


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Tuple			       =		(IPNDMScheduler,)
		UpperCamelCase     :					Union[str, Any]			       =		(("num_inference_steps", 5_0),)







		def 					__A   ( self		,      **A	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  {"""num_train_timesteps""": 10_00}
					config.update(**A	)
					return config







		def 					__A   ( self		,      A=0		,      **A	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  dict(self.forward_default_kwargs	)
					lowerCamelCase    		=  kwargs.pop("""num_inference_steps"""		,      A	)
					lowerCamelCase    		=  self.dummy_sample
					lowerCamelCase    		=  0.1 * sample
					lowerCamelCase    		=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
								lowerCamelCase    		=  self.get_scheduler_config(**A	)
								lowerCamelCase    		=  scheduler_class(**A	)
								scheduler.set_timesteps(A	)
								# copy over dummy past residuals
								lowerCamelCase    		=  dummy_past_residuals[:]

								if time_step is None:
											lowerCamelCase    		=  scheduler.timesteps[len(scheduler.timesteps	) // 2]

								with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(A	)
											lowerCamelCase    		=  scheduler_class.from_pretrained(A	)
											new_scheduler.set_timesteps(A	)
											# copy over dummy past residuals
											lowerCamelCase    		=  dummy_past_residuals[:]

								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample
								lowerCamelCase    		=  new_scheduler.step(A		,      A		,      A		,      **A	).prev_sample

								assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample
								lowerCamelCase    		=  new_scheduler.step(A		,      A		,      A		,      **A	).prev_sample

								assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					pass







		def 					__A   ( self		,      A=0		,      **A	)  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  dict(self.forward_default_kwargs	)
					lowerCamelCase    		=  kwargs.pop("""num_inference_steps"""		,      A	)
					lowerCamelCase    		=  self.dummy_sample
					lowerCamelCase    		=  0.1 * sample
					lowerCamelCase    		=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
								lowerCamelCase    		=  self.get_scheduler_config()
								lowerCamelCase    		=  scheduler_class(**A	)
								scheduler.set_timesteps(A	)

								# copy over dummy past residuals (must be after setting timesteps)
								lowerCamelCase    		=  dummy_past_residuals[:]

								if time_step is None:
											lowerCamelCase    		=  scheduler.timesteps[len(scheduler.timesteps	) // 2]

								with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(A	)
											lowerCamelCase    		=  scheduler_class.from_pretrained(A	)
											# copy over dummy past residuals
											new_scheduler.set_timesteps(A	)

											# copy over dummy past residual (must be after setting timesteps)
											lowerCamelCase    		=  dummy_past_residuals[:]

								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample
								lowerCamelCase    		=  new_scheduler.step(A		,      A		,      A		,      **A	).prev_sample

								assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample
								lowerCamelCase    		=  new_scheduler.step(A		,      A		,      A		,      **A	).prev_sample

								assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"







		def 					__A   ( self		,      **A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  self.scheduler_classes[0]
					lowerCamelCase    		=  self.get_scheduler_config(**A	)
					lowerCamelCase    		=  scheduler_class(**A	)

					lowerCamelCase    		=  10
					lowerCamelCase    		=  self.dummy_model()
					lowerCamelCase    		=  self.dummy_sample_deter
					scheduler.set_timesteps(A	)

					for i, t in enumerate(scheduler.timesteps	):
								lowerCamelCase    		=  model(A		,      A	)
								lowerCamelCase    		=  scheduler.step(A		,      A		,      A	).prev_sample

					for i, t in enumerate(scheduler.timesteps	):
								lowerCamelCase    		=  model(A		,      A	)
								lowerCamelCase    		=  scheduler.step(A		,      A		,      A	).prev_sample

					return sample







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  dict(self.forward_default_kwargs	)

					lowerCamelCase    		=  kwargs.pop("""num_inference_steps"""		,      A	)

					for scheduler_class in self.scheduler_classes:
								lowerCamelCase    		=  self.get_scheduler_config()
								lowerCamelCase    		=  scheduler_class(**A	)

								lowerCamelCase    		=  self.dummy_sample
								lowerCamelCase    		=  0.1 * sample

								if num_inference_steps is not None and hasattr(A		,      """set_timesteps"""	):
											scheduler.set_timesteps(A	)
								elif num_inference_steps is not None and not hasattr(A		,      """set_timesteps"""	):
											lowerCamelCase    		=  num_inference_steps

								# copy over dummy past residuals (must be done after set_timesteps)
								lowerCamelCase    		=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
								lowerCamelCase    		=  dummy_past_residuals[:]

								lowerCamelCase    		=  scheduler.timesteps[5]
								lowerCamelCase    		=  scheduler.timesteps[6]

								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample
								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample

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

								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample
								lowerCamelCase    		=  scheduler.step(A		,      A		,      A		,      **A	).prev_sample

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







		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







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







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					for t, num_inference_steps in zip([1, 5, 10]		,      [10, 50, 1_00]	):
								self.check_over_forward(num_inference_steps=A		,      time_step=A	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  self.full_loop()
					lowerCamelCase    		=  torch.mean(torch.abs(A	)	)

					assert abs(result_mean.item() - 2_54_05_29	) < 10





from typing import TYPE_CHECKING

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


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

