



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  __magic_name__   (							A__						):
		lowercase      :       Dict          =(IPNDMScheduler,)
		lowercase      :       Optional[int]          =(('''num_inference_steps''', 50),)






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		**UpperCamelCase__      :     Optional[Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     {"num_train_timesteps": 10_00}
								config.update(**UpperCamelCase__      )
								return config






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[int]=0				,		**UpperCamelCase__      :     Any      )				->					Tuple:
								'''simple docstring'''







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

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

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

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

														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample
														UpperCAmelCase         =     new_scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample

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

														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample
														UpperCAmelCase         =     new_scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample

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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Any=0				,		**UpperCamelCase__      :     Union[str, Any]      )				->					int:
								'''simple docstring'''







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

								for scheduler_class in self.scheduler_classes:
														UpperCAmelCase         =     self.get_scheduler_config()
														UpperCAmelCase         =     scheduler_class(**UpperCamelCase__      )
														scheduler.set_timesteps(UpperCamelCase__      )

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

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

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

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

														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample
														UpperCAmelCase         =     new_scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample

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

														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample
														UpperCAmelCase         =     new_scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		**UpperCamelCase__      :     Any      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.scheduler_classes[0]
								UpperCAmelCase         =     self.get_scheduler_config(**UpperCamelCase__      )
								UpperCAmelCase         =     scheduler_class(**UpperCamelCase__      )

								UpperCAmelCase         =     10
								UpperCAmelCase         =     self.dummy_model()
								UpperCAmelCase         =     self.dummy_sample_deter
								scheduler.set_timesteps(UpperCamelCase__      )

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

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

								return sample






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     dict(self.forward_default_kwargs      )

								UpperCAmelCase         =     kwargs.pop("num_inference_steps"				,		UpperCamelCase__      )

								for scheduler_class in self.scheduler_classes:
														UpperCAmelCase         =     self.get_scheduler_config()
														UpperCAmelCase         =     scheduler_class(**UpperCamelCase__      )

														UpperCAmelCase         =     self.dummy_sample
														UpperCAmelCase         =     0.1 * sample

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

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

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

														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample
														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample

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

														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample
														UpperCAmelCase         =     scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		**UpperCamelCase__      ).prev_sample

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[int]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Dict:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     self.full_loop()
								UpperCAmelCase         =     torch.mean(torch.abs(UpperCamelCase__      )      )

								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


__lowerCamelCase     :							Optional[int]  							=				{
    "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     :							List[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

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



