


'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Optional[Any]   =					(IPNDMScheduler,)
					lowerCAmelCase      :int   =					(('''num_inference_steps''', 50),)




					def 				snake_case__						(      self							,							**_lowerCamelCase):
								UpperCAmelCase__  :					int        =     {"""num_train_timesteps""": 1000}
								config.update(**_lowerCamelCase)
								return config




					def 				snake_case__						(      self							,							_lowerCamelCase=0							,							**_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     dict(self.forward_default_kwargs)
								UpperCAmelCase__  :					Any        =     kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     self.dummy_sample
								UpperCAmelCase__  :					Any        =     0.1 * sample
								UpperCAmelCase__  :					Tuple        =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
											UpperCAmelCase__  :					Tuple        =     self.get_scheduler_config(**_lowerCamelCase)
											UpperCAmelCase__  :					List[str]        =     scheduler_class(**_lowerCamelCase)
											scheduler.set_timesteps(_lowerCamelCase)
											# copy over dummy past residuals
											UpperCAmelCase__  :					Optional[Any]        =     dummy_past_residuals[:]

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

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(_lowerCamelCase)
														UpperCAmelCase__  :					Optional[int]        =     scheduler_class.from_pretrained(_lowerCamelCase)
														new_scheduler.set_timesteps(_lowerCamelCase)
														# copy over dummy past residuals
														UpperCAmelCase__  :					Union[str, Any]        =     dummy_past_residuals[:]

											UpperCAmelCase__  :					List[str]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					Any        =     new_scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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

											UpperCAmelCase__  :					Optional[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					int        =     new_scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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




					def 				snake_case__						(      self):
								pass




					def 				snake_case__						(      self							,							_lowerCamelCase=0							,							**_lowerCamelCase):
								UpperCAmelCase__  :					str        =     dict(self.forward_default_kwargs)
								UpperCAmelCase__  :					Union[str, Any]        =     kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     self.dummy_sample
								UpperCAmelCase__  :					Tuple        =     0.1 * sample
								UpperCAmelCase__  :					List[Any]        =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
											UpperCAmelCase__  :					Union[str, Any]        =     self.get_scheduler_config()
											UpperCAmelCase__  :					str        =     scheduler_class(**_lowerCamelCase)
											scheduler.set_timesteps(_lowerCamelCase)

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

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

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(_lowerCamelCase)
														UpperCAmelCase__  :					Optional[int]        =     scheduler_class.from_pretrained(_lowerCamelCase)
														# copy over dummy past residuals
														new_scheduler.set_timesteps(_lowerCamelCase)

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

											UpperCAmelCase__  :					Optional[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					Any        =     new_scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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

											UpperCAmelCase__  :					List[str]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					str        =     new_scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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




					def 				snake_case__						(      self							,							**_lowerCamelCase):
								UpperCAmelCase__  :					str        =     self.scheduler_classes[0]
								UpperCAmelCase__  :					Dict        =     self.get_scheduler_config(**_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__  :					Tuple        =     10
								UpperCAmelCase__  :					List[str]        =     self.dummy_model()
								UpperCAmelCase__  :					Union[str, Any]        =     self.dummy_sample_deter
								scheduler.set_timesteps(_lowerCamelCase)

								for i, t in enumerate(scheduler.timesteps):
											UpperCAmelCase__  :					Union[str, Any]        =     model(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					Any        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample

								for i, t in enumerate(scheduler.timesteps):
											UpperCAmelCase__  :					Tuple        =     model(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					Optional[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample

								return sample




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     dict(self.forward_default_kwargs)

								UpperCAmelCase__  :					List[Any]        =     kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)

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

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

											if num_inference_steps is not None and hasattr(_lowerCamelCase							,							"""set_timesteps"""):
														scheduler.set_timesteps(_lowerCamelCase)
											elif num_inference_steps is not None and not hasattr(_lowerCamelCase							,							"""set_timesteps"""):
														UpperCAmelCase__  :					List[str]        =     num_inference_steps

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

											UpperCAmelCase__  :					List[Any]        =     scheduler.timesteps[5]
											UpperCAmelCase__  :					str        =     scheduler.timesteps[6]

											UpperCAmelCase__  :					Optional[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					Union[str, Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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

											UpperCAmelCase__  :					List[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					List[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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




					def 				snake_case__						(      self):
								for timesteps in [100, 1000]:
											self.check_over_configs(num_train_timesteps=_lowerCamelCase							,							time_step=_lowerCamelCase)




					def 				snake_case__						(      self):
								for t, num_inference_steps in zip([1, 5, 10]							,							[10, 50, 100]):
											self.check_over_forward(num_inference_steps=_lowerCamelCase							,							time_step=_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     self.full_loop()
								UpperCAmelCase__  :					List[Any]        =     torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_mean.item() - 254_0529) < 10



'''simple docstring'''



from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Dict        =     1.0 if scale is None else scale
								UpperCAmelCase__  :					Dict        =     0.0 if loc is None else loc

								super().__init__(_lowerCamelCase							,							[AffineTransform(loc=self.loc							,							scale=self.scale							,							event_dim=_lowerCamelCase)])




					@property
					def 				snake_case__						(      self):
								return self.base_dist.mean * self.scale + self.loc




					@property
					def 				snake_case__						(      self):
								return self.base_dist.variance * self.scale**2




					@property
					def 				snake_case__						(      self):
								return self.variance.sqrt()


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					int        =     args_dim
								UpperCAmelCase__  :					Optional[int]        =     nn.ModuleList([nn.Linear(_lowerCamelCase							,							_lowerCamelCase) for dim in args_dim.values()])
								UpperCAmelCase__  :					Optional[Any]        =     domain_map




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     [proj(_lowerCamelCase) for proj in self.proj]

								return self.domain_map(*_lowerCamelCase)


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					Optional[int]        =     function




					def 				snake_case__						(      self							,							_lowerCamelCase							,							*_lowerCamelCase):
								return self.function(_lowerCamelCase							,							*_lowerCamelCase)


class 		_snake_case					:
					lowerCAmelCase      :type
					lowerCAmelCase      :int
					lowerCAmelCase      :Dict[str, int]




					def __init__(      self							,							_lowerCamelCase = 1):
								UpperCAmelCase__  :					Optional[Any]        =     dim
								UpperCAmelCase__  :					int        =     {k: dim * self.args_dim[k] for k in self.args_dim}




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.dim == 1:
											return self.distribution_class(*_lowerCamelCase)
								else:
											return Independent(self.distribution_class(*_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					Dict        =     self._base_distribution(_lowerCamelCase)
								if loc is None and scale is None:
											return distr
								else:
											return AffineTransformed(_lowerCamelCase							,							loc=_lowerCamelCase							,							scale=_lowerCamelCase							,							event_dim=self.event_dim)




					@property
					def 				snake_case__						(      self):
								return () if self.dim == 1 else (self.dim,)




					@property
					def 				snake_case__						(      self):
								return len(self.event_shape)




					@property
					def 				snake_case__						(      self):
								return 0.0




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return ParameterProjection(
								    in_features=_lowerCamelCase							,							args_dim=self.args_dim							,							domain_map=LambdaLayer(self.domain_map)							,							)




					def 				snake_case__						(      self							,							*_lowerCamelCase):
								raise NotImplementedError()




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return (x + torch.sqrt(torch.square(_lowerCamelCase) + 4.0)) / 2.0


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"df": 1, "loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					StudentT




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								UpperCAmelCase__  :					Any        =     2.0 + cls.squareplus(_lowerCamelCase)
								return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					Normal




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								return loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"total_count": 1, "logits": 1}
					lowerCAmelCase      :type   =					NegativeBinomial




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase)
								return total_count.squeeze(-1), logits.squeeze(-1)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     distr_args
								if self.dim == 1:
											return self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)
								else:
											return Independent(self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     distr_args

								if scale is not None:
											# See scaling property of Gamma.
											logits += scale.log()

								return self._base_distribution((total_count, logits))