



"""simple docstring"""
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Dict						=     (IPNDMScheduler,)
					_A		:			Any						=     (("""num_inference_steps""", 5_0),)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     **snake_case:							str						)   ->	Optional[int]:
											snake_case_ :Tuple						=			{"""num_train_timesteps""": 1_000}
											config.update(**snake_case						)
											return config






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							int=0						,     **snake_case:							str						)   ->	Optional[Any]:
											snake_case_ :List[Any]						=			dict(self.forward_default_kwargs						)
											snake_case_ :str						=			kwargs.pop("""num_inference_steps"""						,     snake_case						)
											snake_case_ :Optional[int]						=			self.dummy_sample
											snake_case_ :Tuple						=			0.1 * sample
											snake_case_ :str						=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

											for scheduler_class in self.scheduler_classes:
																	snake_case_ :int						=			self.get_scheduler_config(**snake_case						)
																	snake_case_ :str						=			scheduler_class(**snake_case						)
																	scheduler.set_timesteps(snake_case						)
																	# copy over dummy past residuals
																	snake_case_ :str						=			dummy_past_residuals[:]

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

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

																	snake_case_ :int						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Optional[int]						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

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

																	snake_case_ :Optional[int]						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Tuple						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

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






					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											pass






					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							str=0						,     **snake_case:							Dict						)   ->	Tuple:
											snake_case_ :List[str]						=			dict(self.forward_default_kwargs						)
											snake_case_ :Optional[int]						=			kwargs.pop("""num_inference_steps"""						,     snake_case						)
											snake_case_ :Dict						=			self.dummy_sample
											snake_case_ :Optional[Any]						=			0.1 * sample
											snake_case_ :Dict						=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

											for scheduler_class in self.scheduler_classes:
																	snake_case_ :Tuple						=			self.get_scheduler_config()
																	snake_case_ :List[str]						=			scheduler_class(**snake_case						)
																	scheduler.set_timesteps(snake_case						)

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

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

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

																							# copy over dummy past residual (must be after setting timesteps)
																							snake_case_ :Union[str, Any]						=			dummy_past_residuals[:]

																	snake_case_ :List[Any]						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Union[str, Any]						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

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

																	snake_case_ :Tuple						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :List[str]						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

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






					def    lowerCAmelCase_				(       self:							Optional[int]						,     **snake_case:							Tuple						)   ->	str:
											snake_case_ :int						=			self.scheduler_classes[0]
											snake_case_ :List[Any]						=			self.get_scheduler_config(**snake_case						)
											snake_case_ :int						=			scheduler_class(**snake_case						)

											snake_case_ :str						=			10
											snake_case_ :Optional[Any]						=			self.dummy_model()
											snake_case_ :int						=			self.dummy_sample_deter
											scheduler.set_timesteps(snake_case						)

											for i, t in enumerate(scheduler.timesteps						):
																	snake_case_ :Tuple						=			model(snake_case						,     snake_case						)
																	snake_case_ :Dict						=			scheduler.step(snake_case						,     snake_case						,     snake_case						).prev_sample

											for i, t in enumerate(scheduler.timesteps						):
																	snake_case_ :Optional[int]						=			model(snake_case						,     snake_case						)
																	snake_case_ :Any						=			scheduler.step(snake_case						,     snake_case						,     snake_case						).prev_sample

											return sample






					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											snake_case_ :str						=			dict(self.forward_default_kwargs						)

											snake_case_ :int						=			kwargs.pop("""num_inference_steps"""						,     snake_case						)

											for scheduler_class in self.scheduler_classes:
																	snake_case_ :List[str]						=			self.get_scheduler_config()
																	snake_case_ :Any						=			scheduler_class(**snake_case						)

																	snake_case_ :List[str]						=			self.dummy_sample
																	snake_case_ :Dict						=			0.1 * sample

																	if num_inference_steps is not None and hasattr(snake_case						,     """set_timesteps"""						):
																							scheduler.set_timesteps(snake_case						)
																	elif num_inference_steps is not None and not hasattr(snake_case						,     """set_timesteps"""						):
																							snake_case_ :str						=			num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	snake_case_ :Union[str, Any]						=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
																	snake_case_ :Optional[int]						=			dummy_past_residuals[:]

																	snake_case_ :Optional[int]						=			scheduler.timesteps[5]
																	snake_case_ :str						=			scheduler.timesteps[6]

																	snake_case_ :int						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :List[str]						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

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

																	snake_case_ :str						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Optional[int]						=			scheduler.step(snake_case						,     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:							Tuple						)   ->	Dict:
											for timesteps in [100, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case						,     time_step=snake_case						)






					def    lowerCAmelCase_				(       self:							int						)   ->	Optional[int]:
											for t, num_inference_steps in zip([1, 5, 10]						,     [10, 50, 100]						):
																	self.check_over_forward(num_inference_steps=snake_case						,     time_step=snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Optional[Any]:
											snake_case_ :List[Any]						=			self.full_loop()
											snake_case_ :str						=			torch.mean(torch.abs(snake_case						)						)

											assert abs(result_mean.item() - 2_540_529						) < 10





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a      =				{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["ReformerTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["ReformerTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ReformerAttention",
						    "ReformerForMaskedLM",
						    "ReformerForQuestionAnswering",
						    "ReformerForSequenceClassification",
						    "ReformerLayer",
						    "ReformerModel",
						    "ReformerModelWithLMHead",
						    "ReformerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer import ReformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer_fast import ReformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_reformer import (
												    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ReformerAttention,
												    ReformerForMaskedLM,
												    ReformerForQuestionAnswering,
												    ReformerForSequenceClassification,
												    ReformerLayer,
												    ReformerModel,
												    ReformerModelWithLMHead,
												    ReformerPreTrainedModel,
												)

else:
						import sys

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