
"""simple docstring"""





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 A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		torch.FloatTensor
  SCREAMING_SNAKE_CASE__     :		Optional[torch.FloatTensor]    	=						None







def       __a	(						__lowerCamelCase,						__lowerCamelCase=0.999,						__lowerCamelCase="cosine",						):
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   UpperCAmelCase_   :   Dict     	=       []
   for i in range(__lowerCamelCase    ):
      UpperCAmelCase_   :   Optional[Any]     	=       i / num_diffusion_timesteps
      UpperCAmelCase_   :   List[str]     	=       (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__lowerCamelCase    ) / alpha_bar_fn(__lowerCamelCase    ),						__lowerCamelCase    )    )
   return torch.tensor(__lowerCamelCase,						dtype=torch.floataa    )


class A_      (lowercase__							,lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						[e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE__     :		int    	=						1







  @register_to_config
  def __init__(						self							,  lowercase_ = 1000							,  lowercase_ = 0.00_01							,  lowercase_ = 0.02							,  lowercase_ = "linear"							,  lowercase_ = None							,  lowercase_ = False							,  lowercase_ = "epsilon"							,  lowercase_ = "linspace"							,  lowercase_ = 0							,  ):

     """simple docstring"""
     if trained_betas is not None:
        UpperCAmelCase_   :   int     	=       torch.tensor(lowercase_							,  dtype=torch.floataa    )
     elif beta_schedule == "linear":
        UpperCAmelCase_   :   List[str]     	=       torch.linspace(lowercase_							,  lowercase_							,  lowercase_							,  dtype=torch.floataa    )
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        UpperCAmelCase_   :   Optional[int]     	=       (
            torch.linspace(beta_start**0.5							,  beta_end**0.5							,  lowercase_							,  dtype=torch.floataa    ) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        UpperCAmelCase_   :   Any     	=       betas_for_alpha_bar(lowercase_    )
     else:
        raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

     UpperCAmelCase_   :   Dict     	=       1.0 - self.betas
     UpperCAmelCase_   :   Tuple     	=       torch.cumprod(self.alphas							,  dim=0    )

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

     # setable values
     UpperCAmelCase_   :   List[str]     	=       None
     UpperCAmelCase_   :   int     	=       use_karras_sigmas
     self.set_timesteps(lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   int     	=       []
     UpperCAmelCase_   :   Tuple     	=       False







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # 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							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     if isinstance(lowercase_							,  torch.Tensor    ):
        UpperCAmelCase_   :   Optional[int]     	=       timestep.to(self.timesteps.device    )
     UpperCAmelCase_   :   Tuple     	=       (self.timesteps == timestep).nonzero().item()
     UpperCAmelCase_   :   int     	=       self.sigmas[step_index]
     UpperCAmelCase_   :   Union[str, Any]     	=       sample / ((sigma**2 + 1) ** 0.5)
     UpperCAmelCase_   :   int     	=       True
     return sample







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""

     def lms_derivative(lowercase_    ):
        UpperCAmelCase_   :   Dict     	=       1.0
        for k in range(lowercase_    ):
           if current_order == k:
              continue
           prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
        return prod

     UpperCAmelCase_   :   List[str]     	=       integrate.quad(lowercase_							,  self.sigmas[t]							,  self.sigmas[t + 1]							,  epsrel=1E-4    )[0]

     return integrated_coeff







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       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":
        UpperCAmelCase_   :   Dict     	=       np.linspace(0							,  self.config.num_train_timesteps - 1							,  lowercase_							,  dtype=lowercase_    )[
            ::-1
        ].copy()
     elif self.config.timestep_spacing == "leading":
        UpperCAmelCase_   :   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
        UpperCAmelCase_   :   Tuple     	=       (np.arange(0							,  lowercase_    ) * step_ratio).round()[::-1].copy().astype(lowercase_    )
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        UpperCAmelCase_   :   Optional[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
        UpperCAmelCase_   :   Optional[Any]     	=       (np.arange(self.config.num_train_timesteps							,  0							,  -step_ratio    )).round().copy().astype(lowercase_    )
        timesteps -= 1
     else:
        raise ValueError(
            F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

     UpperCAmelCase_   :   int     	=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
     UpperCAmelCase_   :   Union[str, Any]     	=       np.log(lowercase_    )
     UpperCAmelCase_   :   str     	=       np.interp(lowercase_							,  np.arange(0							,  len(lowercase_    )    )							,  lowercase_    )

     if self.use_karras_sigmas:
        UpperCAmelCase_   :   List[Any]     	=       self._convert_to_karras(in_sigmas=lowercase_    )
        UpperCAmelCase_   :   Any     	=       np.array([self._sigma_to_t(lowercase_							,  lowercase_    ) for sigma in sigmas]    )

     UpperCAmelCase_   :   Union[str, Any]     	=       np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )

     UpperCAmelCase_   :   Dict     	=       torch.from_numpy(lowercase_    ).to(device=lowercase_    )
     if str(lowercase_    ).startswith("mps"    ):
        # mps does not support float64
        UpperCAmelCase_   :   List[str]     	=       torch.from_numpy(lowercase_    ).to(lowercase_							,  dtype=torch.floataa    )
     else:
        UpperCAmelCase_   :   str     	=       torch.from_numpy(lowercase_    ).to(device=lowercase_    )

     UpperCAmelCase_   :   int     	=       []







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     # get log sigma
     UpperCAmelCase_   :   List[str]     	=       np.log(lowercase_    )

     # get distribution
     UpperCAmelCase_   :   str     	=       log_sigma - log_sigmas[:, np.newaxis]

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

     UpperCAmelCase_   :   Optional[Any]     	=       log_sigmas[low_idx]
     UpperCAmelCase_   :   Dict     	=       log_sigmas[high_idx]

     # interpolate sigmas
     UpperCAmelCase_   :   List[Any]     	=       (low - log_sigma) / (low - high)
     UpperCAmelCase_   :   Optional[Any]     	=       np.clip(lowercase_							,  0							,  1    )

     # transform interpolation to time range
     UpperCAmelCase_   :   Tuple     	=       (1 - w) * low_idx + w * high_idx
     UpperCAmelCase_   :   Tuple     	=       t.reshape(sigma.shape    )
     return t







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   float     	=       in_sigmas[-1].item()
     UpperCAmelCase_   :   float     	=       in_sigmas[0].item()

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_ = 4							,  lowercase_ = True							,  ):

     """simple docstring"""
     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(lowercase_							,  torch.Tensor    ):
        UpperCAmelCase_   :   List[Any]     	=       timestep.to(self.timesteps.device    )
     UpperCAmelCase_   :   Union[str, Any]     	=       (self.timesteps == timestep).nonzero().item()
     UpperCAmelCase_   :   str     	=       self.sigmas[step_index]

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

     # 3. Compute linear multistep coefficients
     UpperCAmelCase_   :   Optional[int]     	=       min(step_index + 1							,  lowercase_    )
     UpperCAmelCase_   :   str     	=       [self.get_lms_coefficient(lowercase_							,  lowercase_							,  lowercase_    ) for curr_order in range(lowercase_    )]

     # 4. Compute previous sample based on the derivatives path
     UpperCAmelCase_   :   Optional[int]     	=       sample + sum(
         coeff * derivative for coeff, derivative in zip(lowercase_							,  reversed(self.derivatives    )    )    )

     if not return_dict:
        return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=lowercase_							,  pred_original_sample=lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     UpperCAmelCase_   :   Any     	=       self.sigmas.to(device=original_samples.device							,  dtype=original_samples.dtype    )
     if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_    ):
        # mps does not support float64
        UpperCAmelCase_   :   int     	=       self.timesteps.to(original_samples.device							,  dtype=torch.floataa    )
        UpperCAmelCase_   :   Tuple     	=       timesteps.to(original_samples.device							,  dtype=torch.floataa    )
     else:
        UpperCAmelCase_   :   List[str]     	=       self.timesteps.to(original_samples.device    )
        UpperCAmelCase_   :   Optional[int]     	=       timesteps.to(original_samples.device    )

     UpperCAmelCase_   :   str     	=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

     UpperCAmelCase_   :   Tuple     	=       sigmas[step_indices].flatten()
     while len(sigma.shape    ) < len(original_samples.shape    ):
        UpperCAmelCase_   :   int     	=       sigma.unsqueeze(-1    )

     UpperCAmelCase_   :   str     	=       original_samples + noise * sigma
     return noisy_samples







  def __len__(						self    ):

     """simple docstring"""
     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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[str]=7   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	Any=18   , __lowerCamelCase							:	Optional[int]=30   , __lowerCamelCase							:	Union[str, Any]=4_00   , __lowerCamelCase							:	Optional[int]=True   , __lowerCamelCase							:	List[str]=1 / 2_55   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[int]=8   , )   ->	Optional[Any]:
							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_rescale
							a       =     rescale_factor
							a       =     do_pad
							a       =     pad_size





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Optional[int]:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Any=False   , __lowerCamelCase							:	List[Any]=False				)   ->	List[Any]:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
										a       =     []
										for i in range(self.batch_size				):
													image_inputs.append(
													    np.random.randint(
													        2_55   , size=(self.num_channels, self.max_resolution, self.max_resolution)   , dtype=np.uinta				)				)
							else:
										a       =     []
										for i in range(self.batch_size				):
													a       ,						a       =     np.random.choice(np.arange(self.min_resolution   , self.max_resolution				)   , 2				)
													image_inputs.append(np.random.randint(2_55   , size=(self.num_channels, width, height)   , dtype=np.uinta				)				)

							if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										a       =     [Image.fromarray(np.moveaxis(__lowerCamelCase   , 0   , -1				)				) for x in image_inputs]

							if torchify:
										a       =     [torch.from_numpy(__lowerCamelCase				) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   SwinaSRImageProcessor if is_vision_available() else None





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     SwinaSRImageProcessingTester(self				)





				@property
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_rescale"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "rescale_factor"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_pad"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "pad_size"				)				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Union[str, Any]:
							pass





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	int				)   ->	Dict:
							a       ,						a       =     get_image_size(__lowerCamelCase				)
							a       =     self.image_processor_tester.pad_size

							a       =     (old_height // size + 1) * size - old_height
							a       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , Image.Image				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(np.array(image_inputs[0]				)				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , numpify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , np.ndarray				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , torchify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , torch.Tensor				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )






