from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__    :       Any						=			logging.get_logger(__name__)




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Tuple     =       ["""pixel_values"""]


	def __init__(							self	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 1 / 255	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->None:
					super().__init__(**__SCREAMING_SNAKE_CASE       )
					lowerCAmelCase				=	size if size is not None else {'''height''': 224, '''width''': 224}
					lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )
					lowerCAmelCase				=	do_resize
					lowerCAmelCase				=	do_rescale
					lowerCAmelCase				=	do_normalize
					lowerCAmelCase				=	size
					lowerCAmelCase				=	resample
					lowerCAmelCase				=	rescale_factor
					lowerCAmelCase				=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowerCAmelCase				=	image_std if image_std is not None else IMAGENET_STANDARD_STD


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
					lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )
					if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"       )
					return resize(
					    __SCREAMING_SNAKE_CASE	,    size=(size['''height'''], size['''width'''])	,    resample=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE       )		->np.ndarray:
					return rescale(__SCREAMING_SNAKE_CASE	,    scale=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
					return normalize(__SCREAMING_SNAKE_CASE	,    mean=__SCREAMING_SNAKE_CASE	,    std=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,    **__SCREAMING_SNAKE_CASE	,    )		->Any:
					lowerCAmelCase				=	do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase				=	do_rescale if do_rescale is not None else self.do_rescale
					lowerCAmelCase				=	do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase				=	resample if resample is not None else self.resample
					lowerCAmelCase				=	rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCAmelCase				=	image_mean if image_mean is not None else self.image_mean
					lowerCAmelCase				=	image_std if image_std is not None else self.image_std

					lowerCAmelCase				=	size if size is not None else self.size
					lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )

					lowerCAmelCase				=	make_list_of_images(__SCREAMING_SNAKE_CASE       )

					if not valid_images(__SCREAMING_SNAKE_CASE       ):
									raise ValueError(
									    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
									    '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

					if do_resize and size is None:
									raise ValueError('''Size must be specified if do_resize is True.'''       )

					if do_rescale and rescale_factor is None:
									raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

					# All transformations expect numpy arrays.
					lowerCAmelCase				=	[to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

					if do_resize:
									lowerCAmelCase				=	[self.resize(image=__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE	,    resample=__SCREAMING_SNAKE_CASE       ) for image in images]

					if do_rescale:
									lowerCAmelCase				=	[self.rescale(image=__SCREAMING_SNAKE_CASE	,    scale=__SCREAMING_SNAKE_CASE       ) for image in images]

					if do_normalize:
									lowerCAmelCase				=	[self.normalize(image=__SCREAMING_SNAKE_CASE	,    mean=__SCREAMING_SNAKE_CASE	,    std=__SCREAMING_SNAKE_CASE       ) for image in images]

					lowerCAmelCase				=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) for image in images]

					lowerCAmelCase				=	{'''pixel_values''': images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,    tensor_type=__SCREAMING_SNAKE_CASE       )




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 SchedulerMixin


lowercase__    :       List[Any]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      torch.FloatTensor




class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Tuple     =       1


	@register_to_config
	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 40	,    __SCREAMING_SNAKE_CASE = 0.0_0_2	,    __SCREAMING_SNAKE_CASE = 8_0.0	,    __SCREAMING_SNAKE_CASE = 0.5	,    __SCREAMING_SNAKE_CASE = 1.0	,    __SCREAMING_SNAKE_CASE = 7.0	,    __SCREAMING_SNAKE_CASE = True	,    )		->Tuple:
								# standard deviation of the initial noise distribution
								lowerCAmelCase				=	sigma_max

								lowerCAmelCase				=	np.linspace(0	,    1	,    __SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								# setable values
								lowerCAmelCase				=	None
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	False
								lowerCAmelCase				=	False


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								if schedule_timesteps is None:
															lowerCAmelCase				=	self.timesteps

								lowerCAmelCase				=	(schedule_timesteps == timestep).nonzero()
								return indices.item()


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->torch.FloatTensor:
								# Get sigma corresponding to timestep
								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )
								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigmas[step_idx]

								lowerCAmelCase				=	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								lowerCAmelCase				=	True
								return sample


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								if not isinstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       ):
															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.floataa       )

								lowerCAmelCase				=	1000 * 0.2_5 * np.log(sigmas + 1e-44       )

								return timesteps


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    )		->str:
								if num_inference_steps is None and timesteps is None:
															raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )

								if num_inference_steps is not None and timesteps is not None:
															raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''       )

								# Follow DDPMScheduler custom timesteps logic
								if timesteps is not None:
															for i in range(1	,    len(__SCREAMING_SNAKE_CASE       )       ):
																						if timesteps[i] >= timesteps[i - 1]:
																													raise ValueError('''`timesteps` must be in descending order.'''       )

															if timesteps[0] >= self.config.num_train_timesteps:
																						raise ValueError(
																						    F"`timesteps` must start before `self.config.train_timesteps`:"
																						    F" {self.config.num_train_timesteps}."       )

															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.intaa       )
															lowerCAmelCase				=	True
								else:
															if num_inference_steps > self.config.num_train_timesteps:
																						raise ValueError(
																						    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																						    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																						    F" maximal {self.config.num_train_timesteps} timesteps."       )

															lowerCAmelCase				=	num_inference_steps

															lowerCAmelCase				=	self.config.num_train_timesteps // self.num_inference_steps
															lowerCAmelCase				=	(np.arange(0	,    __SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
															lowerCAmelCase				=	False

								# Map timesteps to Karras sigmas directly for multistep sampling
								# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
								lowerCAmelCase				=	self.config.num_train_timesteps
								lowerCAmelCase				=	timesteps[::-1].copy()
								lowerCAmelCase				=	ramp / (num_train_timesteps - 1)
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								lowerCAmelCase				=	np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )

								if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
															# mps does not support float64
															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE	,    dtype=torch.floataa       )
								else:
															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.config.rho
								lowerCAmelCase				=	sigma_min ** (1 / rho)
								lowerCAmelCase				=	sigma_max ** (1 / rho)
								lowerCAmelCase				=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / (sigma**2 + sigma_data**2)
								lowerCAmelCase				=	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
								lowerCAmelCase				=	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    )		->Union[CMStochasticIterativeSchedulerOutput, Tuple]:

								if (
								    isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.IntTensor       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.LongTensor       )
								):
															raise ValueError(
															    (
															        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
															        F" `{self.__class__}.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(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )

								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

								# sigma_next corresponds to next_t in original implementation
								lowerCAmelCase				=	self.sigmas[step_index]
								if step_index + 1 < self.config.num_train_timesteps:
															lowerCAmelCase				=	self.sigmas[step_index + 1]
								else:
															# Set sigma_next to sigma_min
															lowerCAmelCase				=	self.sigmas[-1]

								# Get scalings for boundary conditions
								lowerCAmelCase	,   lowerCAmelCase				=	self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE       )

								# 1. Denoise model output using boundary conditions
								lowerCAmelCase				=	c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
															lowerCAmelCase				=	denoised.clamp(-1	,    1       )

								# 2. Sample z ~ N(0, s_noise^2 * I)
								# Noise is not used for onestep sampling.
								if len(self.timesteps       ) > 1:
															lowerCAmelCase				=	randn_tensor(
															    model_output.shape	,    dtype=model_output.dtype	,    device=model_output.device	,    generator=__SCREAMING_SNAKE_CASE       )
								else:
															lowerCAmelCase				=	torch.zeros_like(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	noise * self.config.s_noise

								lowerCAmelCase				=	sigma_next.clamp(min=__SCREAMING_SNAKE_CASE	,    max=__SCREAMING_SNAKE_CASE       )

								# 3. Return noisy sample
								# tau = sigma_hat, eps = sigma_min
								lowerCAmelCase				=	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

								if not return_dict:
															return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )


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

								lowerCAmelCase				=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

								lowerCAmelCase				=	original_samples + noise * sigma
								return noisy_samples




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



