





'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Union[str, Any]=0.999					,       __lowerCamelCase :    Dict="cosine"					,       )						->Tuple:
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    _SCREAMING_SNAKE_CASE									=			[]
    for i in range(__lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			i / num_diffusion_timesteps
        _SCREAMING_SNAKE_CASE									=			(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_     (				snake_case_      ,    snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = [e.name for e in KarrasDiffusionSchedulers]
    UpperCamelCase     = 2


    @register_to_config
    def __init__(		self ,		A = 1000 ,		A = 0.0_0085 ,		A = 0.012 ,		A = "linear" ,		A = None ,		A = "epsilon" ,		A = "linspace" ,		A = 0 ,		)		->   Dict:
        if trained_betas is not None:
            _SCREAMING_SNAKE_CASE									=			torch.tensor(A ,		dtype=torch.floataa       )
        elif beta_schedule == "linear":
            _SCREAMING_SNAKE_CASE									=			torch.linspace(A ,		A ,		A ,		dtype=torch.floataa       )
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            _SCREAMING_SNAKE_CASE									=			(
                torch.linspace(beta_start**0.5 ,		beta_end**0.5 ,		A ,		dtype=torch.floataa       ) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            _SCREAMING_SNAKE_CASE									=			betas_for_alpha_bar(A       )
        else:
            raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

        _SCREAMING_SNAKE_CASE									=			1.0 - self.betas
        _SCREAMING_SNAKE_CASE									=			torch.cumprod(self.alphas ,		dim=0       )

        #  set all values
        self.set_timesteps(A ,		A ,		A       )


    def 			snake_case_(		self ,		A ,		A=None       )		->   Union[str, Any]:
        if schedule_timesteps is None:
            _SCREAMING_SNAKE_CASE									=			self.timesteps

        _SCREAMING_SNAKE_CASE									=			(schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter       ) == 0:
            _SCREAMING_SNAKE_CASE									=			1 if len(A       ) > 1 else 0
        else:
            _SCREAMING_SNAKE_CASE									=			timestep.cpu().item() if torch.is_tensor(A       ) else timestep
            _SCREAMING_SNAKE_CASE									=			self._index_counter[timestep_int]

        return indices[pos].item()


    @property
    def 			snake_case_(		self       )		->   Tuple:
        # 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 			snake_case_(		self ,		A ,		A ,		)		->   torch.FloatTensor:
        _SCREAMING_SNAKE_CASE									=			self.index_for_timestep(A       )

        if self.state_in_first_order:
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]
        else:
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index]

        _SCREAMING_SNAKE_CASE									=			sample / ((sigma**2 + 1) ** 0.5)
        return sample


    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		)		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			num_inference_steps

        _SCREAMING_SNAKE_CASE									=			num_train_timesteps or self.config.num_train_timesteps

        # "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									=			np.linspace(0 ,		num_train_timesteps - 1 ,		A ,		dtype=A       )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
            _SCREAMING_SNAKE_CASE									=			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									=			(np.arange(0 ,		A       ) * step_ratio).round()[::-1].copy().astype(A       )
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            _SCREAMING_SNAKE_CASE									=			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									=			(np.arange(A ,		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\'.'       )

        _SCREAMING_SNAKE_CASE									=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
        _SCREAMING_SNAKE_CASE									=			torch.from_numpy(np.log(A       )       ).to(A       )

        _SCREAMING_SNAKE_CASE									=			np.interp(A ,		np.arange(0 ,		len(A       )       ) ,		A       )
        _SCREAMING_SNAKE_CASE									=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
        _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(device=A       )

        # interpolate sigmas
        _SCREAMING_SNAKE_CASE									=			sigmas.log().lerp(sigmas.roll(1       ).log() ,		0.5       ).exp()

        _SCREAMING_SNAKE_CASE									=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
        _SCREAMING_SNAKE_CASE									=			torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

        if str(A       ).startswith("""mps"""       ):
            # mps does not support float64
            _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(A ,		dtype=torch.floataa       )
        else:
            _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(A       )

        # interpolate timesteps
        _SCREAMING_SNAKE_CASE									=			self.sigma_to_t(A       ).to(A ,		dtype=timesteps.dtype       )
        _SCREAMING_SNAKE_CASE									=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,		dim=-1       ).flatten()

        _SCREAMING_SNAKE_CASE									=			torch.cat([timesteps[:1], interleaved_timesteps]       )

        _SCREAMING_SNAKE_CASE									=			None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        _SCREAMING_SNAKE_CASE									=			defaultdict(A       )


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        # get log sigma
        _SCREAMING_SNAKE_CASE									=			sigma.log()

        # get distribution
        _SCREAMING_SNAKE_CASE									=			log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        _SCREAMING_SNAKE_CASE									=			dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
        _SCREAMING_SNAKE_CASE									=			low_idx + 1

        _SCREAMING_SNAKE_CASE									=			self.log_sigmas[low_idx]
        _SCREAMING_SNAKE_CASE									=			self.log_sigmas[high_idx]

        # interpolate sigmas
        _SCREAMING_SNAKE_CASE									=			(low - log_sigma) / (low - high)
        _SCREAMING_SNAKE_CASE									=			w.clamp(0 ,		1       )

        # transform interpolation to time range
        _SCREAMING_SNAKE_CASE									=			(1 - w) * low_idx + w * high_idx
        _SCREAMING_SNAKE_CASE									=			t.view(sigma.shape       )
        return t


    @property
    def 			snake_case_(		self       )		->   int:
        return self.sample is None


    def 			snake_case_(		self ,		A ,		A ,		A ,		A = True ,		)		->   Union[SchedulerOutput, Tuple]:
        _SCREAMING_SNAKE_CASE									=			self.index_for_timestep(A       )

        # advance index counter by 1
        _SCREAMING_SNAKE_CASE									=			timestep.cpu().item() if torch.is_tensor(A       ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index + 1]
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index + 1]
        else:
            # 2nd order / KDPM2's method
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index - 1]
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index]
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        _SCREAMING_SNAKE_CASE									=			0
        _SCREAMING_SNAKE_CASE									=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            _SCREAMING_SNAKE_CASE									=			sigma_hat if self.state_in_first_order else sigma_interpol
            _SCREAMING_SNAKE_CASE									=			sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
            _SCREAMING_SNAKE_CASE									=			sigma_hat if self.state_in_first_order else sigma_interpol
            _SCREAMING_SNAKE_CASE									=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
        elif self.config.prediction_type == "sample":
            raise NotImplementedError("""prediction_type not implemented yet: sample"""       )
        else:
            raise ValueError(
                f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'       )

        if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            _SCREAMING_SNAKE_CASE									=			(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            _SCREAMING_SNAKE_CASE									=			sigma_interpol - sigma_hat

            # store for 2nd order step
            _SCREAMING_SNAKE_CASE									=			sample
        else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            _SCREAMING_SNAKE_CASE									=			(sample - pred_original_sample) / sigma_interpol

            # 3. delta timestep
            _SCREAMING_SNAKE_CASE									=			sigma_next - sigma_hat

            _SCREAMING_SNAKE_CASE									=			self.sample
            _SCREAMING_SNAKE_CASE									=			None

        _SCREAMING_SNAKE_CASE									=			sample + derivative * dt

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=A       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		)		->   torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			self.timesteps.to(original_samples.device ,		dtype=torch.floataa       )
            _SCREAMING_SNAKE_CASE									=			timesteps.to(original_samples.device ,		dtype=torch.floataa       )
        else:
            _SCREAMING_SNAKE_CASE									=			self.timesteps.to(original_samples.device       )
            _SCREAMING_SNAKE_CASE									=			timesteps.to(original_samples.device       )

        _SCREAMING_SNAKE_CASE									=			[self.index_for_timestep(A ,		A       ) for t in timesteps]

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

        _SCREAMING_SNAKE_CASE									=			original_samples + noise * sigma
        return noisy_samples





    def __len__(		self       )		->   str:
        return self.config.num_train_timesteps





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case								=  logging.get_logger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   List[str]				       =["""pixel_values"""]
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   int = 32       ,   UpperCAmelCase_							:   int=PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor
						SCREAMING_SNAKE_CASE__													= resample
						super().__init__(**UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= get_image_size(UpperCAmelCase_							)
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE__													= height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= resize(UpperCAmelCase_       ,   (new_h, new_w)       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						return image
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   float       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   List[str]							):
						return rescale(image=UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)





	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   List[str]=None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[Union[TensorType, str]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											raise ValueError('Invalid image(s)'							)

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for img in images]

						if do_resize:
											SCREAMING_SNAKE_CASE__													= [self.resize(UpperCAmelCase_       ,   size_divisor=UpperCAmelCase_       ,   resample=UpperCAmelCase_							) for image in images]

						if do_rescale:
											SCREAMING_SNAKE_CASE__													= [self.rescale(UpperCAmelCase_       ,   scale=1 / 255							) for image in images]

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {'pixel_values': images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





