




"""simple docstring"""







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						(CMStochasticIterativeScheduler,)
						_a    						=						10


						def   __lowercase (							self				:			Dict						,						**lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				{
										    """num_train_timesteps""": 201,
										    """sigma_min""": 0.002,
										    """sigma_max""": 80.0,
										}

										config.update(**lowerCAmelCase					)
										return config


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				10

										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				self.scheduler_classes[0](**lowerCAmelCase					)

										scheduler.set_timesteps(lowerCAmelCase					)

										lowerCAmelCase  =				scheduler.timesteps[0]
										lowerCAmelCase  =				scheduler.timesteps[1]

										lowerCAmelCase  =				self.dummy_sample
										lowerCAmelCase  =				0.1 * sample

										lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					).prev_sample
										lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					).prev_sample

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


						def   __lowercase (							self				:			Dict					):
										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=lowerCAmelCase					)


						def   __lowercase (							self				:			str					):
										for clip_denoised in [True, False]:
														self.check_over_configs(clip_denoised=lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				1
										scheduler.set_timesteps(lowerCAmelCase					)
										lowerCAmelCase  =				scheduler.timesteps

										lowerCAmelCase  =				torch.manual_seed(0					)

										lowerCAmelCase  =				self.dummy_model()
										lowerCAmelCase  =				self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(lowerCAmelCase					):
														# 1. scale model input
														lowerCAmelCase  =				scheduler.scale_model_input(lowerCAmelCase						,						lowerCAmelCase					)

														# 2. predict noise residual
														lowerCAmelCase  =				model(lowerCAmelCase						,						lowerCAmelCase					)

														# 3. predict previous sample x_t-1
														lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						generator=lowerCAmelCase					).prev_sample

														lowerCAmelCase  =				pred_prev_sample

										lowerCAmelCase  =				torch.sum(torch.abs(lowerCAmelCase					)					)
										lowerCAmelCase  =				torch.mean(torch.abs(lowerCAmelCase					)					)

										assert abs(result_sum.item() - 192.7614					) < 1e-2
										assert abs(result_mean.item() - 0.2510					) < 1e-3


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[106, 0]
										scheduler.set_timesteps(timesteps=lowerCAmelCase					)
										lowerCAmelCase  =				scheduler.timesteps

										lowerCAmelCase  =				torch.manual_seed(0					)

										lowerCAmelCase  =				self.dummy_model()
										lowerCAmelCase  =				self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
														# 1. scale model input
														lowerCAmelCase  =				scheduler.scale_model_input(lowerCAmelCase						,						lowerCAmelCase					)

														# 2. predict noise residual
														lowerCAmelCase  =				model(lowerCAmelCase						,						lowerCAmelCase					)

														# 3. predict previous sample x_t-1
														lowerCAmelCase  =				scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						generator=lowerCAmelCase					).prev_sample

														lowerCAmelCase  =				pred_prev_sample

										lowerCAmelCase  =				torch.sum(torch.abs(lowerCAmelCase					)					)
										lowerCAmelCase  =				torch.mean(torch.abs(lowerCAmelCase					)					)

										assert abs(result_sum.item() - 347.6357					) < 1e-2
										assert abs(result_mean.item() - 0.4527					) < 1e-3


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[39, 30, 12, 15, 0]

										with self.assertRaises(lowerCAmelCase						,						msg="""`timesteps` must be in descending order."""					):
														scheduler.set_timesteps(timesteps=lowerCAmelCase					)


						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[39, 30, 12, 1, 0]
										lowerCAmelCase  =				len(lowerCAmelCase					)

										with self.assertRaises(lowerCAmelCase						,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""					):
														scheduler.set_timesteps(num_inference_steps=lowerCAmelCase						,						timesteps=lowerCAmelCase					)




						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**lowerCAmelCase					)

										lowerCAmelCase  =				[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    lowerCAmelCase						,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""						,						):
														scheduler.set_timesteps(timesteps=lowerCAmelCase					)











"""simple docstring"""







from typing import TYPE_CHECKING

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


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





