






'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class 		SCREAMING_SNAKE_CASE__   (     _SCREAMING_SNAKE_CASE    ):
  _UpperCAmelCase         =(VQDiffusionScheduler,)



  def _lowerCAmelCase     (    self:    Optional[Any]				,   **a:    Optional[Any])  ->Tuple:






        '''simple docstring'''


        a_				     =   {
            "num_vec_classes": 40_97,
            "num_train_timesteps": 1_00,
        }

        config.update(**__A)
        return config



  def _lowerCAmelCase     (    self:    int				,   a:    Optional[int])  ->Any:






        '''simple docstring'''


        a_				     =   4
        a_				     =   8
        a_				     =   8

        a_				     =   torch.randint(0				,   __A				,   (batch_size, height * width))

        return sample



  @property
  def _lowerCAmelCase     (    self:    str)  ->Optional[Any]:






        '''simple docstring'''


        assert False



  def _lowerCAmelCase     (    self:    int				,   a:    int)  ->Tuple:






        '''simple docstring'''


        def model(a:    List[Any]				,   a:    Optional[Any]				,   *a:    Optional[int]):
              a_     ,       a_				     =   sample.shape
              a_				     =   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))
              a_				     =   F.log_softmax(logits.double()				,   dim=1).float()
              return return_value

        return model



  def _lowerCAmelCase     (    self:    List[str])  ->Dict:






        '''simple docstring'''


        for timesteps in [2, 5, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=__A)



  def _lowerCAmelCase     (    self:    List[str])  ->List[str]:






        '''simple docstring'''


        for num_vec_classes in [5, 1_00, 10_00, 40_00]:
              self.check_over_configs(num_vec_classes=__A)



  def _lowerCAmelCase     (    self:    Tuple)  ->List[str]:






        '''simple docstring'''


        for t in [0, 50, 99]:
              self.check_over_forward(time_step=__A)




  def _lowerCAmelCase     (    self:    Union[str, Any])  ->str:






        '''simple docstring'''


        pass











'''simple docstring'''





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						if not inplace:
												a_				     =   ma + ma
						else:
												ma += ma

						return ma



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   -1 * len(lowercase__				)
						a_				     =   list(range(len(tensor.shape[:zero_index]				)				)				)
						return tensor.permute(first_inds + [zero_index + i for i in inds]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						return t.reshape(t.shape[:-no_dims] + (-1,)				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__ = 1e-4				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   mask.expand(*value.shape				)
						return torch.sum(mask * value	,dim=lowercase__				) / (eps + torch.sum(lowercase__	,dim=lowercase__				))



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = 2.3125	,lowercase__ = 21.6875	,lowercase__ = 64				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   torch.linspace(lowercase__	,lowercase__	,no_bins - 1	,device=pts.device				)
						a_				     =   torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2	,dim=-1				)				)
						return torch.bucketize(lowercase__	,lowercase__				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						a_				     =   dicts[0]
						a_				     =   {}
						for k, v in first.items():
												a_				     =   [d[k] for d in dicts]
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_multimap(lowercase__	,lowercase__				)
												else:
																		a_				     =   fn(lowercase__				)

						return new_dict



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   v_bins.view(((1,) * len(x.shape				)) + (len(lowercase__				),)				)
						a_				     =   x[..., None] - reshaped_bins
						a_				     =   torch.argmin(torch.abs(lowercase__				)	,dim=-1				)
						return nn.functional.one_hot(lowercase__	,num_classes=len(lowercase__				)				).float()



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__ = 0	,lowercase__ = 0				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   []
						for i, s in enumerate(data.shape[:no_batch_dims]				):
												a_				     =   torch.arange(lowercase__				)
												a_				     =   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
												ranges.append(lowercase__				)

						a_				     =   [slice(lowercase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
						a_				     =   inds
						ranges.extend(lowercase__				)
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(lowercase__				)]


a_      = TypeVar('T')



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Dict[Any, Union[dict, list, tuple, Any]]:

						'''simple docstring'''


						a_				     =   {}
						for k, v in dic.items():
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_map(lowercase__	,lowercase__	,lowercase__				)
												else:
																		a_				     =   tree_map(lowercase__	,lowercase__	,lowercase__				)

						return new_dict



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Any:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				list:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				tuple:

						'''simple docstring'''


						...



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				List[Any]:

						'''simple docstring'''


						if isinstance(lowercase__	,lowercase__				):
												return dict_map(lowercase__	,lowercase__	,lowercase__				)
						elif isinstance(lowercase__	,lowercase__				):
												return [tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree]
						elif isinstance(lowercase__	,lowercase__				):
												return tuple(tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree				)
						elif isinstance(lowercase__	,lowercase__				):
												return fn(lowercase__				)
						else:
												print(type(lowercase__				)				)
												raise ValueError("Not supported"				)
a_      = partial(tree_map, leaf_type=torch.Tensor)



