
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  str	   =      (EulerDiscreteScheduler,)
 __lowerCAmelCase	:  Any	   =      10


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   {
       """num_train_timesteps""": 11_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [10, 50, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps)

   lowercase__	: Optional[Any]     		=   torch.manual_seed(0)

   lowercase__	: List[Any]     		=   self.dummy_model()
   lowercase__	: Optional[int]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma
   lowercase__	: int     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for i, t in enumerate(scheduler.timesteps):
     lowercase__	: Tuple     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: List[Any]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: str     		=   output.prev_sample

   lowercase__	: str     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: str     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
   assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps)

   lowercase__	: Dict     		=   torch.manual_seed(0)

   lowercase__	: Union[str, Any]     		=   self.dummy_model()
   lowercase__	: List[str]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma
   lowercase__	: Optional[int]     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for i, t in enumerate(scheduler.timesteps):
     lowercase__	: Tuple     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Union[str, Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Optional[int]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: Any     		=   output.prev_sample

   lowercase__	: List[str]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Tuple     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
   assert abs(result_mean.item() - 2.26_76E-06) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.scheduler_classes[0]
   lowercase__	: Any     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps       ,	device=SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   torch.manual_seed(0)

   lowercase__	: Dict     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowercase__	: List[str]     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for t in scheduler.timesteps:
     lowercase__	: List[Any]     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Any     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   output.prev_sample

   lowercase__	: int     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: List[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
   assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: Any     		=   self.get_scheduler_config()
   lowercase__	: Optional[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_       ,	use_karras_sigmas=SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps       ,	device=SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   torch.manual_seed(0)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: Dict     		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowercase__	: Any     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for t in scheduler.timesteps:
     lowercase__	: Union[str, Any]     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: List[str]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: Optional[Any]     		=   output.prev_sample

   lowercase__	: Any     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: List[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
   assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3









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)						)