



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		UpperCAmelCase__      (      unittest.TestCase       ):




							'''simple docstring'''






							UpperCamelCase      						=	(ScoreSdeVeScheduler,)
							UpperCamelCase      						=	()




							@property
							def 			snake_case__					(       self							:			Optional[int]			):
									'''simple docstring'''





									__UpperCAmelCase   :					List[str]													=     4
									__UpperCAmelCase   :					Optional[int]													=     3
									__UpperCAmelCase   :					Any													=     8
									__UpperCAmelCase   :					Union[str, Any]													=     8

									__UpperCAmelCase   :					Tuple													=     torch.rand((batch_size, num_channels, height, width)			)

									return sample




							@property
							def 			snake_case__					(       self							:			List[Any]			):
									'''simple docstring'''





									__UpperCAmelCase   :					int													=     4
									__UpperCAmelCase   :					List[str]													=     3
									__UpperCAmelCase   :					Optional[Any]													=     8
									__UpperCAmelCase   :					Tuple													=     8

									__UpperCAmelCase   :					Union[str, Any]													=     batch_size * num_channels * height * width
									__UpperCAmelCase   :					str													=     torch.arange(a_			)
									__UpperCAmelCase   :					Tuple													=     sample.reshape(a_	,	a_	,	a_	,	a_			)
									__UpperCAmelCase   :					Optional[Any]													=     sample / num_elems
									__UpperCAmelCase   :					List[str]													=     sample.permute(3	,	0	,	1	,	2			)

									return sample




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





									def model(a_							:			Optional[int]	,	a_							:			Optional[Any]	,	*a_							:			Any			):
											return sample * t / (t + 1)

									return model




							def 			snake_case__					(       self							:			Dict	,	**a_							:			Dict			):
									'''simple docstring'''





									__UpperCAmelCase   :					List[Any]													=     {
									    '''num_train_timesteps''': 20_00,
									    '''snr''': 0.1_5,
									    '''sigma_min''': 0.0_1,
									    '''sigma_max''': 13_48,
									    '''sampling_eps''': 1e-5,
									}

									config.update(**a_			)
									return config




							def 			snake_case__					(       self							:			Dict	,	a_							:			Union[str, Any]=0	,	**a_							:			int			):
									'''simple docstring'''





									__UpperCAmelCase   :					Optional[int]													=     dict(self.forward_default_kwargs			)

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

											__UpperCAmelCase   :					Optional[int]													=     self.get_scheduler_config(**a_			)
											__UpperCAmelCase   :					Tuple													=     scheduler_class(**a_			)

											with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(a_			)
													__UpperCAmelCase   :					List[str]													=     scheduler_class.from_pretrained(a_			)

											__UpperCAmelCase   :					List[Any]													=     scheduler.step_pred(
											    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
											__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_pred(
											    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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

											__UpperCAmelCase   :					Dict													=     scheduler.step_correct(a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
											__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_correct(
											    a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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




							def 			snake_case__					(       self							:			Optional[Any]	,	a_							:			Any=0	,	**a_							:			Optional[Any]			):
									'''simple docstring'''





									__UpperCAmelCase   :					Optional[Any]													=     dict(self.forward_default_kwargs			)
									kwargs.update(a_			)

									for scheduler_class in self.scheduler_classes:
											__UpperCAmelCase   :					Tuple													=     self.dummy_sample
											__UpperCAmelCase   :					Tuple													=     0.1 * sample

											__UpperCAmelCase   :					int													=     self.get_scheduler_config()
											__UpperCAmelCase   :					Optional[Any]													=     scheduler_class(**a_			)

											with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(a_			)
													__UpperCAmelCase   :					str													=     scheduler_class.from_pretrained(a_			)

											__UpperCAmelCase   :					Union[str, Any]													=     scheduler.step_pred(
											    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
											__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_pred(
											    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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

											__UpperCAmelCase   :					List[Any]													=     scheduler.step_correct(a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
											__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_correct(
											    a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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




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





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




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





									for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]	,	[1, 1_00, 10_00]			):
											self.check_over_configs(sigma_min=a_	,	sigma_max=a_			)




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





									for t in [0.1, 0.5, 0.7_5]:
											self.check_over_forward(time_step=a_			)




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





									__UpperCAmelCase   :					Union[str, Any]													=     dict(self.forward_default_kwargs			)

									__UpperCAmelCase   :					int													=     self.scheduler_classes[0]
									__UpperCAmelCase   :					List[str]													=     self.get_scheduler_config()
									__UpperCAmelCase   :					Union[str, Any]													=     scheduler_class(**a_			)

									__UpperCAmelCase   :					str													=     3

									__UpperCAmelCase   :					str													=     self.dummy_model()
									__UpperCAmelCase   :					List[Any]													=     self.dummy_sample_deter

									scheduler.set_sigmas(a_			)
									scheduler.set_timesteps(a_			)
									__UpperCAmelCase   :					int													=     torch.manual_seed(0			)

									for i, t in enumerate(scheduler.timesteps			):
											__UpperCAmelCase   :					Any													=     scheduler.sigmas[i]

											for _ in range(scheduler.config.correct_steps			):
													with torch.no_grad():
															__UpperCAmelCase   :					Union[str, Any]													=     model(a_	,	a_			)
													__UpperCAmelCase   :					Optional[int]													=     scheduler.step_correct(a_	,	a_	,	generator=a_	,	**a_			).prev_sample

											with torch.no_grad():
													__UpperCAmelCase   :					int													=     model(a_	,	a_			)

											__UpperCAmelCase   :					int													=     scheduler.step_pred(a_	,	a_	,	a_	,	generator=a_	,	**a_			)
											__UpperCAmelCase   :					int													=     output.prev_sample, output.prev_sample_mean

									__UpperCAmelCase   :					Any													=     torch.sum(torch.abs(a_			)			)
									__UpperCAmelCase   :					Optional[int]													=     torch.mean(torch.abs(a_			)			)

									assert np.isclose(result_sum.item()	,	1_43_72_75_85_28.0			)
									assert np.isclose(result_mean.item()	,	18_71_45_30.0			)




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





									__UpperCAmelCase   :					Any													=     dict(self.forward_default_kwargs			)

									__UpperCAmelCase   :					int													=     kwargs.pop('''num_inference_steps'''	,	a_			)

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

											__UpperCAmelCase   :					List[str]													=     self.dummy_sample
											__UpperCAmelCase   :					Dict													=     0.1 * sample

											if num_inference_steps is not None and hasattr(a_	,	'''set_timesteps'''			):
													scheduler.set_timesteps(a_			)
											elif num_inference_steps is not None and not hasattr(a_	,	'''set_timesteps'''			):
													__UpperCAmelCase   :					Tuple													=     num_inference_steps

											__UpperCAmelCase   :					Dict													=     scheduler.step_pred(a_	,	0	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
											__UpperCAmelCase   :					Dict													=     scheduler.step_pred(a_	,	1	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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










import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class 		UpperCAmelCase__      :




			'''simple docstring'''






			UpperCamelCase      						=	42

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     get_tests_dir('''fixtures/test_sentencepiece.model'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''			)

					__UpperCAmelCase   :					List[str]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Optional[int]													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	0			)

					__UpperCAmelCase   :					Optional[int]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Dict													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	1			)

					self.assertIn(
					    '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
					    ''' which is not implemented in the fast tokenizers.'''	,	str(w[0].message			)	,	)





