




'''simple docstring'''







import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class      _a    (      unittest.TestCase					):
       # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
       __a							:	str  							=   (ScoreSdeVeScheduler,)
       __a							:	Any  							=   ()
       @property
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						4
           UpperCAmelCase     			=						3
           UpperCAmelCase     			=						8
           UpperCAmelCase     			=						8

           UpperCAmelCase     			=						torch.rand((batch_size, num_channels, height, width)							)

           return sample
       @property
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						4
           UpperCAmelCase     			=						3
           UpperCAmelCase     			=						8
           UpperCAmelCase     			=						8

           UpperCAmelCase     			=						batch_size * num_channels * height * width
           UpperCAmelCase     			=						torch.arange(lowercase							)
           UpperCAmelCase     			=						sample.reshape(lowercase  ,							lowercase  ,							lowercase  ,							lowercase							)
           UpperCAmelCase     			=						sample / num_elems
           UpperCAmelCase     			=						sample.permute(3  ,							0  ,							1  ,							2							)

           return sample
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           def model(lowercase		:      Dict  ,							lowercase		:      Union[str, Any]  ,							*lowercase		:      List[str]							):
               return sample * t / (t + 1)

           return model
       def        A (      self		:      List[str]  ,							**lowercase		:      Optional[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						{
               '''num_train_timesteps''': 2_000,
               '''snr''': 0.15,
               '''sigma_min''': 0.01,
               '''sigma_max''': 1_348,
               '''sampling_eps''': 1E-5,
           }

           config.update(**lowercase							)
           return config
       def        A (      self		:      List[str]  ,							lowercase		:      List[Any]=0  ,							**lowercase		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						dict(self.forward_default_kwargs							)

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase     			=						self.dummy_sample
               UpperCAmelCase     			=						0.1 * sample

               UpperCAmelCase     			=						self.get_scheduler_config(**lowercase							)
               UpperCAmelCase     			=						scheduler_class(**lowercase							)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase							)
                   UpperCAmelCase     			=						scheduler_class.from_pretrained(lowercase							)

               UpperCAmelCase     			=						scheduler.step_pred(
                   lowercase  ,							lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample
               UpperCAmelCase     			=						new_scheduler.step_pred(
                   lowercase  ,							lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample

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

               UpperCAmelCase     			=						scheduler.step_correct(lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample
               UpperCAmelCase     			=						new_scheduler.step_correct(
                   lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler correction are not identical"
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      Union[str, Any]=0  ,							**lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						dict(self.forward_default_kwargs							)
           kwargs.update(lowercase							)

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase     			=						self.dummy_sample
               UpperCAmelCase     			=						0.1 * sample

               UpperCAmelCase     			=						self.get_scheduler_config()
               UpperCAmelCase     			=						scheduler_class(**lowercase							)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase							)
                   UpperCAmelCase     			=						scheduler_class.from_pretrained(lowercase							)

               UpperCAmelCase     			=						scheduler.step_pred(
                   lowercase  ,							lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample
               UpperCAmelCase     			=						new_scheduler.step_pred(
                   lowercase  ,							lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample

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

               UpperCAmelCase     			=						scheduler.step_correct(lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample
               UpperCAmelCase     			=						new_scheduler.step_correct(
                   lowercase  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler correction are not identical"
       def        A (      self		:      Any							):



           '''simple docstring'''

           for timesteps in [10, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase							)
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]  ,							[1, 100, 1_000]							):
               self.check_over_configs(sigma_min=lowercase  ,							sigma_max=lowercase							)
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=lowercase							)
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						dict(self.forward_default_kwargs							)

           UpperCAmelCase     			=						self.scheduler_classes[0]
           UpperCAmelCase     			=						self.get_scheduler_config()
           UpperCAmelCase     			=						scheduler_class(**lowercase							)

           UpperCAmelCase     			=						3

           UpperCAmelCase     			=						self.dummy_model()
           UpperCAmelCase     			=						self.dummy_sample_deter

           scheduler.set_sigmas(lowercase							)
           scheduler.set_timesteps(lowercase							)
           UpperCAmelCase     			=						torch.manual_seed(0							)

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

               for _ in range(scheduler.config.correct_steps							):
                   with torch.no_grad():
                       UpperCAmelCase     			=						model(lowercase  ,							lowercase							)
                   UpperCAmelCase     			=						scheduler.step_correct(lowercase  ,							lowercase  ,							generator=lowercase  ,							**lowercase							).prev_sample

               with torch.no_grad():
                   UpperCAmelCase     			=						model(lowercase  ,							lowercase							)

               UpperCAmelCase     			=						scheduler.step_pred(lowercase  ,							lowercase  ,							lowercase  ,							generator=lowercase  ,							**lowercase							)
               UpperCAmelCase			,	UpperCAmelCase     			=						output.prev_sample, output.prev_sample_mean

           UpperCAmelCase     			=						torch.sum(torch.abs(lowercase							)							)
           UpperCAmelCase     			=						torch.mean(torch.abs(lowercase							)							)

           assert np.isclose(result_sum.item()  ,							143_7275_8528.0							)
           assert np.isclose(result_mean.item()  ,							1871_4530.0							)







       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						dict(self.forward_default_kwargs							)

           UpperCAmelCase     			=						kwargs.pop('''num_inference_steps'''  ,							lowercase							)

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase     			=						self.get_scheduler_config()
               UpperCAmelCase     			=						scheduler_class(**lowercase							)

               UpperCAmelCase     			=						self.dummy_sample
               UpperCAmelCase     			=						0.1 * sample

               if num_inference_steps is not None and hasattr(lowercase  ,							'''set_timesteps'''							):
                   scheduler.set_timesteps(lowercase							)
               elif num_inference_steps is not None and not hasattr(lowercase  ,							'''set_timesteps'''							):
                   UpperCAmelCase     			=						num_inference_steps

               UpperCAmelCase     			=						scheduler.step_pred(lowercase  ,							0  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample
               UpperCAmelCase     			=						scheduler.step_pred(lowercase  ,							1  ,							lowercase  ,							generator=torch.manual_seed(0							)  ,							**lowercase							).prev_sample

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










'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A					       =logging.get_logger(__name__)

A					       ={
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class      _a    (      __a					):
       __a							:	Optional[int]  							=   """regnet"""
       __a							:	Optional[Any]  							=   ["""x""", """y"""]
       def __init__(      self		:      int  ,							lowercase		:      List[Any]=3  ,							lowercase		:      Union[str, Any]=32  ,							lowercase		:      Tuple=[128, 192, 512, 1_088]  ,							lowercase		:      List[Any]=[2, 6, 12, 2]  ,							lowercase		:      Optional[Any]=64  ,							lowercase		:      Optional[int]="y"  ,							lowercase		:      int="relu"  ,							**lowercase		:      int  ,							):



           '''simple docstring'''

           super().__init__(**lowercase							)
           if layer_type not in self.layer_types:
               raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types							)}"							)
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						embedding_size
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						groups_width
           UpperCAmelCase     			=						layer_type
           UpperCAmelCase     			=						hidden_act
           # always downsample in the first stage
           UpperCAmelCase     			=						True




