





'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class    __a :
							__a   :				CommonSchedulerState

							# setable values
							__a   :				jnp.ndarray
							__a   :				jnp.ndarray
							__a   :				jnp.ndarray
							__a   :				Optional[int]       =    None

							# running values
							__a   :				Optional[jnp.ndarray]       =    None

							@classmethod
							def       UpperCAmelCase__		(							cls							:       int    ,       __magic_name__							:       CommonSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray				)   ->    List[Any]:







													"""simple docstring"""
													return cls(common=__magic_name__    ,       init_noise_sigma=__magic_name__    ,       timesteps=__magic_name__    ,       sigmas=__magic_name__				)





@dataclass
class    __a (lowerCamelCase       ):
							__a   :				LMSDiscreteSchedulerState





class    __a (lowerCamelCase    ,						lowerCamelCase       ):
							__a   :				Tuple       =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							__a   :				jnp.dtype

							@property
							def       UpperCAmelCase__		(							self							:       Tuple				)   ->    int:







													"""simple docstring"""
													return True

							@register_to_config
							def __init__(							self							:       Tuple    ,       __magic_name__							:       int = 10_00    ,       __magic_name__							:       float = 0.0_0_0_1    ,       __magic_name__							:       float = 0.0_2    ,       __magic_name__							:       str = "linear"    ,       __magic_name__							:       Optional[jnp.ndarray] = None    ,       __magic_name__							:       str = "epsilon"    ,       __magic_name__							:       jnp.dtype = jnp.floataa    ,       )   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					dtype

							def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       Optional[CommonSchedulerState] = None				)   ->    LMSDiscreteSchedulerState:







													"""simple docstring"""
													if common is None:
																			UpperCAmelCase_     : Optional[int]         =					CommonSchedulerState.create(self				)

													UpperCAmelCase_     : Union[str, Any]         =					jnp.arange(0    ,       self.config.num_train_timesteps				).round()[::-1]
													UpperCAmelCase_     : Dict         =					((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													UpperCAmelCase_     : Dict         =					sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=__magic_name__    ,       init_noise_sigma=__magic_name__    ,       timesteps=__magic_name__    ,       sigmas=__magic_name__    ,       )

							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       int				)   ->    jnp.ndarray:







													"""simple docstring"""
													((UpperCAmelCase_)  ,    )     : Dict         =					jnp.where(state.timesteps == timestep    ,       size=1				)
													UpperCAmelCase_     : List[Any]         =					step_index[0]

													UpperCAmelCase_     : Optional[int]         =					state.sigmas[step_index]
													UpperCAmelCase_     : Dict         =					sample / ((sigma**2 + 1) ** 0.5)
													return sample

							def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       Optional[int]    ,       __magic_name__							:       Tuple    ,       __magic_name__							:       int				)   ->    Dict:







													"""simple docstring"""

													def lms_derivative(__magic_name__							:       int				):
																			UpperCAmelCase_     : Union[str, Any]         =					1.0
																			for k in range(__magic_name__				):
																									if current_order == k:
																															continue
																									prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																			return prod

													UpperCAmelCase_     : Optional[Any]         =					integrate.quad(__magic_name__    ,       state.sigmas[t]    ,       state.sigmas[t + 1]    ,       epsrel=1E-4				)[0]

													return integrated_coeff

							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       int    ,       __magic_name__							:       Tuple = ()				)   ->    LMSDiscreteSchedulerState:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					jnp.linspace(self.config.num_train_timesteps - 1    ,       0    ,       __magic_name__    ,       dtype=self.dtype				)

													UpperCAmelCase_     : Dict         =					jnp.floor(__magic_name__				).astype(jnp.intaa				)
													UpperCAmelCase_     : List[str]         =					jnp.ceil(__magic_name__				).astype(jnp.intaa				)

													UpperCAmelCase_     : List[Any]         =					jnp.mod(__magic_name__    ,       1.0				)

													UpperCAmelCase_     : List[str]         =					((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													UpperCAmelCase_     : str         =					(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													UpperCAmelCase_     : List[str]         =					jnp.concatenate([sigmas, jnp.array([0.0]    ,       dtype=self.dtype				)]				)

													UpperCAmelCase_     : Optional[Any]         =					timesteps.astype(jnp.intaa				)

													# initial running values
													UpperCAmelCase_     : List[Any]         =					jnp.zeros((0,) + shape    ,       dtype=self.dtype				)

													return state.replace(
													    timesteps=__magic_name__    ,       sigmas=__magic_name__    ,       num_inference_steps=__magic_name__    ,       derivatives=__magic_name__    ,       )

							def       UpperCAmelCase__		(							self							:       Any    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       int    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       int = 4    ,       __magic_name__							:       bool = True    ,       )   ->    Union[FlaxLMSSchedulerOutput, Tuple]:







													"""simple docstring"""
													if state.num_inference_steps is None:
																			raise ValueError(
																			    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''				)

													UpperCAmelCase_     : Optional[int]         =					state.sigmas[timestep]

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

													# 3. Compute linear multistep coefficients
													UpperCAmelCase_     : Any         =					min(timestep + 1    ,       __magic_name__				)
													UpperCAmelCase_     : List[Any]         =					[self.get_lms_coefficient(__magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__				) for curr_order in range(__magic_name__				)]

													# 4. Compute previous sample based on the derivatives path
													UpperCAmelCase_     : Dict         =					sample + sum(
													    coeff * derivative for coeff, derivative in zip(__magic_name__    ,       reversed(state.derivatives				)				)				)

													if not return_dict:
																			return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=__magic_name__    ,       state=__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       LMSDiscreteSchedulerState    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray    ,       __magic_name__							:       jnp.ndarray    ,       )   ->    jnp.ndarray:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					state.sigmas[timesteps].flatten()
													UpperCAmelCase_     : Dict         =					broadcast_to_shape_from_left(__magic_name__    ,       noise.shape				)

													UpperCAmelCase_     : List[Any]         =					original_samples + noise * sigma

													return noisy_samples



							def __len__(							self							:       str				)   ->    int:







													"""simple docstring"""
													return self.config.num_train_timesteps












'''simple docstring'''







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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


snake_case_       :    int          =					logging.get_logger(__name__)


class    __a (lowerCamelCase       ):
							__a   :				Optional[Any]       =    ["pixel_values"]

							def __init__(							self							:       str    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Dict[str, int] = None    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Dict[str, int] = None    ,       __magic_name__							:       PILImageResampling = PILImageResampling.BICUBIC    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Union[int, float] = 1 / 2_55    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       **__magic_name__							:       List[Any]    ,       )   ->    None:







													"""simple docstring"""
													super().__init__(**__magic_name__				)
													UpperCAmelCase_     : Tuple         =					crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)
													UpperCAmelCase_     : List[Any]         =					size if size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_     : int         =					get_size_dict(__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					do_center_crop
													UpperCAmelCase_     : List[str]         =					crop_size
													UpperCAmelCase_     : Dict         =					do_resize
													UpperCAmelCase_     : str         =					size
													UpperCAmelCase_     : int         =					resample
													UpperCAmelCase_     : Optional[int]         =					do_rescale
													UpperCAmelCase_     : List[str]         =					rescale_factor
													UpperCAmelCase_     : int         =					do_normalize
													UpperCAmelCase_     : Any         =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													UpperCAmelCase_     : str         =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Dict[str, int]    ,       __magic_name__							:       Optional[int] = None    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       Union[str, Any]    ,       )   ->    np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					self.size if size is None else size
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__				)
													UpperCAmelCase_     : Union[str, Any]         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)

													UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					get_image_size(__magic_name__				)
													UpperCAmelCase_     : List[str]         =					min(__magic_name__    ,       __magic_name__				)
													UpperCAmelCase_     : Dict         =					(size['''height'''] / crop_size['''height''']) * min_dim
													UpperCAmelCase_     : List[str]         =					(size['''width'''] / crop_size['''width''']) * min_dim
													return center_crop(__magic_name__    ,       size=(cropped_height, cropped_width)    ,       data_format=__magic_name__    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Dict[str, int]    ,       __magic_name__							:       PILImageResampling = PIL.Image.BICUBIC    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       str    ,       )   ->    np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__				)
													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(
													    __magic_name__    ,       size=(size['''height'''], size['''width'''])    ,       resample=__magic_name__    ,       data_format=__magic_name__    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Union[int, float]    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       Tuple    ,       )   ->    str:







													"""simple docstring"""
													return rescale(__magic_name__    ,       scale=__magic_name__    ,       data_format=__magic_name__    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Union[float, List[float]]    ,       __magic_name__							:       Union[float, List[float]]    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       Dict    ,       )   ->    np.ndarray:







													"""simple docstring"""
													return normalize(__magic_name__    ,       mean=__magic_name__    ,       std=__magic_name__    ,       data_format=__magic_name__    ,       **__magic_name__				)



							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       ImageInput    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[Dict[str, int]] = None    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[Dict[str, int]] = None    ,       __magic_name__							:       PILImageResampling = None    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[float] = None    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       __magic_name__							:       Optional[Union[str, TensorType]] = None    ,       __magic_name__							:       ChannelDimension = ChannelDimension.FIRST    ,       **__magic_name__							:       Optional[Any]    ,       )   ->    PIL.Image.Image:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_     : str         =					crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_     : int         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)
													UpperCAmelCase_     : Dict         =					do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_     : Optional[Any]         =					size if size is not None else self.size
													UpperCAmelCase_     : Tuple         =					get_size_dict(__magic_name__				)
													UpperCAmelCase_     : Any         =					resample if resample is not None else self.resample
													UpperCAmelCase_     : int         =					do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_     : str         =					rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_     : Optional[Any]         =					do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_     : Tuple         =					image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_     : Tuple         =					image_std if image_std is not None else self.image_std

													UpperCAmelCase_     : Optional[Any]         =					make_list_of_images(__magic_name__				)

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

													if do_center_crop and crop_size is None:
																			raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''				)

													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.'''				)

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''				)

													# All transformations expect numpy arrays.
													UpperCAmelCase_     : Tuple         =					[to_numpy_array(__magic_name__				) for image in images]

													if do_center_crop:
																			UpperCAmelCase_     : int         =					[self.center_crop(__magic_name__    ,       __magic_name__    ,       size=__magic_name__				) for image in images]

													if do_resize:
																			UpperCAmelCase_     : Dict         =					[self.resize(image=__magic_name__    ,       size=__magic_name__    ,       resample=__magic_name__				) for image in images]

													if do_rescale:
																			UpperCAmelCase_     : Union[str, Any]         =					[self.rescale(image=__magic_name__    ,       scale=__magic_name__				) for image in images]

													if do_normalize:
																			UpperCAmelCase_     : Any         =					[self.normalize(image=__magic_name__    ,       mean=__magic_name__    ,       std=__magic_name__				) for image in images]

													UpperCAmelCase_     : Optional[int]         =					[to_channel_dimension_format(__magic_name__    ,       __magic_name__				) for image in images]

													UpperCAmelCase_     : Union[str, Any]         =					{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__    ,       tensor_type=__magic_name__				)





