



from typing import TYPE_CHECKING

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


UpperCAmelCase   :							Union[str, 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:
	UpperCAmelCase   :							List[str]							 =[
	    """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

	UpperCAmelCase   :							Any							 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						(CMStochasticIterativeScheduler,)
				lowercase__   						=						10

				def       _lowerCamelCase					( self     , **snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   {
									    "num_train_timesteps": 201,
									    "sigma_min": 0.002,
									    "sigma_max": 80.0,
									}

									config.update(**snake_case__    )
									return config

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   10

									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   self.scheduler_classes[0](**snake_case__    )

									scheduler.set_timesteps(snake_case__    )

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

									UpperCamelCase_		  =   self.dummy_sample
									UpperCamelCase_		  =   0.1 * sample

									UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__    ).prev_sample
									UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__    ).prev_sample

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

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									for clip_denoised in [True, False]:
														self.check_over_configs(clip_denoised=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   1
									scheduler.set_timesteps(snake_case__    )
									UpperCamelCase_		  =   scheduler.timesteps

									UpperCamelCase_		  =   torch.manual_seed(0    )

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

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

														# 2. predict noise residual
														UpperCamelCase_		  =   model(snake_case__     , snake_case__    )

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

														UpperCamelCase_		  =   pred_prev_sample

									UpperCamelCase_		  =   torch.sum(torch.abs(snake_case__    )    )
									UpperCamelCase_		  =   torch.mean(torch.abs(snake_case__    )    )

									assert abs(result_sum.item() - 192.7_614    ) < 1e-2
									assert abs(result_mean.item() - 0.2_510    ) < 1e-3

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

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

									UpperCamelCase_		  =   torch.manual_seed(0    )

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

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

														# 2. predict noise residual
														UpperCamelCase_		  =   model(snake_case__     , snake_case__    )

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

														UpperCamelCase_		  =   pred_prev_sample

									UpperCamelCase_		  =   torch.sum(torch.abs(snake_case__    )    )
									UpperCamelCase_		  =   torch.mean(torch.abs(snake_case__    )    )

									assert abs(result_sum.item() - 347.6_357    ) < 1e-2
									assert abs(result_mean.item() - 0.4_527    ) < 1e-3

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

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

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

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

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

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

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [scheduler.config.num_train_timesteps]

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