




'''simple docstring'''

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Dict    =  (KDPMaAncestralDiscreteScheduler,)
						UpperCAmelCase_		:					Any    =  10






						def       __lowerCAmelCase(   self						:						Optional[int]					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						{
									    """num_train_timesteps""": 1_1_0_0,
									    """beta_start""": 0.00_01,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**__UpperCamelCase       )
									return config






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,   [0.00_02, 0.0_02, 0.02]       ):
												self.check_over_configs(beta_start=__UpperCamelCase					,   beta_end=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config()
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									snake_case__          =						torch.manual_seed(0       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__          =						sample.to(__UpperCamelCase       )

									for i, t in enumerate(scheduler.timesteps       ):
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

									snake_case__          =						torch.sum(torch.abs(__UpperCamelCase       )       )
									snake_case__          =						torch.mean(torch.abs(__UpperCamelCase       )       )

									assert abs(result_sum.item() - 1_38_49.38_77       ) < 1E-2
									assert abs(result_mean.item() - 18.03_31       ) < 5E-3






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config(prediction_type="""v_prediction"""       )
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__          =						sample.to(__UpperCamelCase       )

									snake_case__          =						torch.manual_seed(0       )

									for i, t in enumerate(scheduler.timesteps       ):
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

									snake_case__          =						torch.sum(torch.abs(__UpperCamelCase       )       )
									snake_case__          =						torch.mean(torch.abs(__UpperCamelCase       )       )

									assert abs(result_sum.item() - 3_28.99_70       ) < 1E-2
									assert abs(result_mean.item() - 0.42_84       ) < 1E-3







						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config()
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps					,   device=__UpperCamelCase       )
									snake_case__          =						torch.manual_seed(0       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter.to(__UpperCamelCase       ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

									snake_case__          =						torch.sum(torch.abs(__UpperCamelCase       )       )
									snake_case__          =						torch.mean(torch.abs(__UpperCamelCase       )       )

									assert abs(result_sum.item() - 1_38_49.38_18       ) < 1E-1
									assert abs(result_mean.item() - 18.03_31       ) < 1E-3







'''simple docstring'''







import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     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





@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      int			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														lowerCamelCase__   :      Any			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase							,     keep_in_memory=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





@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 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Optional[int]			    =			tmp_path / """cache"""
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							lowerCamelCase__   :      int			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      int			    =			features.copy() if features else default_expected_features
							lowerCamelCase__   :      List[Any]			    =			(
							    Features({feature: Value(UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
							)
							lowerCamelCase__   :      Union[str, Any]			    =			CsvDatasetReader(UpperCamelCase							,     features=UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train""" ), """train""", """test"""] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Union[str, Any]:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Union[str, Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      List[Any]			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase							,     split=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )
							assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""							,     [str, list] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Union[str, Any]:
							if issubclass(UpperCamelCase							,     UpperCamelCase ):
														lowerCamelCase__   :      int			    =			csv_path
							elif issubclass(UpperCamelCase							,     UpperCamelCase ):
														lowerCamelCase__   :      Tuple			    =			[csv_path]
							lowerCamelCase__   :      Optional[int]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      str			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      Dict			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase=("train",) ) ->			int:
							assert isinstance(UpperCamelCase							,     UpperCamelCase )
							for split in splits:
														lowerCamelCase__   :      Optional[Any]			    =			dataset_dict[split]
														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





@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Optional[Any]:
							lowerCamelCase__   :      List[Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Dict			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														lowerCamelCase__   :      Any			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase							,     keep_in_memory=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase )





@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 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			int:
							lowerCamelCase__   :      int			    =			tmp_path / """cache"""
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							lowerCamelCase__   :      Optional[Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      List[str]			    =			features.copy() if features else default_expected_features
							lowerCamelCase__   :      Tuple			    =			(
							    Features({feature: Value(UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
							)
							lowerCamelCase__   :      str			    =			CsvDatasetReader({"""train""": csv_path}							,     features=UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train""" ), """train""", """test"""] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							if split:
														lowerCamelCase__   :      Tuple			    =			{split: csv_path}
							else:
														lowerCamelCase__   :      Union[str, Any]			    =			{"""train""": csv_path, """test""": csv_path}
							lowerCamelCase__   :      Dict			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Union[str, Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      Tuple			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase							,     splits=list(path.keys() ) )
							assert all(dataset[split].split == split for split in path.keys() )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Tuple:
							with open(UpperCamelCase							,     encoding="""utf-8""" ) as csvfile:
														yield from csv.reader(UpperCamelCase )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Optional[int]:
							lowerCamelCase__   :      Union[str, Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[str]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Optional[Any]			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=1 ).write()

							lowerCamelCase__   :      Optional[Any]			    =			iter_csv_file(UpperCamelCase )
							lowerCamelCase__   :      str			    =			iter_csv_file(UpperCamelCase )

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





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			List[str]:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[str]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Optional[int]			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=2 ).write()

							lowerCamelCase__   :      Tuple			    =			iter_csv_file(UpperCamelCase )
							lowerCamelCase__   :      Union[str, Any]			    =			iter_csv_file(UpperCamelCase )

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





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Optional[int]:
							lowerCamelCase__   :      List[Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[Any]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Dict			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							with pytest.raises(UpperCamelCase ):
														CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=0 )

