'''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 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Distribution      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0							)  ->    Optional[Any]:
  __lowerCAmelCase											=						1.0 if scale is None else scale
  __lowerCAmelCase											=						0.0 if loc is None else loc

  super().__init__(SCREAMING_SNAKE_CASE__      ,       [AffineTransform(loc=self.loc      ,       scale=self.scale      ,       event_dim=SCREAMING_SNAKE_CASE__							)]							)
 @property
 def a					(      self   :	Any							)  ->    Union[str, Any]:
  return self.base_dist.mean * self.scale + self.loc
 @property
 def a					(      self   :	List[Any]							)  ->    Optional[Any]:
  return self.base_dist.variance * self.scale**2





 @property
 def a					(      self   :	List[str]							)  ->    Optional[int]:
  return self.variance.sqrt()







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int]      ,       SCREAMING_SNAKE_CASE__   :	Callable[..., Tuple[torch.Tensor]]      ,       **SCREAMING_SNAKE_CASE__   :	Dict							)  ->    None:
  super().__init__(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						args_dim
  __lowerCAmelCase											=						nn.ModuleList([nn.Linear(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) for dim in args_dim.values()]							)
  __lowerCAmelCase											=						domain_map





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor							)  ->    Tuple[torch.Tensor]:
  __lowerCAmelCase											=						[proj(SCREAMING_SNAKE_CASE__							) for proj in self.proj]

  return self.domain_map(*SCREAMING_SNAKE_CASE__							)







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    str:
  super().__init__()
  __lowerCAmelCase											=						function





 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       *SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    int:
  return self.function(SCREAMING_SNAKE_CASE__      ,       *SCREAMING_SNAKE_CASE__							)







class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       type
 _SCREAMING_SNAKE_CASE     :       int
 _SCREAMING_SNAKE_CASE     :       Dict[str, int]
 def __init__(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	int = 1							)  ->    None:
  __lowerCAmelCase											=						dim
  __lowerCAmelCase											=						{k: dim * self.args_dim[k] for k in self.args_dim}
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    Union[str, Any]:
  if self.dim == 1:
   return self.distribution_class(*SCREAMING_SNAKE_CASE__							)
  else:
   return Independent(self.distribution_class(*SCREAMING_SNAKE_CASE__							)      ,       1							)
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.Tensor] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.Tensor] = None      ,       )  ->    Distribution:
  __lowerCAmelCase											=						self._base_distribution(SCREAMING_SNAKE_CASE__							)
  if loc is None and scale is None:
   return distr
  else:
   return AffineTransformed(SCREAMING_SNAKE_CASE__      ,       loc=SCREAMING_SNAKE_CASE__      ,       scale=SCREAMING_SNAKE_CASE__      ,       event_dim=self.event_dim							)
 @property
 def a					(      self   :	Tuple							)  ->    Tuple:
  return () if self.dim == 1 else (self.dim,)
 @property
 def a					(      self   :	Optional[Any]							)  ->    int:
  return len(self.event_shape							)
 @property
 def a					(      self   :	Union[str, Any]							)  ->    float:
  return 0.0
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    nn.Module:
  return ParameterProjection(
      in_features=SCREAMING_SNAKE_CASE__      ,       args_dim=self.args_dim      ,       domain_map=LambdaLayer(self.domain_map							)      ,       )
 def a					(      self   :	Union[str, Any]      ,       *SCREAMING_SNAKE_CASE__   :	torch.Tensor							)  ->    Any:
  raise NotImplementedError()





 @staticmethod
 def a					(      SCREAMING_SNAKE_CASE__   :	torch.Tensor							)  ->    torch.Tensor:
  return (x + torch.sqrt(torch.square(SCREAMING_SNAKE_CASE__							) + 4.0							)) / 2.0







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Dict[str, int]      	=					{"df": 1, "loc": 1, "scale": 1}
 _SCREAMING_SNAKE_CASE     :       type      	=					StudentT
 @classmethod
 def a					(      cls   :	Any      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor							)  ->    Optional[Any]:
  __lowerCAmelCase											=						cls.squareplus(SCREAMING_SNAKE_CASE__							).clamp_min(torch.finfo(scale.dtype							).eps							)
  __lowerCAmelCase											=						2.0 + cls.squareplus(SCREAMING_SNAKE_CASE__							)
  return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Dict[str, int]      	=					{"loc": 1, "scale": 1}
 _SCREAMING_SNAKE_CASE     :       type      	=					Normal
 @classmethod
 def a					(      cls   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor							)  ->    int:
  __lowerCAmelCase											=						cls.squareplus(SCREAMING_SNAKE_CASE__							).clamp_min(torch.finfo(scale.dtype							).eps							)
  return loc.squeeze(-1							), scale.squeeze(-1							)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Dict[str, int]      	=					{"total_count": 1, "logits": 1}
 _SCREAMING_SNAKE_CASE     :       type      	=					NegativeBinomial
 @classmethod
 def a					(      cls   :	str      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor      ,       SCREAMING_SNAKE_CASE__   :	torch.Tensor							)  ->    str:
  __lowerCAmelCase											=						cls.squareplus(SCREAMING_SNAKE_CASE__							)
  return total_count.squeeze(-1							), logits.squeeze(-1							)
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    Distribution:
  __lowerCAmelCase							,       __lowerCAmelCase											=						distr_args
  if self.dim == 1:
   return self.distribution_class(total_count=SCREAMING_SNAKE_CASE__      ,       logits=SCREAMING_SNAKE_CASE__							)
  else:
   return Independent(self.distribution_class(total_count=SCREAMING_SNAKE_CASE__      ,       logits=SCREAMING_SNAKE_CASE__							)      ,       1							)





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.Tensor] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.Tensor] = None							)  ->    Distribution:
  __lowerCAmelCase							,       __lowerCAmelCase											=						distr_args

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

  return self._base_distribution((total_count, logits)							)







'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	Any							)  ->    int:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						UNetaDModel(
      block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,       )
  return model





 def a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase											=						self.dummy_uncond_unet
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""      ,       return_dict=SCREAMING_SNAKE_CASE__							)[0]

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]
  __lowerCAmelCase											=						image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
  assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2







@slow
@require_torch
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						"""google/ddpm-cifar10-32"""

  __lowerCAmelCase											=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






