          =			self.full_loop(use_karras_sigmas=lowerCamelCase__   )
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			torch.mean(torch.abs(lowerCamelCase__   )   )

					assert abs(result_mean.item() - 0.2248   ) < 1e-3


	def 					lowercase__	(self			:			str   )  ->      Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			self.full_loop(prediction_type='''v_prediction'''   )
					SCREAMING_SNAKE_CASE	:    List[Any]          =			torch.mean(torch.abs(lowerCamelCase__   )   )

					assert abs(result_mean.item() - 0.1453   ) < 1e-3


	def 					lowercase__	(self			:			List[Any]   )  ->      Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			self.full_loop(prediction_type='''v_prediction''',       use_karras_sigmas=lowerCamelCase__   )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			torch.mean(torch.abs(lowerCamelCase__   )   )

					assert abs(result_mean.item() - 0.0649   ) < 1e-3






	def 					lowercase__	(self			:			List[str]   )  ->      Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Tuple          =			self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE	:    List[str]          =			self.get_scheduler_config(thresholding=lowerCamelCase__,       dynamic_thresholding_ratio=0   )
					SCREAMING_SNAKE_CASE	:    Tuple          =			scheduler_class(**lowerCamelCase__   )

					SCREAMING_SNAKE_CASE	:    Any          =			10
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			self.dummy_model()
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			self.dummy_sample_deter.half()
					scheduler.set_timesteps(lowerCamelCase__   )

					for i, t in enumerate(scheduler.timesteps   ):
									SCREAMING_SNAKE_CASE	:    Dict          =			model(lowerCamelCase__,       lowerCamelCase__   )
									SCREAMING_SNAKE_CASE	:    List[Any]          =			scheduler.step(lowerCamelCase__,       lowerCamelCase__,       lowerCamelCase__   ).prev_sample

					assert sample.dtype == torch.floataa





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__	(		lowercase							,				lowercase					):
   assert isinstance(lowercase							,				lowercase					)
   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__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Any       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"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_:							Optional[int]       =	SqlDatasetReader(
          "dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase							,				keep_in_memory=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
@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__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   lowerCAmelCase_:							List[str]       =	features.copy() if features else default_expected_features
   lowerCAmelCase_:							Any       =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   lowerCAmelCase_:							int       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				features=lowercase							,				cache_dir=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
def 		snake_case__	(		lowercase					):
   with contextlib.closing(sqlitea.connect(lowercase					)					) as con:
      lowerCAmelCase_:							Any       =	con.cursor()
      cur.execute("SELECT * FROM dataset"					)
      for row in cur:
         yield row
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[int]       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[Any]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							str       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=1					).write()

   lowerCAmelCase_:							Union[str, Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							str       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[int]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Optional[Any]       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=2					).write()

   lowerCAmelCase_:							Optional[Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	iter_sql_file(lowercase					)

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







@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Any       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   with pytest.raises(lowercase					):
      SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=0					).write()