




"""simple docstring"""

from typing import TYPE_CHECKING

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


__lowerCamelCase										=    {
    "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:
						__lowerCamelCase										=    [
						    "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

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




'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Tuple										=  (DDPMScheduler,)




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{
												    """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(**_lowercase )
												return config




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												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=_lowercase       ,  beta_end=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

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

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

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

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config(prediction_type="""v_prediction""" )
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

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

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

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

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

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

												scheduler.set_timesteps(timesteps=_lowercase )

												_lowerCAmelCase    		=					scheduler.timesteps

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

																		_lowerCAmelCase    		=					scheduler.previous_timestep(_lowercase )
																		_lowerCAmelCase    		=					prev_t.item()

																		self.assertEqual(_lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

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

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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]
												_lowerCAmelCase    		=					len(_lowercase )

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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[scheduler.config.num_train_timesteps]

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