


"""simple docstring"""




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[Any]										=				(DDPMParallelScheduler,)

		def 			__UpperCAmelCase       (  self							, **_a      ):
				__a           =			{
				    '''num_train_timesteps''': 1_000,
				    '''beta_start''': 0.0001,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**_a      )
				return config

		def 			__UpperCAmelCase       (  self      ):
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=_a      )

		def 			__UpperCAmelCase       (  self      ):
				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							, [0.002, 0.02, 0.2, 2]      ):
						self.check_over_configs(beta_start=_a							, beta_end=_a      )

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

		def 			__UpperCAmelCase       (  self      ):
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=_a      )

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

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

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

		def 			__UpperCAmelCase       (  self      ):
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config()
				__a           =			scheduler_class(**_a      )

				assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0979      )      ) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1E-5

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config()
				__a           =			scheduler_class(**_a      )

				__a           =			len(_a      )

				__a           =			self.dummy_model()
				__a           =			self.dummy_sample_deter
				__a           =			self.dummy_sample_deter + 0.1
				__a           =			self.dummy_sample_deter - 0.1

				__a           =			samplea.shape[0]
				__a           =			torch.stack([samplea, samplea, samplea]							, dim=0      )
				__a           =			torch.arange(_a      )[0:3, None].repeat(1							, _a      )

				__a           =			model(samples.flatten(0							, 1      )							, timesteps.flatten(0							, 1      )      )
				__a           =			scheduler.batch_step_no_noise(_a							, timesteps.flatten(0							, 1      )							, samples.flatten(0							, 1      )      )

				__a           =			torch.sum(torch.abs(_a      )      )
				__a           =			torch.mean(torch.abs(_a      )      )

				assert abs(result_sum.item() - 1153.1833      ) < 1E-2
				assert abs(result_mean.item() - 0.5005      ) < 1E-3

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config()
				__a           =			scheduler_class(**_a      )

				__a           =			len(_a      )

				__a           =			self.dummy_model()
				__a           =			self.dummy_sample_deter
				__a           =			torch.manual_seed(0      )

				for t in reversed(range(_a      )      ):
						# 1. predict noise residual
						__a           =			model(_a							, _a      )

						# 2. predict previous mean of sample x_t-1
						__a           =			scheduler.step(_a							, _a							, _a							, generator=_a      ).prev_sample

						__a           =			pred_prev_sample

				__a           =			torch.sum(torch.abs(_a      )      )
				__a           =			torch.mean(torch.abs(_a      )      )

				assert abs(result_sum.item() - 258.9606      ) < 1E-2
				assert abs(result_mean.item() - 0.3372      ) < 1E-3

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config(prediction_type='''v_prediction'''      )
				__a           =			scheduler_class(**_a      )

				__a           =			len(_a      )

				__a           =			self.dummy_model()
				__a           =			self.dummy_sample_deter
				__a           =			torch.manual_seed(0      )

				for t in reversed(range(_a      )      ):
						# 1. predict noise residual
						__a           =			model(_a							, _a      )

						# 2. predict previous mean of sample x_t-1
						__a           =			scheduler.step(_a							, _a							, _a							, generator=_a      ).prev_sample

						__a           =			pred_prev_sample

				__a           =			torch.sum(torch.abs(_a      )      )
				__a           =			torch.mean(torch.abs(_a      )      )

				assert abs(result_sum.item() - 202.0296      ) < 1E-2
				assert abs(result_mean.item() - 0.2631      ) < 1E-3

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config()
				__a           =			scheduler_class(**_a      )

				__a           =			[100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=_a      )

				__a           =			scheduler.timesteps

				for i, timestep in enumerate(_a      ):
						if i == len(_a      ) - 1:
								__a           =			-1
						else:
								__a           =			timesteps[i + 1]

						__a           =			scheduler.previous_timestep(_a      )
						__a           =			prev_t.item()

						self.assertEqual(_a							, _a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config()
				__a           =			scheduler_class(**_a      )

				__a           =			[100, 87, 50, 51, 0]

				with self.assertRaises(_a							, msg='''`custom_timesteps` must be in descending order.'''      ):
						scheduler.set_timesteps(timesteps=_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config()
				__a           =			scheduler_class(**_a      )

				__a           =			[100, 87, 50, 1, 0]
				__a           =			len(_a      )

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





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.scheduler_classes[0]
				__a           =			self.get_scheduler_config()
				__a           =			scheduler_class(**_a      )

				__a           =			[scheduler.config.num_train_timesteps]

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










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






