





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCAmelCase         =   logging.get_logger(__name__)

__UpperCAmelCase         =   {
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}




class       UpperCamelCase__			(			lowercase_		,			lowercase_	):




     """simple docstring"""
     SCREAMING_SNAKE_CASE__						=					'''convnext'''


     def __init__(		self							:	Optional[Any]     ,		lowerCamelCase_							:	Dict=3     ,		lowerCamelCase_							:	Tuple=4     ,		lowerCamelCase_							:	int=4     ,		lowerCamelCase_							:	Optional[int]=None     ,		lowerCamelCase_							:	Optional[Any]=None     ,		lowerCamelCase_							:	Tuple="gelu"     ,		lowerCamelCase_							:	Optional[int]=0.02     ,		lowerCamelCase_							:	Tuple=1e-12     ,		lowerCamelCase_							:	Optional[int]=1e-6     ,		lowerCamelCase_							:	int=0.0     ,		lowerCamelCase_							:	Optional[Any]=2_24     ,		lowerCamelCase_							:	int=None     ,		lowerCamelCase_							:	List[Any]=None     ,		**lowerCamelCase_							:	Optional[Any]     ,		):

       '''simple docstring'''



       super().__init__(**lowerCamelCase_		)

       SCREAMING_SNAKE_CASE				:							int						=	num_channels
       SCREAMING_SNAKE_CASE				:							Any						=	patch_size
       SCREAMING_SNAKE_CASE				:							str						=	num_stages
       SCREAMING_SNAKE_CASE				:							Tuple						=	[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
       SCREAMING_SNAKE_CASE				:							Dict						=	[3, 3, 9, 3] if depths is None else depths
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	hidden_act
       SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	initializer_range
       SCREAMING_SNAKE_CASE				:							List[Any]						=	layer_norm_eps
       SCREAMING_SNAKE_CASE				:							List[Any]						=	layer_scale_init_value
       SCREAMING_SNAKE_CASE				:							List[Any]						=	drop_path_rate
       SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	image_size
       SCREAMING_SNAKE_CASE				:							str						=	["""stem"""] + [f'''stage{idx}''' for idx in range(1     ,		len(self.depths		) + 1		)]
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	get_aligned_output_features_output_indices(
           out_features=lowerCamelCase_     ,		out_indices=lowerCamelCase_     ,		stage_names=self.stage_names		)







class       UpperCamelCase__			(			lowercase_	):




     """simple docstring"""
     SCREAMING_SNAKE_CASE__						=					version.parse('''1.11'''	)


     @property
     def lowerCamelCase_	(		self							:	Union[str, Any]		):

       '''simple docstring'''



       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]		)



     @property
     def lowerCamelCase_	(		self							:	int		):

       '''simple docstring'''



       return 1e-5










'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					SCREAMING_SNAKE_CASE__						=					(ScoreSdeVeScheduler,)
					SCREAMING_SNAKE_CASE__						=					()


					@property
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	4
							SCREAMING_SNAKE_CASE				:							int						=	3
							SCREAMING_SNAKE_CASE				:							Any						=	8
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	8

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.rand((batch_size, num_channels, height, width)		)

							return sample


					@property
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	4
							SCREAMING_SNAKE_CASE				:							Tuple						=	3
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	8
							SCREAMING_SNAKE_CASE				:							List[str]						=	8

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

							return sample


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							def model(lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int     ,		*lowerCamelCase_							:	int		):
									return sample * t / (t + 1)

							return model


					def lowerCamelCase_	(		self							:	str     ,		**lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
							    """num_train_timesteps""": 20_00,
							    """snr""": 0.15,
							    """sigma_min""": 0.01,
							    """sigma_max""": 13_48,
							    """sampling_eps""": 1e-5,
							}

							config.update(**lowerCamelCase_		)
							return config


					def lowerCamelCase_	(		self							:	List[str]     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							int						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, 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"

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							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 lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)
							kwargs.update(lowerCamelCase_		)

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

									SCREAMING_SNAKE_CASE				:							int						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	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"

									SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							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 lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]     ,		[1, 1_00, 10_00]		):
									self.check_over_configs(sigma_min=lowerCamelCase_     ,		sigma_max=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							for t in [0.1, 0.5, 0.75]:
									self.check_over_forward(time_step=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							str						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							Dict						=	self.get_scheduler_config()
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler_class(**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	3

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.dummy_model()
							SCREAMING_SNAKE_CASE				:							Dict						=	self.dummy_sample_deter

							scheduler.set_sigmas(lowerCamelCase_		)
							scheduler.set_timesteps(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							str						=	torch.manual_seed(0		)

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

									for _ in range(scheduler.config.correct_steps		):
											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		).prev_sample

									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_     ,		lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							List[Any]						=	scheduler.step_pred(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	output.prev_sample, output.prev_sample_mean

							SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Any						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert np.isclose(result_sum.item()     ,		14_372_758_528.0		)
							assert np.isclose(result_mean.item()     ,		18_714_530.0		)



					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							Any						=	kwargs.pop("""num_inference_steps"""     ,		lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							List[str]						=	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"""		):
											SCREAMING_SNAKE_CASE				:							Dict						=	num_inference_steps

									SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler.step_pred(lowerCamelCase_     ,		0     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							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		)



