



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	(DDIMScheduler,)
			UpperCamelCase      						=	(("""eta""", 0.0), ("""num_inference_steps""", 50))




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





					__UpperCAmelCase   :					int													=     {
					    '''num_train_timesteps''': 10_00,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					    '''clip_sample''': True,
					}

					config.update(**a_			)
					return config




			def 			snake_case__					(       self							:			List[Any]	,	**a_							:			Dict			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Tuple													=     self.get_scheduler_config(**a_			)
					__UpperCAmelCase   :					List[str]													=     scheduler_class(**a_			)

					__UpperCAmelCase							, __UpperCAmelCase   :					List[Any]													=     10, 0.0

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

					scheduler.set_timesteps(a_			)

					for t in scheduler.timesteps:
							__UpperCAmelCase   :					str													=     model(a_	,	a_			)
							__UpperCAmelCase   :					List[str]													=     scheduler.step(a_	,	a_	,	a_	,	a_			).prev_sample

					return sample




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





					for timesteps in [1_00, 5_00, 10_00]:
							self.check_over_configs(num_train_timesteps=a_			)




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





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

					__UpperCAmelCase   :					Optional[int]													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Dict													=     self.get_scheduler_config(steps_offset=1			)
					__UpperCAmelCase   :					List[str]													=     scheduler_class(**a_			)
					scheduler.set_timesteps(5			)
					assert torch.equal(scheduler.timesteps	,	torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]			)			)




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





					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=a_	,	beta_end=a_			)




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





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




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





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




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





					for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=a_			)




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





					for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=a_			)




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





					for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=a_			)




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





					self.check_over_configs(thresholding=a_			)
					for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(
									    thresholding=a_	,	prediction_type=a_	,	sample_max_value=a_	,	)




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





					for t in [1, 10, 49]:
							self.check_over_forward(time_step=a_			)




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





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




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





					for t, eta in zip([1, 10, 49]	,	[0.0, 0.5, 1.0]			):
							self.check_over_forward(time_step=a_	,	eta=a_			)




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





					__UpperCAmelCase   :					List[Any]													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					List[Any]													=     self.get_scheduler_config()
					__UpperCAmelCase   :					Optional[Any]													=     scheduler_class(**a_			)

					assert torch.sum(torch.abs(scheduler._get_variance(0	,	0			) - 0.0			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_20	,	4_00			) - 0.1_4_7_7_1			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_80	,	9_60			) - 0.3_2_4_6_0			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(0	,	0			) - 0.0			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87	,	4_86			) - 0.0_0_9_7_9			)			) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99	,	9_98			) - 0.0_2			)			) < 1e-5




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





					__UpperCAmelCase   :					str													=     self.full_loop()

					__UpperCAmelCase   :					Dict													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					Union[str, Any]													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1e-2
					assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3




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





					__UpperCAmelCase   :					Dict													=     self.full_loop(prediction_type='''v_prediction'''			)

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

					assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1e-2
					assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3




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





					__UpperCAmelCase   :					Optional[Any]													=     self.full_loop(set_alpha_to_one=a_	,	beta_start=0.0_1			)
					__UpperCAmelCase   :					Dict													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					Dict													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1e-2
					assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3




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





					__UpperCAmelCase   :					int													=     self.full_loop(set_alpha_to_one=a_	,	beta_start=0.0_1			)
					__UpperCAmelCase   :					Optional[Any]													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					Optional[int]													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1e-2
					assert abs(result_mean.item() - 0.1_9_4_1			) < 1e-3










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			)	,	)





