





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Union[str, Any]								=	(UnCLIPScheduler,)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				**lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   {
										    'num_train_timesteps': 10_00,
										    'variance_type': 'fixed_small_log',
										    'clip_sample': True,
										    'clip_sample_range': 1.0,
										    'prediction_type': 'epsilon',
										}

										config.update(**lowerCamelCase_					)
										return config







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										for time_step in [0, 5_00, 9_99]:
														for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																		if prev_timestep is not None and prev_timestep >= time_step:
																						continue

																		self.check_over_forward(time_step=lowerCamelCase_ ,				prev_timestep=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.scheduler_classes[0]
										_snake_case :			str  						=   self.get_scheduler_config(variance_type='fixed_small_log'					)
										_snake_case :			str  						=   scheduler_class(**lowerCamelCase_					)

										assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000e-10					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.054_9625					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.999_4987					)					) < 1e-5







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.scheduler_classes[0]
										_snake_case :			Any  						=   self.get_scheduler_config(variance_type='learned_range'					)
										_snake_case :			Dict  						=   scheduler_class(**lowerCamelCase_					)

										_snake_case :			List[Any]  						=   0.5

										assert scheduler._get_variance(1 ,				predicted_variance=lowerCamelCase_					) - -10.171_2790 < 1e-5
										assert scheduler._get_variance(4_87 ,				predicted_variance=lowerCamelCase_					) - -5.799_8052 < 1e-5
										assert scheduler._get_variance(9_99 ,				predicted_variance=lowerCamelCase_					) - -0.001_0011 < 1e-5







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.scheduler_classes[0]
										_snake_case :			str  						=   self.get_scheduler_config()
										_snake_case :			Optional[int]  						=   scheduler_class(**lowerCamelCase_					)

										_snake_case :			Tuple  						=   scheduler.timesteps

										_snake_case :			Dict  						=   self.dummy_model()
										_snake_case :			Any  						=   self.dummy_sample_deter
										_snake_case :			List[str]  						=   torch.manual_seed(0					)

										for i, t in enumerate(lowerCamelCase_					):
														# 1. predict noise residual
														_snake_case :			Dict  						=   model(lowerCamelCase_ ,				lowerCamelCase_					)

														# 2. predict previous mean of sample x_t-1
														_snake_case :			Union[str, Any]  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				generator=lowerCamelCase_					).prev_sample

														_snake_case :			Union[str, Any]  						=   pred_prev_sample

										_snake_case :			str  						=   torch.sum(torch.abs(lowerCamelCase_					)					)
										_snake_case :			Dict  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_sum.item() - 252.268_2495					) < 1e-2
										assert abs(result_mean.item() - 0.328_4743					) < 1e-3







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   self.scheduler_classes[0]
										_snake_case :			Optional[int]  						=   self.get_scheduler_config()
										_snake_case :			Tuple  						=   scheduler_class(**lowerCamelCase_					)

										scheduler.set_timesteps(25					)

										_snake_case :			Union[str, Any]  						=   scheduler.timesteps

										_snake_case :			Tuple  						=   self.dummy_model()
										_snake_case :			str  						=   self.dummy_sample_deter
										_snake_case :			List[Any]  						=   torch.manual_seed(0					)

										for i, t in enumerate(lowerCamelCase_					):
														# 1. predict noise residual
														_snake_case :			Any  						=   model(lowerCamelCase_ ,				lowerCamelCase_					)

														if i + 1 == timesteps.shape[0]:
																		_snake_case :			List[Any]  						=   None
														else:
																		_snake_case :			Union[str, Any]  						=   timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														_snake_case :			Any  						=   scheduler.step(
														    lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				prev_timestep=lowerCamelCase_ ,				generator=lowerCamelCase_					).prev_sample

														_snake_case :			Optional[int]  						=   pred_prev_sample

										_snake_case :			Tuple  						=   torch.sum(torch.abs(lowerCamelCase_					)					)
										_snake_case :			Dict  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_sum.item() - 258.204_4983					) < 1e-2
										assert abs(result_mean.item() - 0.336_2038					) < 1e-3







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										pass







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										pass













from typing import TYPE_CHECKING

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


lowercase_		:				Optional[Any]				=						{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Tuple				=						[
		    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoCBertForCausalLM''',
		    '''RoCBertForMaskedLM''',
		    '''RoCBertForMultipleChoice''',
		    '''RoCBertForPreTraining''',
		    '''RoCBertForQuestionAnswering''',
		    '''RoCBertForSequenceClassification''',
		    '''RoCBertForTokenClassification''',
		    '''RoCBertLayer''',
		    '''RoCBertModel''',
		    '''RoCBertPreTrainedModel''',
		    '''load_tf_weights_in_roc_bert''',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		lowercase_		:				Tuple				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






