

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   (IPNDMScheduler,)
					__magic_name__        =   (("num_inference_steps", 5_0),)





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						UpperCAmelCase__:	Any									= {"num_train_timesteps": 1_0_0_0}
						config.update(**lowerCamelCase__						)
						return config





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=0				,      **lowerCamelCase__						):
						UpperCAmelCase__:	int									= dict(self.forward_default_kwargs						)
						UpperCAmelCase__:	Union[str, Any]									= kwargs.pop("num_inference_steps"				,      lowerCamelCase__						)
						UpperCAmelCase__:	int									= self.dummy_sample
						UpperCAmelCase__:	Dict									= 0.1 * sample
						UpperCAmelCase__:	Optional[Any]									= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

							UpperCAmelCase__:	Tuple									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						).prev_sample
							UpperCAmelCase__:	Optional[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__:	str									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						).prev_sample
							UpperCAmelCase__:	Dict									= 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    _UpperCAmelCase    (				self						):
						pass





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=0				,      **lowerCamelCase__						):
						UpperCAmelCase__:	str									= dict(self.forward_default_kwargs						)
						UpperCAmelCase__:	str									= kwargs.pop("num_inference_steps"				,      lowerCamelCase__						)
						UpperCAmelCase__:	Any									= self.dummy_sample
						UpperCAmelCase__:	Union[str, Any]									= 0.1 * sample
						UpperCAmelCase__:	List[str]									= [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__:	List[str]									= scheduler_class(**lowerCamelCase__						)
							scheduler.set_timesteps(lowerCamelCase__						)

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

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

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(lowerCamelCase__						)
								UpperCAmelCase__:	Any									= 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__:	Dict									= dummy_past_residuals[:]

							UpperCAmelCase__:	Dict									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						).prev_sample
							UpperCAmelCase__:	Union[str, 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[Any]									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						).prev_sample
							UpperCAmelCase__:	Optional[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    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						UpperCAmelCase__:	int									= self.scheduler_classes[0]
						UpperCAmelCase__:	Dict									= self.get_scheduler_config(**lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= scheduler_class(**lowerCamelCase__						)

						UpperCAmelCase__:	Union[str, Any]									= 1_0
						UpperCAmelCase__:	Any									= self.dummy_model()
						UpperCAmelCase__:	Any									= self.dummy_sample_deter
						scheduler.set_timesteps(lowerCamelCase__						)

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

						for i, t in enumerate(scheduler.timesteps						):
							UpperCAmelCase__:	str									= model(lowerCamelCase__				,      lowerCamelCase__						)
							UpperCAmelCase__:	str									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						).prev_sample

						return sample





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= dict(self.forward_default_kwargs						)

						UpperCAmelCase__:	Any									= kwargs.pop("num_inference_steps"				,      lowerCamelCase__						)

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

							UpperCAmelCase__:	List[str]									= self.dummy_sample
							UpperCAmelCase__:	Optional[int]									= 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__:	Union[str, Any]									= num_inference_steps

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

							UpperCAmelCase__:	Union[str, Any]									= scheduler.timesteps[5]
							UpperCAmelCase__:	Optional[Any]									= scheduler.timesteps[6]

							UpperCAmelCase__:	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						)

							UpperCAmelCase__:	Tuple									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						).prev_sample
							UpperCAmelCase__:	List[str]									= scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						).prev_sample

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





					def    _UpperCAmelCase    (				self						):
						for timesteps in [1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCamelCase__				,      time_step=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						for t, num_inference_steps in zip([1, 5, 1_0]				,      [1_0, 5_0, 1_0_0]						):
							self.check_over_forward(num_inference_steps=lowerCamelCase__				,      time_step=lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= self.full_loop()
						UpperCAmelCase__:	int									= torch.mean(torch.abs(lowerCamelCase__						)						)

						assert abs(result_mean.item() - 2_5_4_0_5_2_9						) < 1_0


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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=0						):
						UpperCAmelCase__:	Any									= 1.0 if scale is None else scale
						UpperCAmelCase__:	int									= 0.0 if loc is None else loc

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





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





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





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

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= [proj(lowerCamelCase__						) for proj in self.proj]

						return self.domain_map(*lowerCamelCase__						)

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						super().__init__()
						UpperCAmelCase__:	str									= function





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      *lowerCamelCase__						):
						return self.function(lowerCamelCase__				,      *lowerCamelCase__						)

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   42
					__magic_name__        =   42
					__magic_name__        =   42





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





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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      ):
						UpperCAmelCase__:	Optional[Any]									= 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    _UpperCAmelCase    (				self						):
						return () if self.dim == 1 else (self.dim,)





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





					@property
					def    _UpperCAmelCase    (				self						):
						return 0.0





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						return ParameterProjection(
						    in_features=lowerCamelCase__				,      args_dim=self.args_dim				,      domain_map=LambdaLayer(self.domain_map						)				,      )





					def    _UpperCAmelCase    (				self				,      *lowerCamelCase__						):
						raise NotImplementedError()





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

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"df": 1, "loc": 1, "scale": 1}
					__magic_name__        =   StudentT





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

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"loc": 1, "scale": 1}
					__magic_name__        =   Normal





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

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"total_count": 1, "logits": 1}
					__magic_name__        =   NegativeBinomial





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= cls.squareplus(lowerCamelCase__						)
						return total_count.squeeze(-1						), logits.squeeze(-1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__:	str									= 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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None						):
						UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= distr_args

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

						return self._base_distribution((total_count, logits)						)