


import collections
import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 NatBackbone, NatForImageClassification, NatModel
						from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :str      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Dict=13      ,      lowerCAmelCase__      :int=64      ,      lowerCAmelCase__      :Optional[Any]=4      ,      lowerCAmelCase__      :int=3      ,      lowerCAmelCase__      :Union[str, Any]=16      ,      lowerCAmelCase__      :List[str]=[1, 2, 1]      ,      lowerCAmelCase__      :List[Any]=[2, 4, 8]      ,      lowerCAmelCase__      :Optional[Any]=3      ,      lowerCAmelCase__      :Optional[Any]=2.0      ,      lowerCAmelCase__      :Any=True      ,      lowerCAmelCase__      :Optional[int]=0.0      ,      lowerCAmelCase__      :Union[str, Any]=0.0      ,      lowerCAmelCase__      :int=0.1      ,      lowerCAmelCase__      :List[str]="gelu"      ,      lowerCAmelCase__      :List[str]=True      ,      lowerCAmelCase__      :Tuple=0.02      ,      lowerCAmelCase__      :Any=1E-5      ,      lowerCAmelCase__      :Tuple=True      ,      lowerCAmelCase__      :Union[str, Any]=None      ,      lowerCAmelCase__      :int=True      ,      lowerCAmelCase__      :Any=10      ,      lowerCAmelCase__      :Optional[Any]=["stage1", "stage2"]      ,      lowerCAmelCase__      :List[Any]=[1, 2]      ,      )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					int		      =		parent
										__SCREAMING_SNAKE_CASE						:					Dict		      =		batch_size
										__SCREAMING_SNAKE_CASE						:					Dict		      =		image_size
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		patch_size
										__SCREAMING_SNAKE_CASE						:					Dict		      =		num_channels
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		embed_dim
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		depths
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		num_heads
										__SCREAMING_SNAKE_CASE						:					Dict		      =		kernel_size
										__SCREAMING_SNAKE_CASE						:					Any		      =		mlp_ratio
										__SCREAMING_SNAKE_CASE						:					Dict		      =		qkv_bias
										__SCREAMING_SNAKE_CASE						:					str		      =		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:					Dict		      =		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:					str		      =		drop_path_rate
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		hidden_act
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		patch_norm
										__SCREAMING_SNAKE_CASE						:					Dict		      =		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:					Dict		      =		initializer_range
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		is_training
										__SCREAMING_SNAKE_CASE						:					Dict		      =		scope
										__SCREAMING_SNAKE_CASE						:					Any		      =		use_labels
										__SCREAMING_SNAKE_CASE						:					Any		      =		num_labels
										__SCREAMING_SNAKE_CASE						:					str		      =		out_features
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		out_indices


				def 		__magic_name__(	self      :Any     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		None
										if self.use_labels:
																__SCREAMING_SNAKE_CASE						:					Dict		      =		ids_tensor([self.batch_size]      ,      self.num_labels     )

										__SCREAMING_SNAKE_CASE						:					str		      =		self.get_config()

										return config, pixel_values, labels


				def 		__magic_name__(	self      :List[str]     )		->      int:
										return NatConfig(
										    num_labels=self.num_labels      ,      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      ,      kernel_size=self.kernel_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      ,      patch_norm=self.patch_norm      ,      layer_norm_eps=self.layer_norm_eps      ,      initializer_range=self.initializer_range      ,      out_features=self.out_features      ,      out_indices=self.out_indices      ,      )


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Dict     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		NatModel(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Any		      =		model(lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					int		      =		(config.image_size // config.patch_size) // (2 ** (len(config.depths     ) - 1))
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

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


				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Tuple     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		NatForImageClassification(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Any		      =		model(lowerCAmelCase__      ,      labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)     )

										# test greyscale images
										__SCREAMING_SNAKE_CASE						:					str		      =		1
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		NatForImageClassification(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model(lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Dict     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					int		      =		NatBackbone(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Dict		      =		model(lowerCAmelCase__     )

										# verify hidden states
										self.parent.assertEqual(len(result.feature_maps     )      ,      len(config.out_features     )     )
										self.parent.assertListEqual(list(result.feature_maps[0].shape     )      ,      [self.batch_size, model.channels[0], 16, 16]     )

										# verify channels
										self.parent.assertEqual(len(model.channels     )      ,      len(config.out_features     )     )

										# verify backbone works with out_features=None
										__SCREAMING_SNAKE_CASE						:					int		      =		None
										__SCREAMING_SNAKE_CASE						:					int		      =		NatBackbone(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					str		      =		model(lowerCAmelCase__     )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps     )      ,      1     )
										self.parent.assertListEqual(list(result.feature_maps[0].shape     )      ,      [self.batch_size, model.channels[-1], 4, 4]     )

										# verify channels
										self.parent.assertEqual(len(model.channels     )      ,      1     )



				def 		__magic_name__(	self      :int     )		->      int:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.prepare_config_and_inputs()
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		config_and_inputs
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		{'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_natten
@require_torch
class 	_lowercase				(      A__						,      A__						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Any      =		(
				    (
				        NatModel,
				        NatForImageClassification,
				        NatBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		(
				    {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		False

				SCREAMING_SNAKE_CASE__    :      int      =		False
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		False
				SCREAMING_SNAKE_CASE__    :      Tuple      =		False
				SCREAMING_SNAKE_CASE__    :      Tuple      =		False


				def 		__magic_name__(	self      :Optional[Any]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		NatModelTester(self     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		ConfigTester(self      ,      config_class=lowerCAmelCase__      ,      embed_dim=37     )


				def 		__magic_name__(	self      :List[str]     )		->      Dict:
										self.create_and_test_config_common_properties()
										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 		__magic_name__(	self      :Optional[int]     )		->      List[str]:
										return


				def 		__magic_name__(	self      :Union[str, Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase__     )


				@unittest.skip(reason='''Nat does not use inputs_embeds'''     )
				def 		__magic_name__(	self      :List[str]     )		->      Optional[int]:
										pass


				@unittest.skip(reason='''Nat does not use feedforward chunking'''     )
				def 		__magic_name__(	self      :Union[str, Any]     )		->      Union[str, Any]:
										pass


				def 		__magic_name__(	self      :Optional[Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__SCREAMING_SNAKE_CASE						:					int		      =		model_class(lowerCAmelCase__     )
																self.assertIsInstance(model.get_input_embeddings()      ,      (nn.Module)     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase__      ,      nn.Linear     )     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[str]		      =		self.model_tester.prepare_config_and_inputs_for_common()

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

																__SCREAMING_SNAKE_CASE						:					int		      =		['''pixel_values''']
																self.assertListEqual(arg_names[:1]      ,      lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      List[Any]:
										self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Any     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model_class(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()

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

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		outputs.hidden_states

										__SCREAMING_SNAKE_CASE						:					Dict		      =		getattr(
										    self.model_tester      ,      '''expected_num_hidden_layers'''      ,      len(self.model_tester.depths     ) + 1     )
										self.assertEqual(len(lowerCAmelCase__     )      ,      lowerCAmelCase__     )

										# Nat has a different seq_length
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		(
										    config.patch_size
										    if isinstance(config.patch_size      ,      collections.abc.Iterable     )
										    else (config.patch_size, config.patch_size)
										)

										__SCREAMING_SNAKE_CASE						:					int		      =		image_size[0] // patch_size[0]
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		image_size[1] // patch_size[1]

										self.assertListEqual(
										    list(hidden_states[0].shape[-3:]     )      ,      [height, width, self.model_tester.embed_dim]      ,      )

										if model_class.__name__ != "NatBackbone":
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		outputs.reshaped_hidden_states
																self.assertEqual(len(lowerCAmelCase__     )      ,      lowerCAmelCase__     )

																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Dict		      =		reshaped_hidden_states[0].shape
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		(
																    reshaped_hidden_states[0].view(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     ).permute(0      ,      2      ,      3      ,      1     )
																)
																self.assertListEqual(
																    list(reshaped_hidden_states.shape[-3:]     )      ,      [height, width, self.model_tester.embed_dim]      ,      )


				def 		__magic_name__(	self      :str     )		->      int:
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Any		      =		self.model_tester.prepare_config_and_inputs_for_common()

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(
										    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:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		True
																self.check_hidden_states_output(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		True

																self.check_hidden_states_output(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )


				@slow
				def 		__magic_name__(	self      :Optional[Any]     )		->      List[str]:
										for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		NatModel.from_pretrained(lowerCAmelCase__     )
																self.assertIsNotNone(lowerCAmelCase__     )



				def 		__magic_name__(	self      :Optional[int]     )		->      str:
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Any		      =		self.model_tester.prepare_config_and_inputs_for_common()

										__SCREAMING_SNAKE_CASE						:					Dict		      =		_config_zero_init(lowerCAmelCase__     )
										for model_class in self.all_model_classes:
																__SCREAMING_SNAKE_CASE						:					Any		      =		model_class(config=lowerCAmelCase__     )
																for name, param in model.named_parameters():
																						if "embeddings" 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_natten
@require_vision
@require_torch
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



				@cached_property
				def 		__magic_name__(	self      :str     )		->      List[Any]:
										return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ) if is_vision_available() else None



				@slow
				def 		__magic_name__(	self      :Optional[int]     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ).to(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.default_image_processor

										__SCREAMING_SNAKE_CASE						:					Dict		      =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		image_processor(images=lowerCAmelCase__      ,      return_tensors='''pt'''     ).to(lowerCAmelCase__     )

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

										# verify the logits
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.Size((1, 1_000)     )
										self.assertEqual(outputs.logits.shape      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.tensor([0.3805, -0.8676, -0.3912]     ).to(lowerCAmelCase__     )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,      lowerCAmelCase__      ,      atol=1E-4     )     )




@require_torch
@require_natten
class 	_lowercase				(      unittest.TestCase						,      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		(NatBackbone,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__    :      str      =		NatConfig


				def 		__magic_name__(	self      :int     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		NatModelTester(self     )




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

import numpy as np
import torch

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


__lowerCAmelCase     :			str      =logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      torch.FloatTensor
				SCREAMING_SNAKE_CASE__    :      Optional[torch.FloatTensor]      =		None




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__=0.999   ,	lowercase__="cosine"   ,	):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase__				):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowercase__				):
																		return math.exp(t * -12.0				)

						else:
												raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

						__SCREAMING_SNAKE_CASE						:					List[str]		      =		[]
						for i in range(lowercase__				):
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		i / num_diffusion_timesteps
												__SCREAMING_SNAKE_CASE						:					Dict		      =		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowercase__				) / alpha_bar_fn(lowercase__				)   ,	lowercase__				)				)
						return torch.tensor(lowercase__   ,	dtype=torch.floataa				)
class 	_lowercase				(      A__						,      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		[e.name for e in KarrasDiffusionSchedulers]
				SCREAMING_SNAKE_CASE__    :      Optional[int]      =		1


				@register_to_config
				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :int = 1_000      ,      lowerCAmelCase__      :float = 0.0001      ,      lowerCAmelCase__      :float = 0.02      ,      lowerCAmelCase__      :str = "linear"      ,      lowerCAmelCase__      :Optional[Union[np.ndarray, List[float]]] = None      ,      lowerCAmelCase__      :str = "epsilon"      ,      lowerCAmelCase__      :str = "linear"      ,      lowerCAmelCase__      :Optional[bool] = False      ,      lowerCAmelCase__      :str = "linspace"      ,      lowerCAmelCase__      :int = 0      ,      )		->      Optional[int]:
										if trained_betas is not None:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.tensor(lowerCAmelCase__      ,      dtype=torch.floataa     )
										elif beta_schedule == "linear":
																__SCREAMING_SNAKE_CASE						:					str		      =		torch.linspace(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      dtype=torch.floataa     )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(
																    torch.linspace(beta_start**0.5      ,      beta_end**0.5      ,      lowerCAmelCase__      ,      dtype=torch.floataa     ) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																__SCREAMING_SNAKE_CASE						:					Dict		      =		betas_for_alpha_bar(lowerCAmelCase__     )
										else:
																raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		1.0 - self.betas
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.cumprod(self.alphas      ,      dim=0     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.from_numpy(lowerCAmelCase__     )

										# setable values
										__SCREAMING_SNAKE_CASE						:					str		      =		None
										__SCREAMING_SNAKE_CASE						:					int		      =		np.linspace(0      ,      num_train_timesteps - 1      ,      lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )[::-1].copy()
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.from_numpy(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		False
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		use_karras_sigmas


				@property
				def 		__magic_name__(	self      :int     )		->      Union[str, Any]:
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[float, torch.FloatTensor]     )		->      torch.FloatTensor:
										if isinstance(lowerCAmelCase__      ,      torch.Tensor     ):
																__SCREAMING_SNAKE_CASE						:					Any		      =		timestep.to(self.timesteps.device     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		(self.timesteps == timestep).nonzero().item()
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.sigmas[step_index]

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		sample / ((sigma**2 + 1) ** 0.5)

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		True
										return sample


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Union[str, torch.device] = None     )		->      int:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.linspace(0      ,      self.config.num_train_timesteps - 1      ,      lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )[
																    ::-1
																].copy()
										elif self.config.timestep_spacing == "leading":
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.config.num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		(np.arange(0      ,      lowerCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__     )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.config.num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		(np.arange(self.config.num_train_timesteps      ,      0      ,      -step_ratio     )).round().copy().astype(lowerCAmelCase__     )
																timesteps -= 1
										else:
																raise ValueError(
																    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
										__SCREAMING_SNAKE_CASE						:					int		      =		np.log(lowerCAmelCase__     )

										if self.config.interpolation_type == "linear":
																__SCREAMING_SNAKE_CASE						:					str		      =		np.interp(lowerCAmelCase__      ,      np.arange(0      ,      len(lowerCAmelCase__     )     )      ,      lowerCAmelCase__     )
										elif self.config.interpolation_type == "log_linear":
																__SCREAMING_SNAKE_CASE						:					Any		      =		torch.linspace(np.log(sigmas[-1]     )      ,      np.log(sigmas[0]     )      ,      num_inference_steps + 1     ).exp()
										else:
																raise ValueError(
																    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
																    ''' \'linear\' or \'log_linear\''''     )

										if self.use_karras_sigmas:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self._convert_to_karras(in_sigmas=lowerCAmelCase__      ,      num_inference_steps=self.num_inference_steps     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.array([self._sigma_to_t(lowerCAmelCase__      ,      lowerCAmelCase__     ) for sigma in sigmas]     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )
										if str(lowerCAmelCase__     ).startswith('''mps'''     ):
																# mps does not support float64
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.from_numpy(lowerCAmelCase__     ).to(lowerCAmelCase__      ,      dtype=torch.floataa     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      int:
										# get log sigma
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.log(lowerCAmelCase__     )

										# get distribution
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										__SCREAMING_SNAKE_CASE						:					int		      =		np.cumsum((dists >= 0)      ,      axis=0     ).argmax(axis=0     ).clip(max=log_sigmas.shape[0] - 2     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		low_idx + 1

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		log_sigmas[low_idx]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		log_sigmas[high_idx]

										# interpolate sigmas
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(low - log_sigma) / (low - high)
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.clip(lowerCAmelCase__      ,      0      ,      1     )

										# transform interpolation to time range
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(1 - w) * low_idx + w * high_idx
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		t.reshape(sigma.shape     )
										return t


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[str, Any]     )		->      torch.FloatTensor:
										__SCREAMING_SNAKE_CASE						:					float		      =		in_sigmas[-1].item()
										__SCREAMING_SNAKE_CASE						:					float		      =		in_sigmas[0].item()

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		7.0  # 7.0 is the value used in the paper
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.linspace(0      ,      1      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		sigma_min ** (1 / rho)
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		sigma_max ** (1 / rho)
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas


				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[float, torch.FloatTensor]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :float = float('''inf'''     )      ,      lowerCAmelCase__      :float = 1.0      ,      lowerCAmelCase__      :Optional[torch.Generator] = None      ,      lowerCAmelCase__      :bool = True      ,      )		->      Union[EulerDiscreteSchedulerOutput, Tuple]:

										if (
										    isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     )
										    or isinstance(lowerCAmelCase__      ,      torch.IntTensor     )
										    or isinstance(lowerCAmelCase__      ,      torch.LongTensor     )
										):
																raise ValueError(
																    (
																        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																        ''' one of the `scheduler.timesteps` as a timestep.'''
																    )      ,      )

										if not self.is_scale_input_called:
																logger.warning(
																    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																    '''See `StableDiffusionPipeline` for a usage example.'''     )

										if isinstance(lowerCAmelCase__      ,      torch.Tensor     ):
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		timestep.to(self.timesteps.device     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(self.timesteps == timestep).nonzero().item()
										__SCREAMING_SNAKE_CASE						:					str		      =		self.sigmas[step_index]

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		min(s_churn / (len(self.sigmas     ) - 1)      ,      2**0.5 - 1     ) if s_tmin <= sigma <= s_tmax else 0.0

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		randn_tensor(
										    model_output.shape      ,      dtype=model_output.dtype      ,      device=model_output.device      ,      generator=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		noise * s_noise
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		sigma * (gamma + 1)

										if gamma > 0:
																__SCREAMING_SNAKE_CASE						:					str		      =		sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										# NOTE: "original_sample" should not be an expected prediction_type but is left in for
										# backwards compatibility
										if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																__SCREAMING_SNAKE_CASE						:					Any		      =		model_output
										elif self.config.prediction_type == "epsilon":
																__SCREAMING_SNAKE_CASE						:					Any		      =		sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

										# 2. Convert to an ODE derivative
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		(sample - pred_original_sample) / sigma_hat

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.sigmas[step_index + 1] - sigma_hat

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		sample + derivative * dt

										if not return_dict:
																return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__      ,      pred_original_sample=lowerCAmelCase__     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :torch.FloatTensor      ,      )		->      torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.sigmas.to(device=original_samples.device      ,      dtype=original_samples.dtype     )
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__     ):
																# mps does not support float64
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
																__SCREAMING_SNAKE_CASE						:					int		      =		timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.timesteps.to(original_samples.device     )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		timesteps.to(original_samples.device     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sigmas[step_indices].flatten()
										while len(sigma.shape     ) < len(original_samples.shape     ):
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sigma.unsqueeze(-1     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		original_samples + noise * sigma
										return noisy_samples



				def __len__(	self      :Optional[Any]     )		->      Union[str, Any]:
										return self.config.num_train_timesteps
