



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		List[str]		      = (DDIMParallelScheduler,)
	__SCREAMING_SNAKE_CASE       :		Any		      = (('eta', 0.0), ('num_inference_steps', 50))


	def    _a     (self	,				**lowercase							):
				A_     :						Union[str, Any]     =					{
				    """num_train_timesteps""": 1000,
				    """beta_start""": 0.00_01,
				    """beta_end""": 0.02,
				    """beta_schedule""": """linear""",
				    """clip_sample""": True,
				}

				config.update(**lowercase							)
				return config


	def    _a     (self	,				**lowercase							):
				A_     :						List[Any]     =					self.scheduler_classes[0]
				A_     :						int     =					self.get_scheduler_config(**lowercase							)
				A_     :						List[str]     =					scheduler_class(**lowercase							)

				A_     :						int     =					10, 0.0

				A_     :						Optional[int]     =					self.dummy_model()
				A_     :						Any     =					self.dummy_sample_deter

				scheduler.set_timesteps(lowercase							)

				for t in scheduler.timesteps:
							A_     :						Dict     =					model(lowercase	,				lowercase							)
							A_     :						int     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				lowercase							).prev_sample

				return sample


	def    _a     (self							):
				for timesteps in [100, 500, 1000]:
							self.check_over_configs(num_train_timesteps=lowercase							)


	def    _a     (self							):
				for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=lowercase							)

				A_     :						Optional[int]     =					self.scheduler_classes[0]
				A_     :						Dict     =					self.get_scheduler_config(steps_offset=1							)
				A_     :						List[Any]     =					scheduler_class(**lowercase							)
				scheduler.set_timesteps(5							)
				assert torch.equal(scheduler.timesteps	,				torch.LongTensor([801, 601, 401, 201, 1]							)							)


	def    _a     (self							):
				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]	,				[0.0_02, 0.02, 0.2, 2]							):
							self.check_over_configs(beta_start=lowercase	,				beta_end=lowercase							)


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


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


	def    _a     (self							):
				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowercase							)


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


	def    _a     (self							):
				for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=lowercase							)


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


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


	def    _a     (self							):
				for t, num_inference_steps in zip([1, 10, 50]	,				[10, 50, 500]							):
							self.check_over_forward(time_step=lowercase	,				num_inference_steps=lowercase							)


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


	def    _a     (self							):
				A_     :						int     =					self.scheduler_classes[0]
				A_     :						str     =					self.get_scheduler_config()
				A_     :						Any     =					scheduler_class(**lowercase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0	,				0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(420	,				400							) - 0.1_47_71							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(980	,				960							) - 0.3_24_60							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(0	,				0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(487	,				486							) - 0.0_09_79							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999	,				998							) - 0.02							)							) < 1E-5


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.scheduler_classes[0]
				A_     :						Optional[int]     =					self.get_scheduler_config()
				A_     :						Any     =					scheduler_class(**lowercase							)

				A_     :						Optional[int]     =					10, 0.0
				scheduler.set_timesteps(lowercase							)

				A_     :						Any     =					self.dummy_model()
				A_     :						Dict     =					self.dummy_sample_deter
				A_     :						List[str]     =					self.dummy_sample_deter + 0.1
				A_     :						Any     =					self.dummy_sample_deter - 0.1

				A_     :						Optional[Any]     =					samplea.shape[0]
				A_     :						Optional[int]     =					torch.stack([samplea, samplea, samplea]	,				dim=0							)
				A_     :						str     =					torch.arange(lowercase							)[0:3, None].repeat(1	,				lowercase							)

				A_     :						Tuple     =					model(samples.flatten(0	,				1							)	,				timesteps.flatten(0	,				1							)							)
				A_     :						int     =					scheduler.batch_step_no_noise(lowercase	,				timesteps.flatten(0	,				1							)	,				samples.flatten(0	,				1							)	,				lowercase							)

				A_     :						int     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Union[str, Any]     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 1147.7904							) < 1E-2
				assert abs(result_mean.item() - 0.49_82							) < 1E-3


	def    _a     (self							):
				A_     :						Tuple     =					self.full_loop()

				A_     :						int     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Union[str, Any]     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 172.0067							) < 1E-2
				assert abs(result_mean.item() - 0.22_39_67							) < 1E-3


	def    _a     (self							):
				A_     :						Union[str, Any]     =					self.full_loop(prediction_type="""v_prediction"""							)

				A_     :						Optional[Any]     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Optional[Any]     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 52.53_02							) < 1E-2
				assert abs(result_mean.item() - 0.06_84							) < 1E-3


	def    _a     (self							):
				# We specify different beta, so that the first alpha is 0.99
				A_     :						str     =					self.full_loop(set_alpha_to_one=lowercase	,				beta_start=0.01							)
				A_     :						int     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Tuple     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 149.8295							) < 1E-2
				assert abs(result_mean.item() - 0.19_51							) < 1E-3


	def    _a     (self							):
				# We specify different beta, so that the first alpha is 0.99
				A_     :						List[str]     =					self.full_loop(set_alpha_to_one=lowercase	,				beta_start=0.01							)
				A_     :						List[Any]     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Any     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 149.0784							) < 1E-2
				assert abs(result_mean.item() - 0.19_41							) < 1E-3




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Dict     =					10


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4]
				A_     :						List[str]     =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_     :						Tuple     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						Any     =					"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,   A_     :						Optional[int]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					""""""
				A_,   A_     :						Union[str, Any]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						List[Any]     =					(
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,   A_     :						int     =					process_story(lowercase							)

				A_     :						List[Any]     =					[
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase	,				lowercase							)

				A_     :						Tuple     =					["""It was the best of times."""]
				self.assertEqual(lowercase	,				lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					torch.tensor([1, 2, 3, 4]							)
				A_     :						Union[str, Any]     =					torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				0							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_     :						List[str]     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				23							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_     :						Tuple     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				1							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						List[str]     =					101
				A_     :						str     =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_     :						int     =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_     :						Optional[Any]     =					compute_token_type_ids(lowercase	,				lowercase							)
				np.testing.assert_array_equal(lowercase	,				lowercase							)