



"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 	__snake_case	(     _lowercase):
		snake_case__    :       List[Any]        =						(PNDMScheduler,)
		snake_case__    :       Optional[Any]        =						(("num_inference_steps", 5_0),)




		def 						SCREAMING_SNAKE_CASE    (			self					:						str						, **__lowerCAmelCase					:						int		):






									"""simple docstring"""






									_lowerCamelCase						:    List[str]         =			{
									    '''num_train_timesteps''': 1_0_0_0,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**__lowerCAmelCase		)
									return config




		def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						List[Any]=0						, **__lowerCAmelCase					:						Dict		):






									"""simple docstring"""






									_lowerCamelCase						:    List[str]         =			dict(self.forward_default_kwargs		)
									_lowerCamelCase						:    Union[str, Any]         =			kwargs.pop('''num_inference_steps'''						, __lowerCAmelCase		)
									_lowerCamelCase						:    Dict         =			self.dummy_sample
									_lowerCamelCase						:    Optional[int]         =			0.1 * sample
									_lowerCamelCase						:    Any         =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
																_lowerCamelCase						:    Union[str, Any]         =			self.get_scheduler_config(**__lowerCAmelCase		)
																_lowerCamelCase						:    List[Any]         =			scheduler_class(**__lowerCAmelCase		)
																scheduler.set_timesteps(__lowerCAmelCase		)
																# copy over dummy past residuals
																_lowerCamelCase						:    Optional[Any]         =			dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(__lowerCAmelCase		)
																							_lowerCamelCase						:    Any         =			scheduler_class.from_pretrained(__lowerCAmelCase		)
																							new_scheduler.set_timesteps(__lowerCAmelCase		)
																							# copy over dummy past residuals
																							_lowerCamelCase						:    List[Any]         =			dummy_past_residuals[:]

																_lowerCamelCase						:    Tuple         =			scheduler.step_prk(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample
																_lowerCamelCase						:    str         =			new_scheduler.step_prk(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample

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

																_lowerCamelCase						:    int         =			scheduler.step_plms(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample
																_lowerCamelCase						:    List[str]         =			new_scheduler.step_plms(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample

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




		def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






									"""simple docstring"""






									pass




		def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						List[Any]=0						, **__lowerCAmelCase					:						Optional[int]		):






									"""simple docstring"""






									_lowerCamelCase						:    List[str]         =			dict(self.forward_default_kwargs		)
									_lowerCamelCase						:    List[str]         =			kwargs.pop('''num_inference_steps'''						, __lowerCAmelCase		)
									_lowerCamelCase						:    Any         =			self.dummy_sample
									_lowerCamelCase						:    Any         =			0.1 * sample
									_lowerCamelCase						:    Tuple         =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
																_lowerCamelCase						:    int         =			self.get_scheduler_config()
																_lowerCamelCase						:    List[Any]         =			scheduler_class(**__lowerCAmelCase		)
																scheduler.set_timesteps(__lowerCAmelCase		)

																# copy over dummy past residuals (must be after setting timesteps)
																_lowerCamelCase						:    str         =			dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(__lowerCAmelCase		)
																							_lowerCamelCase						:    Optional[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)
																							_lowerCamelCase						:    List[Any]         =			dummy_past_residuals[:]

																_lowerCamelCase						:    Any         =			scheduler.step_prk(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample
																_lowerCamelCase						:    Optional[int]         =			new_scheduler.step_prk(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample

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

																_lowerCamelCase						:    Optional[int]         =			scheduler.step_plms(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample
																_lowerCamelCase						:    List[Any]         =			new_scheduler.step_plms(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample

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




		def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, **__lowerCAmelCase					:						Optional[int]		):






									"""simple docstring"""






									_lowerCamelCase						:    Tuple         =			self.scheduler_classes[0]
									_lowerCamelCase						:    Any         =			self.get_scheduler_config(**__lowerCAmelCase		)
									_lowerCamelCase						:    Dict         =			scheduler_class(**__lowerCAmelCase		)

									_lowerCamelCase						:    Optional[Any]         =			1_0
									_lowerCamelCase						:    Optional[Any]         =			self.dummy_model()
									_lowerCamelCase						:    Optional[Any]         =			self.dummy_sample_deter
									scheduler.set_timesteps(__lowerCAmelCase		)

									for i, t in enumerate(scheduler.prk_timesteps		):
																_lowerCamelCase						:    Dict         =			model(__lowerCAmelCase						, __lowerCAmelCase		)
																_lowerCamelCase						:    List[str]         =			scheduler.step_prk(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps		):
																_lowerCamelCase						:    Optional[int]         =			model(__lowerCAmelCase						, __lowerCAmelCase		)
																_lowerCamelCase						:    Dict         =			scheduler.step_plms(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		).prev_sample

									return sample




		def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






									"""simple docstring"""






									_lowerCamelCase						:    str         =			dict(self.forward_default_kwargs		)

									_lowerCamelCase						:    List[str]         =			kwargs.pop('''num_inference_steps'''						, __lowerCAmelCase		)

									for scheduler_class in self.scheduler_classes:
																_lowerCamelCase						:    Union[str, Any]         =			self.get_scheduler_config()
																_lowerCamelCase						:    Union[str, Any]         =			scheduler_class(**__lowerCAmelCase		)

																_lowerCamelCase						:    Any         =			self.dummy_sample
																_lowerCamelCase						:    Tuple         =			0.1 * sample

																if num_inference_steps is not None and hasattr(__lowerCAmelCase						, '''set_timesteps'''		):
																							scheduler.set_timesteps(__lowerCAmelCase		)
																elif num_inference_steps is not None and not hasattr(__lowerCAmelCase						, '''set_timesteps'''		):
																							_lowerCamelCase						:    List[Any]         =			num_inference_steps

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

																_lowerCamelCase						:    Any         =			scheduler.step_prk(__lowerCAmelCase						, 0						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample
																_lowerCamelCase						:    str         =			scheduler.step_prk(__lowerCAmelCase						, 1						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample

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

																_lowerCamelCase						:    Union[str, Any]         =			scheduler.step_plms(__lowerCAmelCase						, 0						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample
																_lowerCamelCase						:    Optional[int]         =			scheduler.step_plms(__lowerCAmelCase						, 1						, __lowerCAmelCase						, **__lowerCAmelCase		).prev_sample

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




		def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






									"""simple docstring"""






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




		def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






									"""simple docstring"""






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

									_lowerCamelCase						:    Dict         =			self.scheduler_classes[0]
									_lowerCamelCase						:    List[str]         =			self.get_scheduler_config(steps_offset=1		)
									_lowerCamelCase						:    int         =			scheduler_class(**__lowerCAmelCase		)
									scheduler.set_timesteps(1_0		)
									assert torch.equal(
									    scheduler.timesteps						, torch.LongTensor(
									        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]		)						, )




		def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






									"""simple docstring"""






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




		def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






									"""simple docstring"""






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




		def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






									"""simple docstring"""






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




		def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






									"""simple docstring"""






									for t in [1, 5, 1_0]:
																self.check_over_forward(time_step=__lowerCAmelCase		)




		def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






									"""simple docstring"""






									for t, num_inference_steps in zip([1, 5, 1_0]						, [1_0, 5_0, 1_0_0]		):
																self.check_over_forward(num_inference_steps=__lowerCAmelCase		)




		def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






									"""simple docstring"""






									_lowerCamelCase						:    List[str]         =			2_7

									for scheduler_class in self.scheduler_classes:
																_lowerCamelCase						:    Dict         =			self.dummy_sample
																_lowerCamelCase						:    Dict         =			0.1 * sample

																_lowerCamelCase						:    Any         =			self.get_scheduler_config()
																_lowerCamelCase						:    int         =			scheduler_class(**__lowerCAmelCase		)

																scheduler.set_timesteps(__lowerCAmelCase		)

																# 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]		):
																							_lowerCamelCase						:    Dict         =			scheduler.step_prk(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		).prev_sample




		def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






									"""simple docstring"""






									with self.assertRaises(__lowerCAmelCase		):
																_lowerCamelCase						:    Optional[Any]         =			self.scheduler_classes[0]
																_lowerCamelCase						:    Tuple         =			self.get_scheduler_config()
																_lowerCamelCase						:    Optional[int]         =			scheduler_class(**__lowerCAmelCase		)

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




		def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






									"""simple docstring"""






									_lowerCamelCase						:    List[Any]         =			self.full_loop()
									_lowerCamelCase						:    List[str]         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
									_lowerCamelCase						:    Optional[Any]         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

									assert abs(result_sum.item() - 1_9_8.1_3_1_8		) < 1E-2
									assert abs(result_mean.item() - 0.25_80		) < 1E-3




		def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






									"""simple docstring"""






									_lowerCamelCase						:    List[Any]         =			self.full_loop(prediction_type='''v_prediction'''		)
									_lowerCamelCase						:    Optional[Any]         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
									_lowerCamelCase						:    int         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

									assert abs(result_sum.item() - 67.39_86		) < 1E-2
									assert abs(result_mean.item() - 0.08_78		) < 1E-3




		def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






									"""simple docstring"""






									_lowerCamelCase						:    Tuple         =			self.full_loop(set_alpha_to_one=__lowerCAmelCase						, beta_start=0.01		)
									_lowerCamelCase						:    List[str]         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
									_lowerCamelCase						:    Optional[int]         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

									assert abs(result_sum.item() - 2_3_0.0_3_9_9		) < 1E-2
									assert abs(result_mean.item() - 0.29_95		) < 1E-3




		def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






									"""simple docstring"""






									_lowerCamelCase						:    Any         =			self.full_loop(set_alpha_to_one=__lowerCAmelCase						, beta_start=0.01		)
									_lowerCamelCase						:    Dict         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
									_lowerCamelCase						:    Dict         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

									assert abs(result_sum.item() - 1_8_6.9_4_8_2		) < 1E-2
									assert abs(result_mean.item() - 0.24_34		) < 1E-3







"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       def wrapper(*A_ :		int,    **A_ :		List[str]						):
              _lowerCamelCase						:    Tuple         =			timeit.default_timer()
              _lowerCamelCase						:    Optional[Any]         =			func(*A_,    **A_						)
              _lowerCamelCase						:    Tuple         =			timeit.default_timer() - starttime
              return delta

       _lowerCamelCase						:    Optional[Any]         =			func.__name__

       return wrapper






def 	snake_case_  (				A_ :		dict,    A_ :		List[str]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			[]
       _lowerCamelCase						:    Optional[int]         =			seq_shapes or {}
       for i in range(A_						):
              _lowerCamelCase						:    Any         =			{}
              for col_id, (k, v) in enumerate(features.items()						):
                     if isinstance(A_,    _ArrayXD						):
                            _lowerCamelCase						:    str         =			np.random.rand(*v.shape						).astype(v.dtype						)
                     elif isinstance(A_,    datasets.Value						):
                            if v.dtype == "string":
                                   _lowerCamelCase						:    List[str]         =			'''The small grey turtle was surprisingly fast when challenged.'''
                            else:
                                   _lowerCamelCase						:    int         =			np.random.randint(10,    size=1						).astype(v.dtype						).item()
                     elif isinstance(A_,    datasets.Sequence						):
                            while isinstance(A_,    datasets.Sequence						):
                                   _lowerCamelCase						:    Union[str, Any]         =			v.feature
                            _lowerCamelCase						:    Dict         =			seq_shapes[k]
                            _lowerCamelCase						:    int         =			np.random.rand(*A_						).astype(v.dtype						)
                     _lowerCamelCase						:    Optional[Any]         =			data

              dummy_data.append((i, example)						)

       return dummy_data







def 	snake_case_  (				A_ :		List[str],    A_ :		Optional[int],    A_ :		Optional[int]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			generate_examples(A_,    num_examples=A_,    seq_shapes=A_						)

       with ArrowWriter(features=A_,    path=A_						) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase						:    Optional[Any]         =			features.encode_example(A_						)
                     writer.write(A_						)

              _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			writer.finalize()

       if not num_final_examples == num_examples:
              raise ValueError(
                  F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''						)

       _lowerCamelCase						:    List[Any]         =			datasets.Dataset.from_file(filename=A_,    info=datasets.DatasetInfo(features=A_						)						)

       return dataset


