
'''simple docstring'''

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	__lowerCAmelCase    (  unittest.TestCase				):
     # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
     snake_case   :    Tuple							      =      (ScoreSdeVeScheduler,)
     snake_case   :    Dict							      =      ()



     @property
     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    4
          _UpperCAmelCase					:  Any         =    3
          _UpperCAmelCase					:  str         =    8
          _UpperCAmelCase					:  Union[str, Any]         =    8

          _UpperCAmelCase					:  Union[str, Any]         =    torch.rand((batch_size, num_channels, height, width)	)

          return sample



     @property
     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    4
          _UpperCAmelCase					:  Any         =    3
          _UpperCAmelCase					:  int         =    8
          _UpperCAmelCase					:  Union[str, Any]         =    8

          _UpperCAmelCase					:  Union[str, Any]         =    batch_size * num_channels * height * width
          _UpperCAmelCase					:  Optional[int]         =    torch.arange(lowerCAmelCase__	)
          _UpperCAmelCase					:  Optional[int]         =    sample.reshape(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Optional[Any]         =    sample / num_elems
          _UpperCAmelCase					:  str         =    sample.permute(3						,	0						,	1						,	2	)

          return sample



     def     snake_case_ (self	):
          def model(lowerCAmelCase__						,	lowerCAmelCase__						,	*lowerCAmelCase__	):
               return sample * t / (t + 1)

          return model



     def     snake_case_ (self						,	**lowerCAmelCase__	):
          _UpperCAmelCase					:  Dict         =    {
              """num_train_timesteps""": 2_0_0_0,
              """snr""": 0.1_5,
              """sigma_min""": 0.0_1,
              """sigma_max""": 1_3_4_8,
              """sampling_eps""": 1e-5,
          }

          config.update(**lowerCAmelCase__	)
          return config



     def     snake_case_ (self						,	lowerCAmelCase__=0						,	**lowerCAmelCase__	):
          _UpperCAmelCase					:  int         =    dict(self.forward_default_kwargs	)

          for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase					:  int         =    self.dummy_sample
               _UpperCAmelCase					:  Any         =    0.1 * sample

               _UpperCAmelCase					:  Tuple         =    self.get_scheduler_config(**lowerCAmelCase__	)
               _UpperCAmelCase					:  Union[str, Any]         =    scheduler_class(**lowerCAmelCase__	)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase__	)
                    _UpperCAmelCase					:  Tuple         =    scheduler_class.from_pretrained(lowerCAmelCase__	)

               _UpperCAmelCase					:  List[Any]         =    scheduler.step_pred(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample
               _UpperCAmelCase					:  Optional[Any]         =    new_scheduler.step_pred(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample

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

               _UpperCAmelCase					:  List[Any]         =    scheduler.step_correct(lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample
               _UpperCAmelCase					:  Any         =    new_scheduler.step_correct(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample

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



     def     snake_case_ (self						,	lowerCAmelCase__=0						,	**lowerCAmelCase__	):
          _UpperCAmelCase					:  Optional[Any]         =    dict(self.forward_default_kwargs	)
          kwargs.update(lowerCAmelCase__	)

          for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase					:  List[str]         =    self.dummy_sample
               _UpperCAmelCase					:  Tuple         =    0.1 * sample

               _UpperCAmelCase					:  Optional[int]         =    self.get_scheduler_config()
               _UpperCAmelCase					:  List[Any]         =    scheduler_class(**lowerCAmelCase__	)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase__	)
                    _UpperCAmelCase					:  Any         =    scheduler_class.from_pretrained(lowerCAmelCase__	)

               _UpperCAmelCase					:  Optional[int]         =    scheduler.step_pred(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample
               _UpperCAmelCase					:  str         =    new_scheduler.step_pred(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample

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

               _UpperCAmelCase					:  Union[str, Any]         =    scheduler.step_correct(lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample
               _UpperCAmelCase					:  Optional[int]         =    new_scheduler.step_correct(
                   lowerCAmelCase__						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample

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



     def     snake_case_ (self	):
          for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]						,	[1, 1_0_0, 1_0_0_0]	):
               self.check_over_configs(sigma_min=lowerCAmelCase__						,	sigma_max=lowerCAmelCase__	)



     def     snake_case_ (self	):
          for t in [0.1, 0.5, 0.7_5]:
               self.check_over_forward(time_step=lowerCAmelCase__	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Any         =    dict(self.forward_default_kwargs	)

          _UpperCAmelCase					:  str         =    self.scheduler_classes[0]
          _UpperCAmelCase					:  str         =    self.get_scheduler_config()
          _UpperCAmelCase					:  int         =    scheduler_class(**lowerCAmelCase__	)

          _UpperCAmelCase					:  List[str]         =    3

          _UpperCAmelCase					:  List[Any]         =    self.dummy_model()
          _UpperCAmelCase					:  int         =    self.dummy_sample_deter

          scheduler.set_sigmas(lowerCAmelCase__	)
          scheduler.set_timesteps(lowerCAmelCase__	)
          _UpperCAmelCase					:  List[str]         =    torch.manual_seed(0	)

          for i, t in enumerate(scheduler.timesteps	):
               _UpperCAmelCase					:  Any         =    scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps	):
                    with torch.no_grad():
                         _UpperCAmelCase					:  Optional[Any]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)
                    _UpperCAmelCase					:  Tuple         =    scheduler.step_correct(lowerCAmelCase__						,	lowerCAmelCase__						,	generator=lowerCAmelCase__						,	**lowerCAmelCase__	).prev_sample

               with torch.no_grad():
                    _UpperCAmelCase					:  List[str]         =    model(lowerCAmelCase__						,	lowerCAmelCase__	)

               _UpperCAmelCase					:  Any         =    scheduler.step_pred(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	generator=lowerCAmelCase__						,	**lowerCAmelCase__	)
               _UpperCAmelCase  ,     _UpperCAmelCase					:  Tuple         =    output.prev_sample, output.prev_sample_mean

          _UpperCAmelCase					:  List[Any]         =    torch.sum(torch.abs(lowerCAmelCase__	)	)
          _UpperCAmelCase					:  Tuple         =    torch.mean(torch.abs(lowerCAmelCase__	)	)

          assert np.isclose(result_sum.item()						,	1_4_3_7_2_7_5_8_5_2_8.0	)
          assert np.isclose(result_mean.item()						,	1_8_7_1_4_5_3_0.0	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[Any]         =    dict(self.forward_default_kwargs	)

          _UpperCAmelCase					:  Tuple         =    kwargs.pop("""num_inference_steps"""						,	lowerCAmelCase__	)

          for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase					:  List[Any]         =    self.get_scheduler_config()
               _UpperCAmelCase					:  Tuple         =    scheduler_class(**lowerCAmelCase__	)

               _UpperCAmelCase					:  Any         =    self.dummy_sample
               _UpperCAmelCase					:  Dict         =    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

               _UpperCAmelCase					:  Union[str, Any]         =    scheduler.step_pred(lowerCAmelCase__						,	0						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample
               _UpperCAmelCase					:  Union[str, Any]         =    scheduler.step_pred(lowerCAmelCase__						,	1						,	lowerCAmelCase__						,	generator=torch.manual_seed(0	)						,	**lowerCAmelCase__	).prev_sample

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





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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






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

    return ma




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1E-4       )->		torch.Tensor:



    """simple docstring"""






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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 2.3125       ,		SCREAMING_SNAKE_CASE_ = 21.6875       ,		SCREAMING_SNAKE_CASE_ = 6_4       )->		torch.Tensor:



    """simple docstring"""






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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






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

    return new_dict




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 0       ,		SCREAMING_SNAKE_CASE_ = 0       )->		torch.Tensor:



    """simple docstring"""






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

    UpperCamelCase_          =		[slice(SCREAMING_SNAKE_CASE_       ) for _ in range(len(data.shape       ) - no_batch_dims       )]
    UpperCamelCase_          =		inds
    ranges.extend(SCREAMING_SNAKE_CASE_       )
    # 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(SCREAMING_SNAKE_CASE_       )]


SCREAMING_SNAKE_CASE						:Dict      =			TypeVar("""T""")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Dict[Any, Union[dict, list, tuple, Any]]:



    """simple docstring"""






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

    return new_dict




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple:



    """simple docstring"""






    ...




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






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