




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (PNDMScheduler,)
						UpperCamelCase      =       (("""num_inference_steps""", 50),)

						def      A__    (		self :str     ,			**__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						int										={
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**__snake_case			)
										return config

						def      A__    (		self :List[Any]     ,			__snake_case :Union[str, Any]=0     ,			**__snake_case :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Any										=dict(self.forward_default_kwargs			)
										__magic_name__						:						str										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						int										=self.dummy_sample
										__magic_name__						:						Dict										=0.1 * sample
										__magic_name__						:						Tuple										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Optional[int]										=self.get_scheduler_config(**__snake_case			)
														__magic_name__						:						Dict										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)
														# copy over dummy past residuals
														__magic_name__						:						Tuple										=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						int										=scheduler_class.from_pretrained(__snake_case			)
																		new_scheduler.set_timesteps(__snake_case			)
																		# copy over dummy past residuals
																		__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														__magic_name__						:						int										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=new_scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Tuple										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						str										=new_scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]=0     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=dict(self.forward_default_kwargs			)
										__magic_name__						:						Optional[int]										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						List[Any]										=self.dummy_sample
										__magic_name__						:						Optional[Any]										=0.1 * sample
										__magic_name__						:						Optional[int]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						List[str]										=self.get_scheduler_config()
														__magic_name__						:						int										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						Optional[int]										=scheduler_class.from_pretrained(__snake_case			)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(__snake_case			)

																		# copy over dummy past residual (must be after setting timesteps)
																		__magic_name__						:						Any										=dummy_past_residuals[:]

														__magic_name__						:						List[str]										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=new_scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Any										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						List[Any]										=new_scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Any     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.scheduler_classes[0]
										__magic_name__						:						Dict										=self.get_scheduler_config(**__snake_case			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)

										__magic_name__						:						int										=10
										__magic_name__						:						Tuple										=self.dummy_model()
										__magic_name__						:						Dict										=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case			)

										for i, t in enumerate(scheduler.prk_timesteps			):
														__magic_name__						:						Optional[int]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						List[str]										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps			):
														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Union[str, Any]										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										return sample

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=dict(self.forward_default_kwargs			)

										__magic_name__						:						Tuple										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						str										=self.get_scheduler_config()
														__magic_name__						:						Dict										=scheduler_class(**__snake_case			)

														__magic_name__						:						Optional[int]										=self.dummy_sample
														__magic_name__						:						int										=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case     ,			"""set_timesteps"""			):
																		scheduler.set_timesteps(__snake_case			)
														elif num_inference_steps is not None and not hasattr(__snake_case     ,			"""set_timesteps"""			):
																		__magic_name__						:						List[str]										=num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														__magic_name__						:						List[str]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														__magic_name__						:						int										=dummy_past_residuals[:]

														__magic_name__						:						Union[str, Any]										=scheduler.step_prk(__snake_case     ,			0     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						int										=scheduler.step_prk(__snake_case     ,			1     ,			__snake_case     ,			**__snake_case			).prev_sample

														self.assertEqual(output_a.shape     ,			sample.shape			)
														self.assertEqual(output_a.shape     ,			output_a.shape			)

														__magic_name__						:						List[Any]										=scheduler.step_plms(__snake_case     ,			0     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						str										=scheduler.step_plms(__snake_case     ,			1     ,			__snake_case     ,			**__snake_case			).prev_sample

														self.assertEqual(output_a.shape     ,			sample.shape			)
														self.assertEqual(output_a.shape     ,			output_a.shape			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case			)

						def      A__    (		self :int			):
										'''simple docstring'''

										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__snake_case			)

										__magic_name__						:						str										=self.scheduler_classes[0]
										__magic_name__						:						Tuple										=self.get_scheduler_config(steps_offset=1			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)
										scheduler.set_timesteps(10			)
										assert torch.equal(
										    scheduler.timesteps     ,			torch.LongTensor(
										        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]			)     ,			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.0001, 0.001]     ,			[0.002, 0.02]			):
														self.check_over_configs(beta_start=__snake_case     ,			beta_end=__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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

						def      A__    (		self :str			):
										'''simple docstring'''

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for t in [1, 5, 10]:
														self.check_over_forward(time_step=__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]     ,			[10, 50, 1_00]			):
														self.check_over_forward(num_inference_steps=__snake_case			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=27

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Union[str, Any]										=self.dummy_sample
														__magic_name__						:						str										=0.1 * sample

														__magic_name__						:						List[str]										=self.get_scheduler_config()
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

														scheduler.set_timesteps(__snake_case			)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]			):
																		__magic_name__						:						Dict										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										with self.assertRaises(__snake_case			):
														__magic_name__						:						List[Any]										=self.scheduler_classes[0]
														__magic_name__						:						Optional[Any]										=self.get_scheduler_config()
														__magic_name__						:						List[str]										=scheduler_class(**__snake_case			)

														scheduler.step_plms(self.dummy_sample     ,			1     ,			self.dummy_sample			).prev_sample

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.full_loop()
										__magic_name__						:						int										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Optional[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 198.1318			) < 1E-2
										assert abs(result_mean.item() - 0.2580			) < 1E-3

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.full_loop(prediction_type="""v_prediction"""			)
										__magic_name__						:						List[str]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[str]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 67.3986			) < 1E-2
										assert abs(result_mean.item() - 0.0878			) < 1E-3

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.full_loop(set_alpha_to_one=__snake_case     ,			beta_start=0.01			)
										__magic_name__						:						Tuple										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 230.0399			) < 1E-2
										assert abs(result_mean.item() - 0.2995			) < 1E-3


						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.full_loop(set_alpha_to_one=__snake_case     ,			beta_start=0.01			)
										__magic_name__						:						Union[str, Any]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Any										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 186.9482			) < 1E-2
										assert abs(result_mean.item() - 0.2434			) < 1E-3







'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
