import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class _a   (       __SCREAMING_SNAKE_CASE					):

	'''simple docstring'''



	lowerCamelCase_    :			Dict	  =       (DDIMScheduler,)
	lowerCamelCase_    :			List[Any]	  =       (("""eta""", 0.0), ("""num_inference_steps""", 5_0))

	def      __UpperCAmelCase(   self							, **__UpperCAmelCase    ):
			__A							:    int         =      {
			    'num_train_timesteps': 1_000,
			    'beta_start': 0.00_01,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			    'clip_sample': True,
			}

			config.update(**lowerCAmelCase__    )
			return config

	def      __UpperCAmelCase(   self							, **__UpperCAmelCase    ):
			__A							:    str         =      self.scheduler_classes[0]
			__A							:    List[str]         =      self.get_scheduler_config(**lowerCAmelCase__    )
			__A							:    List[Any]         =      scheduler_class(**lowerCAmelCase__    )

			__A							:    Dict         =      10, 0.0

			__A							:    Optional[Any]         =      self.dummy_model()
			__A							:    List[Any]         =      self.dummy_sample_deter

			scheduler.set_timesteps(lowerCAmelCase__    )

			for t in scheduler.timesteps:
					__A							:    List[Any]         =      model(lowerCAmelCase__							, lowerCAmelCase__    )
					__A							:    Tuple         =      scheduler.step(lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__    ).prev_sample

			return sample

	def      __UpperCAmelCase(   self    ):




			for timesteps in [100, 500, 1_000]:
					self.check_over_configs(num_train_timesteps=lowerCAmelCase__    )

	def      __UpperCAmelCase(   self    ):




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

			__A							:    Dict         =      self.scheduler_classes[0]
			__A							:    str         =      self.get_scheduler_config(steps_offset=1    )
			__A							:    Any         =      scheduler_class(**lowerCAmelCase__    )
			scheduler.set_timesteps(5    )
			assert torch.equal(scheduler.timesteps							, torch.LongTensor([801, 601, 401, 201, 1]    )    )

	def      __UpperCAmelCase(   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=lowerCAmelCase__							, beta_end=lowerCAmelCase__    )

	def      __UpperCAmelCase(   self    ):




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

	def      __UpperCAmelCase(   self    ):




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

	def      __UpperCAmelCase(   self    ):




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

	def      __UpperCAmelCase(   self    ):




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

	def      __UpperCAmelCase(   self    ):




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

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

	def      __UpperCAmelCase(   self    ):




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

	def      __UpperCAmelCase(   self    ):




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

	def      __UpperCAmelCase(   self    ):




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

	def      __UpperCAmelCase(   self    ):
			__A							:    str         =      self.scheduler_classes[0]
			__A							:    Any         =      self.get_scheduler_config()
			__A							:    Dict         =      scheduler_class(**lowerCAmelCase__    )

			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      __UpperCAmelCase(   self    ):
			__A							:    Optional[int]         =      self.full_loop()

			__A							:    List[str]         =      torch.sum(torch.abs(lowerCAmelCase__    )    )
			__A							:    Any         =      torch.mean(torch.abs(lowerCAmelCase__    )    )

			assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
			assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

	def      __UpperCAmelCase(   self    ):
			__A							:    Dict         =      self.full_loop(prediction_type="v_prediction"    )

			__A							:    List[Any]         =      torch.sum(torch.abs(lowerCAmelCase__    )    )
			__A							:    Union[str, Any]         =      torch.mean(torch.abs(lowerCAmelCase__    )    )

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

	def      __UpperCAmelCase(   self    ):
			__A							:    List[Any]         =      self.full_loop(set_alpha_to_one=lowerCAmelCase__							, beta_start=0.01    )
			__A							:    Union[str, Any]         =      torch.sum(torch.abs(lowerCAmelCase__    )    )
			__A							:    Optional[Any]         =      torch.mean(torch.abs(lowerCAmelCase__    )    )

			assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
			assert abs(result_mean.item() - 0.19_51    ) < 1e-3







	def      __UpperCAmelCase(   self    ):
			__A							:    List[Any]         =      self.full_loop(set_alpha_to_one=lowerCAmelCase__							, beta_start=0.01    )
			__A							:    Dict         =      torch.sum(torch.abs(lowerCAmelCase__    )    )
			__A							:    Optional[Any]         =      torch.mean(torch.abs(lowerCAmelCase__    )    )

			assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
			assert abs(result_mean.item() - 0.19_41    ) < 1e-3









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__       :	List[Any]     				={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Optional[Any]     				=['MBart50Tokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Any     				=['MBart50TokenizerFast']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__       :	Optional[int]     				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





