



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

import numpy as np
import torch
from scipy import integrate

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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class        __snake_case		(  UpperCamelCase_		):
							_a       =				42
							_a       =				None





def     UpperCamelCase(						__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Optional[int]=0.9_9_9  ,__UpperCamelCase							:	Union[str, Any]="cosine"  ,):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__UpperCamelCase							:	int				):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(__UpperCamelCase							:	str				):
						return math.exp(t * -1_2.0				)

		else:
				raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

		lowerCAmelCase_    :  Any             =				[]
		for i in range(__UpperCamelCase				):
				lowerCAmelCase_    :  Tuple             =				i / num_diffusion_timesteps
				lowerCAmelCase_    :  str             =				(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__UpperCamelCase				) / alpha_bar_fn(__UpperCamelCase				)  ,__UpperCamelCase				)				)
		return torch.tensor(__UpperCamelCase  ,dtype=torch.floataa				)





class        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		):
							_a       =				[e.name for e in KarrasDiffusionSchedulers]
							_a       =				1




							@register_to_config
							def __init__( self  : Union[str, Any]       ,       A_  : int = 1_0_0_0       ,       A_  : float = 0.0001       ,       A_  : float = 0.02       ,       A_  : str = "linear"       ,       A_  : Optional[Union[np.ndarray, List[float]]] = None       ,       A_  : Optional[bool] = False       ,       A_  : str = "epsilon"       ,       A_  : str = "linspace"       ,       A_  : int = 0       ,       ):
									if trained_betas is not None:
											lowerCAmelCase_    :  List[Any]             =				torch.tensor(A_       ,       dtype=torch.floataa)
									elif beta_schedule == "linear":
											lowerCAmelCase_    :  Any             =				torch.linspace(A_       ,       A_       ,       A_       ,       dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											lowerCAmelCase_    :  Any             =				(
											    torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       A_       ,       dtype=torch.floataa) ** 2
											)
									elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											lowerCAmelCase_    :  List[str]             =				betas_for_alpha_bar(A_)
									else:
											raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

									lowerCAmelCase_    :  Any             =				1.0 - self.betas
									lowerCAmelCase_    :  Optional[int]             =				torch.cumprod(self.alphas       ,       dim=0)

									lowerCAmelCase_    :  Optional[int]             =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									lowerCAmelCase_    :  Optional[Any]             =				np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
									lowerCAmelCase_    :  Dict             =				torch.from_numpy(A_)

									# setable values
									lowerCAmelCase_    :  Tuple             =				None
									lowerCAmelCase_    :  Dict             =				use_karras_sigmas
									self.set_timesteps(A_       ,       A_)
									lowerCAmelCase_    :  str             =				[]
									lowerCAmelCase_    :  List[Any]             =				False




							@property
							def      UpperCAmelCase__    ( self  : 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      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : torch.FloatTensor       ,       A_  : Union[float, torch.FloatTensor]):
									if isinstance(A_       ,       torch.Tensor):
											lowerCAmelCase_    :  Optional[int]             =				timestep.to(self.timesteps.device)
									lowerCAmelCase_    :  str             =				(self.timesteps == timestep).nonzero().item()
									lowerCAmelCase_    :  Union[str, Any]             =				self.sigmas[step_index]
									lowerCAmelCase_    :  Optional[int]             =				sample / ((sigma**2 + 1) ** 0.5)
									lowerCAmelCase_    :  Any             =				True
									return sample




							def      UpperCAmelCase__    ( self  : Any       ,       A_  : Any       ,       A_  : Optional[Any]       ,       A_  : int):

									def lms_derivative(A_  : Any):
											lowerCAmelCase_    :  int             =				1.0
											for k in range(A_):
													if current_order == k:
															continue
													prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
											return prod

									lowerCAmelCase_    :  List[Any]             =				integrate.quad(A_       ,       self.sigmas[t]       ,       self.sigmas[t + 1]       ,       epsrel=1e-4)[0]

									return integrated_coeff




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : int       ,       A_  : Union[str, torch.device] = None):
									lowerCAmelCase_    :  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":
											lowerCAmelCase_    :  str             =				np.linspace(0       ,       self.config.num_train_timesteps - 1       ,       A_       ,       dtype=A_)[
											    ::-1
											].copy()
									elif self.config.timestep_spacing == "leading":
											lowerCAmelCase_    :  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
											lowerCAmelCase_    :  Optional[Any]             =				(np.arange(0       ,       A_) * step_ratio).round()[::-1].copy().astype(A_)
											timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
											lowerCAmelCase_    :  Union[str, Any]             =				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
											lowerCAmelCase_    :  Dict             =				(np.arange(self.config.num_train_timesteps       ,       0       ,       -step_ratio)).round().copy().astype(A_)
											timesteps -= 1
									else:
											raise ValueError(
											    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

									lowerCAmelCase_    :  Optional[Any]             =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									lowerCAmelCase_    :  Dict             =				np.log(A_)
									lowerCAmelCase_    :  int             =				np.interp(A_       ,       np.arange(0       ,       len(A_))       ,       A_)

									if self.use_karras_sigmas:
											lowerCAmelCase_    :  Union[str, Any]             =				self._convert_to_karras(in_sigmas=A_)
											lowerCAmelCase_    :  Tuple             =				np.array([self._sigma_to_t(A_       ,       A_) for sigma in sigmas])

									lowerCAmelCase_    :  Optional[Any]             =				np.concatenate([sigmas, [0.0]]).astype(np.floataa)

									lowerCAmelCase_    :  int             =				torch.from_numpy(A_).to(device=A_)
									if str(A_).startswith('''mps'''):
											# mps does not support float64
											lowerCAmelCase_    :  List[Any]             =				torch.from_numpy(A_).to(A_       ,       dtype=torch.floataa)
									else:
											lowerCAmelCase_    :  List[str]             =				torch.from_numpy(A_).to(device=A_)

									lowerCAmelCase_    :  Dict             =				[]




							def      UpperCAmelCase__    ( self  : int       ,       A_  : List[str]       ,       A_  : Union[str, Any]):
									# get log sigma
									lowerCAmelCase_    :  List[str]             =				np.log(A_)

									# get distribution
									lowerCAmelCase_    :  Union[str, Any]             =				log_sigma - log_sigmas[:, np.newaxis]

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

									lowerCAmelCase_    :  int             =				log_sigmas[low_idx]
									lowerCAmelCase_    :  List[Any]             =				log_sigmas[high_idx]

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

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




							def      UpperCAmelCase__    ( self  : Tuple       ,       A_  : torch.FloatTensor):
									lowerCAmelCase_    :  float             =				in_sigmas[-1].item()
									lowerCAmelCase_    :  float             =				in_sigmas[0].item()

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




							def      UpperCAmelCase__    ( self  : Optional[int]       ,       A_  : torch.FloatTensor       ,       A_  : Union[float, torch.FloatTensor]       ,       A_  : torch.FloatTensor       ,       A_  : int = 4       ,       A_  : bool = True       ,       ):
									if not self.is_scale_input_called:
											warnings.warn(
											    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
											    '''See `StableDiffusionPipeline` for a usage example.''')

									if isinstance(A_       ,       torch.Tensor):
											lowerCAmelCase_    :  Any             =				timestep.to(self.timesteps.device)
									lowerCAmelCase_    :  int             =				(self.timesteps == timestep).nonzero().item()
									lowerCAmelCase_    :  Optional[int]             =				self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
											lowerCAmelCase_    :  List[Any]             =				sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											lowerCAmelCase_    :  Any             =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
											lowerCAmelCase_    :  int             =				model_output
									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
									lowerCAmelCase_    :  Tuple             =				(sample - pred_original_sample) / sigma
									self.derivatives.append(A_)
									if len(self.derivatives) > order:
											self.derivatives.pop(0)

									# 3. Compute linear multistep coefficients
									lowerCAmelCase_    :  Dict             =				min(step_index + 1       ,       A_)
									lowerCAmelCase_    :  Tuple             =				[self.get_lms_coefficient(A_       ,       A_       ,       A_) for curr_order in range(A_)]

									# 4. Compute previous sample based on the derivatives path
									lowerCAmelCase_    :  List[Any]             =				sample + sum(
									    coeff * derivative for coeff, derivative in zip(A_       ,       reversed(self.derivatives)))

									if not return_dict:
											return (prev_sample,)

									return LMSDiscreteSchedulerOutput(prev_sample=A_       ,       pred_original_sample=A_)




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : torch.FloatTensor       ,       A_  : torch.FloatTensor       ,       A_  : torch.FloatTensor       ,       ):
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									lowerCAmelCase_    :  Optional[int]             =				self.sigmas.to(device=original_samples.device       ,       dtype=original_samples.dtype)
									if original_samples.device.type == "mps" and torch.is_floating_point(A_):
											# mps does not support float64
											lowerCAmelCase_    :  int             =				self.timesteps.to(original_samples.device       ,       dtype=torch.floataa)
											lowerCAmelCase_    :  Any             =				timesteps.to(original_samples.device       ,       dtype=torch.floataa)
									else:
											lowerCAmelCase_    :  Dict             =				self.timesteps.to(original_samples.device)
											lowerCAmelCase_    :  int             =				timesteps.to(original_samples.device)

									lowerCAmelCase_    :  Tuple             =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

									lowerCAmelCase_    :  str             =				original_samples + noise * sigma
									return noisy_samples







							def __len__( self  : Optional[Any]):
									return self.config.num_train_timesteps










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor



class        __snake_case		(  unittest.TestCase		):




							def __init__( self  : Union[str, Any]       ,       A_  : Optional[Any]       ,       A_  : Optional[Any]=7       ,       A_  : str=3       ,       A_  : Optional[Any]=1_8       ,       A_  : Union[str, Any]=3_0       ,       A_  : str=4_0_0       ,       A_  : List[str]=True       ,       A_  : str=None       ,       A_  : str=True       ,       A_  : List[Any]=None       ,       A_  : List[Any]=True       ,       A_  : Optional[Any]=[0.5, 0.5, 0.5]       ,       A_  : Optional[int]=[0.5, 0.5, 0.5]       ,       ):
									lowerCAmelCase_    :  List[str]             =				size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									lowerCAmelCase_    :  List[Any]             =				crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_    :  str             =				parent
									lowerCAmelCase_    :  Union[str, Any]             =				batch_size
									lowerCAmelCase_    :  Optional[Any]             =				num_channels
									lowerCAmelCase_    :  List[str]             =				image_size
									lowerCAmelCase_    :  List[str]             =				min_resolution
									lowerCAmelCase_    :  Union[str, Any]             =				max_resolution
									lowerCAmelCase_    :  Optional[int]             =				do_resize
									lowerCAmelCase_    :  Dict             =				size
									lowerCAmelCase_    :  Dict             =				do_center_crop
									lowerCAmelCase_    :  str             =				crop_size
									lowerCAmelCase_    :  List[Any]             =				do_normalize
									lowerCAmelCase_    :  str             =				image_mean
									lowerCAmelCase_    :  Dict             =				image_std







							def      UpperCAmelCase__    ( self  : int):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}





@require_torch
@require_vision
class        __snake_case		(  UpperCamelCase_		,unittest.TestCase		):
							_a       =				DeiTImageProcessor if is_vision_available() else None
							_a       =				True




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  int             =				DeiTImageProcessingTester(self)




							@property
							def      UpperCAmelCase__    ( self  : Optional[Any]):
									return self.image_processor_tester.prepare_image_processor_dict()




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  List[Any]             =				self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(A_       ,       '''do_resize'''))
									self.assertTrue(hasattr(A_       ,       '''size'''))
									self.assertTrue(hasattr(A_       ,       '''do_center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''do_normalize'''))
									self.assertTrue(hasattr(A_       ,       '''image_mean'''))
									self.assertTrue(hasattr(A_       ,       '''image_std'''))




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Tuple             =				self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,       {'''height''': 2_0, '''width''': 2_0})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_    :  Any             =				self.image_processing_class.from_dict(self.image_processor_dict       ,       size=4_2       ,       crop_size=8_4)
									self.assertEqual(image_processor.size       ,       {'''height''': 4_2, '''width''': 4_2})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 8_4, '''width''': 8_4})




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									pass




							def      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  List[str]             =				self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									lowerCAmelCase_    :  Any             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       Image.Image)

									# Test not batched input
									lowerCAmelCase_    :  Union[str, Any]             =				image_processing(image_inputs[0]       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )

									# Test batched
									lowerCAmelCase_    :  Dict             =				image_processing(A_       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )




							def      UpperCAmelCase__    ( self  : List[str]):
									# Initialize image_processing
									lowerCAmelCase_    :  Union[str, Any]             =				self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       numpify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       np.ndarray)

									# Test not batched input
									lowerCAmelCase_    :  Optional[Any]             =				image_processing(image_inputs[0]       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )

									# Test batched
									lowerCAmelCase_    :  str             =				image_processing(A_       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )







							def      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  str             =				self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       torchify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       torch.Tensor)

									# Test not batched input
									lowerCAmelCase_    :  Dict             =				image_processing(image_inputs[0]       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )

									# Test batched
									lowerCAmelCase_    :  Tuple             =				image_processing(A_       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )





