

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    __UpperCAmelCase	(			_UpperCAmelCase		):
   UpperCamelCase  	=	42
   UpperCamelCase  	=	None






def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     Any=0.999							,					UpperCAmelCase				:     int="cosine"							,					)    ->		str:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCAmelCase				:     Optional[int]     ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(UpperCAmelCase				:     List[str]     ):
      return math.exp(t * -12.0     )

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

  UpperCAmelCase			:				Optional[Any]										=					[]
  for i in range(UpperCAmelCase     ):
    UpperCAmelCase			:				Dict										=					i / num_diffusion_timesteps
    UpperCAmelCase			:				Union[str, Any]										=					(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    __UpperCAmelCase	(			_UpperCAmelCase     ,    _UpperCAmelCase		):
   UpperCamelCase  	=	[e.name for e in KarrasDiffusionSchedulers]
   UpperCamelCase  	=	1



   @register_to_config
   def __init__(				self				:	Any,						__A				:	int = 1_0_0_0,						__A				:	float = 0.0_0_0_1,						__A				:	float = 0.0_2,						__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:
       UpperCAmelCase			:				int										=					torch.tensor(SCREAMING_SNAKE_CASE_,						dtype=torch.floataa		)
     elif beta_schedule == "linear":
       UpperCAmelCase			:				Union[str, Any]										=					torch.linspace(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						dtype=torch.floataa		)
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase			:				int										=					(
           torch.linspace(beta_start**0.5,						beta_end**0.5,						SCREAMING_SNAKE_CASE_,						dtype=torch.floataa		) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCAmelCase			:				List[Any]										=					betas_for_alpha_bar(SCREAMING_SNAKE_CASE_		)
     else:
       raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

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

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

     # setable values
     UpperCAmelCase			:				List[Any]										=					None
     UpperCAmelCase			:				Union[str, Any]										=					use_karras_sigmas
     self.set_timesteps(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_		)
     UpperCAmelCase			:				Any										=					[]
     UpperCAmelCase			:				Tuple										=					False



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



   def 			__magic_name__       (				self				:	int,						__A				:	str,						__A				:	Union[str, Any],						__A				:	Dict		):

     def lms_derivative(__A				:	List[str]		):
       UpperCAmelCase			:				Any										=					1.0
       for k in range(SCREAMING_SNAKE_CASE_		):
         if current_order == k:
           continue
         prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
       return prod

     UpperCAmelCase			:				List[Any]										=					integrate.quad(SCREAMING_SNAKE_CASE_,						self.sigmas[t],						self.sigmas[t + 1],						epsrel=1E-4		)[0]

     return integrated_coeff



   def 			__magic_name__       (				self				:	int,						__A				:	int,						__A				:	Union[str, torch.device] = None		):
     UpperCAmelCase			:				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			:				Optional[Any]										=					np.linspace(0,						self.config.num_train_timesteps - 1,						SCREAMING_SNAKE_CASE_,						dtype=SCREAMING_SNAKE_CASE_		)[
           ::-1
       ].copy()
     elif self.config.timestep_spacing == "leading":
       UpperCAmelCase			:				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
       UpperCAmelCase			:				int										=					(np.arange(0,						SCREAMING_SNAKE_CASE_		) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_		)
       timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
       UpperCAmelCase			:				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
       UpperCAmelCase			:				Union[str, Any]										=					(np.arange(self.config.num_train_timesteps,						0,						-step_ratio		)).round().copy().astype(SCREAMING_SNAKE_CASE_		)
       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			:				List[Any]										=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
     UpperCAmelCase			:				List[Any]										=					np.log(SCREAMING_SNAKE_CASE_		)
     UpperCAmelCase			:				Any										=					np.interp(SCREAMING_SNAKE_CASE_,						np.arange(0,						len(SCREAMING_SNAKE_CASE_		)		),						SCREAMING_SNAKE_CASE_		)

     if self.use_karras_sigmas:
       UpperCAmelCase			:				str										=					self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_		)
       UpperCAmelCase			:				List[str]										=					np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_		) for sigma in sigmas]		)

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

     UpperCAmelCase			:				int										=					torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(device=SCREAMING_SNAKE_CASE_		)
     if str(SCREAMING_SNAKE_CASE_		).startswith('''mps'''		):
       # mps does not support float64
       UpperCAmelCase			:				Optional[int]										=					torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_,						dtype=torch.floataa		)
     else:
       UpperCAmelCase			:				int										=					torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(device=SCREAMING_SNAKE_CASE_		)

     UpperCAmelCase			:				List[Any]										=					[]



   def 			__magic_name__       (				self				:	Optional[Any],						__A				:	Tuple,						__A				:	Optional[Any]		):
     # get log sigma
     UpperCAmelCase			:				str										=					np.log(SCREAMING_SNAKE_CASE_		)

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

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

     UpperCAmelCase			:				Any										=					log_sigmas[low_idx]
     UpperCAmelCase			:				int										=					log_sigmas[high_idx]

     # interpolate sigmas
     UpperCAmelCase			:				Any										=					(low - log_sigma) / (low - high)
     UpperCAmelCase			:				Tuple										=					np.clip(SCREAMING_SNAKE_CASE_,						0,						1		)

     # transform interpolation to time range
     UpperCAmelCase			:				Dict										=					(1 - w) * low_idx + w * high_idx
     UpperCAmelCase			:				Union[str, Any]										=					t.reshape(sigma.shape		)
     return t



   def 			__magic_name__       (				self				:	int,						__A				:	torch.FloatTensor		):
     UpperCAmelCase			:				float										=					in_sigmas[-1].item()
     UpperCAmelCase			:				float										=					in_sigmas[0].item()

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



   def 			__magic_name__       (				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(SCREAMING_SNAKE_CASE_,						torch.Tensor		):
       UpperCAmelCase			:				Any										=					timestep.to(self.timesteps.device		)
     UpperCAmelCase			:				int										=					(self.timesteps == timestep).nonzero().item()
     UpperCAmelCase			:				Optional[int]										=					self.sigmas[step_index]

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

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

     # 4. Compute previous sample based on the derivatives path
     UpperCAmelCase			:				Any										=					sample + sum(
         coeff * derivative for coeff, derivative in zip(SCREAMING_SNAKE_CASE_,						reversed(self.derivatives		)		)		)

     if not return_dict:
       return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_,						pred_original_sample=SCREAMING_SNAKE_CASE_		)



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

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

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

     UpperCAmelCase			:				Union[str, Any]										=					original_samples + noise * sigma
     return noisy_samples





   def __len__(				self				:	Tuple		):
     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 MobileNetVaImageProcessor



class 							a       (      unittest.TestCase ):





     def __init__(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					List[str]=7     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=3     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=18     ,						SCREAMING_SNAKE_CASE_       :					List[str]=30     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=400     ,						SCREAMING_SNAKE_CASE_       :					Dict=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						SCREAMING_SNAKE_CASE_       :					List[str]=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						):
        __lowerCamelCase:					List[Any]	 =						size if size is not None else {"""shortest_edge""": 20}
        __lowerCamelCase:					Tuple	 =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        __lowerCamelCase:					List[Any]	 =						parent
        __lowerCamelCase:					Optional[int]	 =						batch_size
        __lowerCamelCase:					str	 =						num_channels
        __lowerCamelCase:					Dict	 =						image_size
        __lowerCamelCase:					Tuple	 =						min_resolution
        __lowerCamelCase:					Union[str, Any]	 =						max_resolution
        __lowerCamelCase:					Tuple	 =						do_resize
        __lowerCamelCase:					List[str]	 =						size
        __lowerCamelCase:					int	 =						do_center_crop
        __lowerCamelCase:					Tuple	 =						crop_size







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class 							a       (      _UpperCAmelCase  ,unittest.TestCase ):
     UpperCAmelCase__			:	int       =						MobileNetVaImageProcessor if is_vision_available() else None





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					Tuple	 =						MobileNetVaImageProcessingTester(self				)





     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        return self.image_processor_tester.prepare_image_processor_dict()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					List[Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_resize"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""size"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_center_crop"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""center_crop"""				)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					str	 =						self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 20}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 18, """width""": 18}				)

        __lowerCamelCase:					Optional[int]	 =						self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 42}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 84, """width""": 84}				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        pass





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					List[str]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        __lowerCamelCase:					str	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						Image.Image				)

        # 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:					Tuple	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						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 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					Union[str, Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        __lowerCamelCase:					Tuple	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						numpify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						np.ndarray				)

        # Test not batched input
        __lowerCamelCase:					List[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:					Optional[Any]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						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 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					str	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        __lowerCamelCase:					List[str]	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						torchify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						torch.Tensor				)

        # 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:					Optional[int]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						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"""],
            )     ,						)


