
'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
		from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor






class  A  :





	def __init__(       self      :							Union[str, Any]   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Optional[Any]=13   ,				__magic_name__      :							Tuple=32   ,				__magic_name__      :							Tuple=2   ,				__magic_name__      :							Tuple=3   ,				__magic_name__      :							Dict=16   ,				__magic_name__      :							List[Any]=[1, 2, 1]   ,				__magic_name__      :							Union[str, Any]=[2, 2, 4]   ,				__magic_name__      :							str=2   ,				__magic_name__      :							Union[str, Any]=2.0   ,				__magic_name__      :							Optional[int]=True   ,				__magic_name__      :							Dict=0.0   ,				__magic_name__      :							List[Any]=0.0   ,				__magic_name__      :							Optional[Any]=0.1   ,				__magic_name__      :							int="gelu"   ,				__magic_name__      :							List[str]=False   ,				__magic_name__      :							Optional[Any]=True   ,				__magic_name__      :							Any=0.02   ,				__magic_name__      :							List[str]=1E-5   ,				__magic_name__      :							Dict=True   ,				__magic_name__      :							List[str]=None   ,				__magic_name__      :							Optional[int]=True   ,				__magic_name__      :							str=10   ,				__magic_name__      :							List[str]=8   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				parent
				lowerCAmelCase__										=				batch_size
				lowerCAmelCase__										=				image_size
				lowerCAmelCase__										=				patch_size
				lowerCAmelCase__										=				num_channels
				lowerCAmelCase__										=				embed_dim
				lowerCAmelCase__										=				depths
				lowerCAmelCase__										=				num_heads
				lowerCAmelCase__										=				window_size
				lowerCAmelCase__										=				mlp_ratio
				lowerCAmelCase__										=				qkv_bias
				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				drop_path_rate
				lowerCAmelCase__										=				hidden_act
				lowerCAmelCase__										=				use_absolute_embeddings
				lowerCAmelCase__										=				patch_norm
				lowerCAmelCase__										=				layer_norm_eps
				lowerCAmelCase__										=				initializer_range
				lowerCAmelCase__										=				is_training
				lowerCAmelCase__										=				scope
				lowerCAmelCase__										=				use_labels
				lowerCAmelCase__										=				type_sequence_label_size
				lowerCAmelCase__										=				encoder_stride





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

				lowerCAmelCase__										=				None
				if self.use_labels:
							lowerCAmelCase__										=				ids_tensor([self.batch_size]   ,				self.type_sequence_label_size				)

				lowerCAmelCase__										=				self.get_config()

				return config, pixel_values, labels





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return SwinvaConfig(
				    image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				embed_dim=self.embed_dim   ,				depths=self.depths   ,				num_heads=self.num_heads   ,				window_size=self.window_size   ,				mlp_ratio=self.mlp_ratio   ,				qkv_bias=self.qkv_bias   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				drop_path_rate=self.drop_path_rate   ,				hidden_act=self.hidden_act   ,				use_absolute_embeddings=self.use_absolute_embeddings   ,				path_norm=self.patch_norm   ,				layer_norm_eps=self.layer_norm_eps   ,				initializer_range=self.initializer_range   ,				encoder_stride=self.encoder_stride   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							str   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				SwinvaModel(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__				)

				lowerCAmelCase__										=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
				lowerCAmelCase__										=				int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

				self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, expected_seq_len, expected_dim)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				SwinvaForMaskedImageModeling(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__				)
				self.parent.assertEqual(
				    result.logits.shape   ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

				# test greyscale images
				lowerCAmelCase__										=				1
				lowerCAmelCase__										=				SwinvaForMaskedImageModeling(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()

				lowerCAmelCase__										=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
				lowerCAmelCase__										=				model(__magic_name__				)
				self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, 1, self.image_size, self.image_size)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							str   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.type_sequence_label_size
				lowerCAmelCase__										=				SwinvaForImageClassification(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				labels=__magic_name__				)
				self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.prepare_config_and_inputs()
				lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				config_and_inputs
				lowerCAmelCase__										=				{"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Optional[int]   =  (
	    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
	)
	snake_case__   :Any   =  (
	    {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	snake_case__   :Optional[int]   =  False
	snake_case__   :Union[str, Any]   =  False
	snake_case__   :Any   =  False
	snake_case__   :List[str]   =  False





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				SwinvaModelTester(self				)
				lowerCAmelCase__										=				ConfigTester(self   ,				config_class=__magic_name__   ,				embed_dim=37				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__magic_name__				)





	@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				pass





	@unittest.skip(reason="Swinv2 does not use inputs_embeds"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				pass





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				model_class(__magic_name__				)
							self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)				)
							lowerCAmelCase__										=				model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__magic_name__   ,				nn.Linear				)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				model_class(__magic_name__				)
							lowerCAmelCase__										=				inspect.signature(model.forward				)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase__										=				[*signature.parameters.keys()]

							lowerCAmelCase__										=				["pixel_values"]
							self.assertListEqual(arg_names[:1]   ,				__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				True

				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				True
							lowerCAmelCase__										=				False
							lowerCAmelCase__										=				True
							lowerCAmelCase__										=				model_class(__magic_name__				)
							model.to(__magic_name__				)
							model.eval()
							with torch.no_grad():
										lowerCAmelCase__										=				model(**self._prepare_for_class(__magic_name__   ,				__magic_name__				)				)
							lowerCAmelCase__										=				outputs.attentions
							lowerCAmelCase__										=				len(self.model_tester.depths				)
							self.assertEqual(len(__magic_name__				)   ,				__magic_name__				)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							lowerCAmelCase__										=				True
							lowerCAmelCase__										=				config.window_size**2
							lowerCAmelCase__										=				model_class(__magic_name__				)
							model.to(__magic_name__				)
							model.eval()
							with torch.no_grad():
										lowerCAmelCase__										=				model(**self._prepare_for_class(__magic_name__   ,				__magic_name__				)				)
							lowerCAmelCase__										=				outputs.attentions
							self.assertEqual(len(__magic_name__				)   ,				__magic_name__				)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]				)   ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,				)
							lowerCAmelCase__										=				len(__magic_name__				)

							# Check attention is always last and order is fine
							lowerCAmelCase__										=				True
							lowerCAmelCase__										=				True
							lowerCAmelCase__										=				model_class(__magic_name__				)
							model.to(__magic_name__				)
							model.eval()
							with torch.no_grad():
										lowerCAmelCase__										=				model(**self._prepare_for_class(__magic_name__   ,				__magic_name__				)				)

							if hasattr(self.model_tester   ,				"num_hidden_states_types"				):
										lowerCAmelCase__										=				self.model_tester.num_hidden_states_types
							else:
										# also another +1 for reshaped_hidden_states
										lowerCAmelCase__										=				2
							self.assertEqual(out_len + added_hidden_states   ,				len(__magic_name__				)				)

							lowerCAmelCase__										=				outputs.attentions

							self.assertEqual(len(__magic_name__				)   ,				__magic_name__				)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]				)   ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Any   ,				__magic_name__      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				model_class(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()

				with torch.no_grad():
							lowerCAmelCase__										=				model(**self._prepare_for_class(__magic_name__   ,				__magic_name__				)				)

				lowerCAmelCase__										=				outputs.hidden_states

				lowerCAmelCase__										=				getattr(
				    self.model_tester   ,				"expected_num_hidden_layers"   ,				len(self.model_tester.depths				) + 1				)
				self.assertEqual(len(__magic_name__				)   ,				__magic_name__				)

				# Swinv2 has a different seq_length
				lowerCAmelCase__										=				(
				    config.patch_size
				    if isinstance(config.patch_size   ,				collections.abc.Iterable				)
				    else (config.patch_size, config.patch_size)
				)

				lowerCAmelCase__										=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]				)   ,				[num_patches, self.model_tester.embed_dim]   ,				)

				lowerCAmelCase__										=				outputs.reshaped_hidden_states
				self.assertEqual(len(__magic_name__				)   ,				__magic_name__				)

				lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				reshaped_hidden_states[0].shape
				lowerCAmelCase__										=				(
				    reshaped_hidden_states[0].view(__magic_name__   ,				__magic_name__   ,				height * width				).permute(0   ,				2   ,				1				)
				)
				self.assertListEqual(
				    list(reshaped_hidden_states.shape[-2:]				)   ,				[num_patches, self.model_tester.embed_dim]   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

				lowerCAmelCase__										=				(
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size   ,				collections.abc.Iterable				)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)

				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				True
							self.check_hidden_states_output(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__				)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowerCAmelCase__										=				True

							self.check_hidden_states_output(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				3

				lowerCAmelCase__										=				(
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size   ,				collections.abc.Iterable				)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)
				lowerCAmelCase__										=				(
				    config.patch_size
				    if isinstance(config.patch_size   ,				collections.abc.Iterable				)
				    else (config.patch_size, config.patch_size)
				)

				lowerCAmelCase__										=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				lowerCAmelCase__										=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				True
							self.check_hidden_states_output(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				(padded_height, padded_width)				)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowerCAmelCase__										=				True
							self.check_hidden_states_output(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				(padded_height, padded_width)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__magic_name__				)





	@slow
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__										=				SwinvaModel.from_pretrained(__magic_name__				)
							self.assertIsNotNone(__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

				lowerCAmelCase__										=				_config_zero_init(__magic_name__				)
				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				model_class(config=__magic_name__				)
							for name, param in model.named_parameters():
										if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1E9).round() / 1E9).item()   ,				[0.0, 1.0]   ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   ,				)






@require_vision
@require_torch
class  A  (						unittest.TestCase     ):





	@cached_property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return (
				    AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"				)
				    if is_vision_available()
				    else None
				)







	@slow
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"				).to(
				    __magic_name__				)
				lowerCAmelCase__										=				self.default_image_processor

				lowerCAmelCase__										=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
				lowerCAmelCase__										=				image_processor(images=__magic_name__   ,				return_tensors="pt"				).to(__magic_name__				)

				# forward pass
				with torch.no_grad():
							lowerCAmelCase__										=				model(**__magic_name__				)

				# verify the logits
				lowerCAmelCase__										=				torch.Size((1, 1000)				)
				self.assertEqual(outputs.logits.shape   ,				__magic_name__				)
				lowerCAmelCase__										=				torch.tensor([-0.3947, -0.4306, 0.0026]				).to(__magic_name__				)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				__magic_name__   ,				atol=1E-4				)				)













# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					torch.FloatTensor
       lowerCamelCase				:					torch.FloatTensor




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					int								=			1



       @register_to_config
       def __init__(				self      ,    a__ = 20_00      ,    a__ = 0.15      ,    a__ = 0.01      ,    a__ = 13_48.0      ,    a__ = 1e-5      ,    a__ = 1      ,    )			->   Any:






            '''simple docstring'''





            __snake_case      :int 							=    sigma_max

            # setable values
            __snake_case      :Any 							=    None

            self.set_sigmas(a__      ,    a__      ,    a__      ,    a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   torch.FloatTensor:






            '''simple docstring'''





            return sample



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

            __snake_case      :int 							=    torch.linspace(1      ,    a__      ,    a__      ,    device=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    sigma_min if sigma_min is not None else self.config.sigma_min
            __snake_case      :Any 							=    sigma_max if sigma_max is not None else self.config.sigma_max
            __snake_case      :Any 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                 self.set_timesteps(a__      ,    a__     )

            __snake_case      :int 							=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            __snake_case      :List[str] 							=    torch.exp(torch.linspace(math.log(a__     )      ,    math.log(a__     )      ,    a__     )     )
            __snake_case      :Any 							=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Tuple:






            '''simple docstring'''





            return torch.where(
                timesteps == 0      ,    torch.zeros_like(t.to(timesteps.device     )     )      ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device     )      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SdeVeOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            __snake_case      :Optional[int] 							=    timestep * torch.ones(
                sample.shape[0]      ,    device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
            __snake_case      :Tuple 							=    (timestep * (len(self.timesteps     ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            __snake_case      :str 							=    timesteps.to(self.discrete_sigmas.device     )

            __snake_case      :str 							=    self.discrete_sigmas[timesteps].to(sample.device     )
            __snake_case      :Union[str, Any] 							=    self.get_adjacent_sigma(a__      ,    a__     ).to(sample.device     )
            __snake_case      :Dict 							=    torch.zeros_like(a__     )
            __snake_case      :str 							=    (sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            __snake_case      :Optional[Any] 							=    diffusion.flatten()
            while len(diffusion.shape     ) < len(sample.shape     ):
                 __snake_case      :int 							=    diffusion.unsqueeze(-1     )
            __snake_case      :Optional[Any] 							=    drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            __snake_case      :str 							=    randn_tensor(
                sample.shape      ,    layout=sample.layout      ,    generator=a__      ,    device=sample.device      ,    dtype=sample.dtype     )
            __snake_case      :str 							=    sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            __snake_case      :str 							=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=a__      ,    prev_sample_mean=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SchedulerOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            __snake_case      :Any 							=    randn_tensor(sample.shape      ,    layout=sample.layout      ,    generator=a__     ).to(sample.device     )

            # compute step size from the model_output, the noise, and the snr
            __snake_case      :List[Any] 							=    torch.norm(model_output.reshape(model_output.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Tuple 							=    torch.norm(noise.reshape(noise.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Dict 							=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
            __snake_case      :int 							=    step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            __snake_case      :Tuple 							=    step_size.flatten()
            while len(step_size.shape     ) < len(sample.shape     ):
                 __snake_case      :Optional[int] 							=    step_size.unsqueeze(-1     )
            __snake_case      :List[str] 							=    sample + step_size * model_output
            __snake_case      :Tuple 							=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample,)

            return SchedulerOutput(prev_sample=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    )			->   torch.FloatTensor:






            '''simple docstring'''





            __snake_case      :Tuple 							=    timesteps.to(original_samples.device     )
            __snake_case      :Optional[Any] 							=    self.discrete_sigmas.to(original_samples.device     )[timesteps]
            __snake_case      :int 							=    (
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(a__     ) * sigmas[:, None, None, None]
            )
            __snake_case      :str 							=    noise + original_samples
            return noisy_samples



       def __len__(				self     )			->   Optional[int]:






            '''simple docstring'''





            return self.config.num_train_timesteps


