



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							(DDPMScheduler,)



	def 		UpperCamelCase				(    self				:     str					,						**snake_case_				:     List[Any])->				Optional[Any]:
						__lowerCAmelCase						 ={
						    """num_train_timesteps""": 10_00,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						    """variance_type""": """fixed_small""",
						    """clip_sample""": True,
						}

						config.update(**snake_case_)
						return config



	def 		UpperCamelCase				(    self				:     List[Any])->				Tuple:
						for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				str:
						for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,						[0.0_0_2, 0.0_2, 0.2, 2]):
											self.check_over_configs(beta_start=snake_case_					,						beta_end=snake_case_)



	def 		UpperCamelCase				(    self				:     List[Any])->				List[Any]:
						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=snake_case_)



	def 		UpperCamelCase				(    self				:     Any)->				Optional[int]:
						for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[int])->				str:
						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Union[str, Any]:
						self.check_over_configs(thresholding=snake_case_)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=snake_case_					,						prediction_type=snake_case_					,						sample_max_value=snake_case_					,						)



	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[int])->				Union[str, Any]:
						for t in [0, 5_00, 9_99]:
											self.check_over_forward(time_step=snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any])->				Any:
						__lowerCAmelCase						 =self.scheduler_classes[0]
						__lowerCAmelCase						 =self.get_scheduler_config()
						__lowerCAmelCase						 =scheduler_class(**snake_case_)

						assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1e-5



	def 		UpperCamelCase				(    self				:     int)->				Optional[Any]:
						__lowerCAmelCase						 =self.scheduler_classes[0]
						__lowerCAmelCase						 =self.get_scheduler_config()
						__lowerCAmelCase						 =scheduler_class(**snake_case_)

						__lowerCAmelCase						 =len(snake_case_)

						__lowerCAmelCase						 =self.dummy_model()
						__lowerCAmelCase						 =self.dummy_sample_deter
						__lowerCAmelCase						 =torch.manual_seed(0)

						for t in reversed(range(snake_case_)):
											# 1. predict noise residual
											__lowerCAmelCase						 =model(snake_case_					,						snake_case_)

											# 2. predict previous mean of sample x_t-1
											__lowerCAmelCase						 =scheduler.step(snake_case_					,						snake_case_					,						snake_case_					,						generator=snake_case_).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											__lowerCAmelCase						 =pred_prev_sample

						__lowerCAmelCase						 =torch.sum(torch.abs(snake_case_))
						__lowerCAmelCase						 =torch.mean(torch.abs(snake_case_))

						assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1e-2
						assert abs(result_mean.item() - 0.3_3_7_2) < 1e-3



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Union[str, Any]:
						__lowerCAmelCase						 =self.scheduler_classes[0]
						__lowerCAmelCase						 =self.get_scheduler_config(prediction_type="""v_prediction""")
						__lowerCAmelCase						 =scheduler_class(**snake_case_)

						__lowerCAmelCase						 =len(snake_case_)

						__lowerCAmelCase						 =self.dummy_model()
						__lowerCAmelCase						 =self.dummy_sample_deter
						__lowerCAmelCase						 =torch.manual_seed(0)

						for t in reversed(range(snake_case_)):
											# 1. predict noise residual
											__lowerCAmelCase						 =model(snake_case_					,						snake_case_)

											# 2. predict previous mean of sample x_t-1
											__lowerCAmelCase						 =scheduler.step(snake_case_					,						snake_case_					,						snake_case_					,						generator=snake_case_).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											__lowerCAmelCase						 =pred_prev_sample

						__lowerCAmelCase						 =torch.sum(torch.abs(snake_case_))
						__lowerCAmelCase						 =torch.mean(torch.abs(snake_case_))

						assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1e-2
						assert abs(result_mean.item() - 0.2_6_3_1) < 1e-3



	def 		UpperCamelCase				(    self				:     Optional[Any])->				List[str]:
						__lowerCAmelCase						 =self.scheduler_classes[0]
						__lowerCAmelCase						 =self.get_scheduler_config()
						__lowerCAmelCase						 =scheduler_class(**snake_case_)

						__lowerCAmelCase						 =[1_00, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=snake_case_)

						__lowerCAmelCase						 =scheduler.timesteps

						for i, timestep in enumerate(snake_case_):
											if i == len(snake_case_) - 1:
																__lowerCAmelCase						 =-1
											else:
																__lowerCAmelCase						 =timesteps[i + 1]

											__lowerCAmelCase						 =scheduler.previous_timestep(snake_case_)
											__lowerCAmelCase						 =prev_t.item()

											self.assertEqual(snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict)->				List[Any]:
						__lowerCAmelCase						 =self.scheduler_classes[0]
						__lowerCAmelCase						 =self.get_scheduler_config()
						__lowerCAmelCase						 =scheduler_class(**snake_case_)

						__lowerCAmelCase						 =[1_00, 87, 50, 51, 0]

						with self.assertRaises(snake_case_					,						msg="""`custom_timesteps` must be in descending order."""):
											scheduler.set_timesteps(timesteps=snake_case_)



	def 		UpperCamelCase				(    self				:     List[Any])->				Union[str, Any]:
						__lowerCAmelCase						 =self.scheduler_classes[0]
						__lowerCAmelCase						 =self.get_scheduler_config()
						__lowerCAmelCase						 =scheduler_class(**snake_case_)

						__lowerCAmelCase						 =[1_00, 87, 50, 1, 0]
						__lowerCAmelCase						 =len(snake_case_)

						with self.assertRaises(snake_case_					,						msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
											scheduler.set_timesteps(num_inference_steps=snake_case_					,						timesteps=snake_case_)



	def 		UpperCamelCase				(    self				:     str)->				Any:
						__lowerCAmelCase						 =self.scheduler_classes[0]
						__lowerCAmelCase						 =self.get_scheduler_config()
						__lowerCAmelCase						 =scheduler_class(**snake_case_)

						__lowerCAmelCase						 =[scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    snake_case_					,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""					,						):
											scheduler.set_timesteps(timesteps=snake_case_)









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Any						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Tuple						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Union[str, Any]						)	->    Any:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, split=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""					, [str, list]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]						)	->    Any:
					if issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =text_path
					elif issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =[text_path]
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     str=("train",)						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					for split in splits:
										__lowerCAmelCase						 =dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Dict:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Union[str, Any]						)	->    Tuple:
					__lowerCAmelCase						 =tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any						)	->    int:
					if split:
										__lowerCAmelCase						 ={split: text_path}
					else:
										__lowerCAmelCase						 ="""train"""
										__lowerCAmelCase						 ={"""train""": text_path, """test""": text_path}
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase					, splits=list(path.keys()						)						)
					assert all(dataset[split].split == split for split in path.keys()						)




