



'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase__:   Optional[Any]								=			logging.get_logger(__name__)


if is_vision_available():
						import PIL

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PILImageResampling.BICUBIC		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     bool = True		,			__snake_case			:     Union[int, float] = 1 / 255		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     bool = True		,			**__snake_case			:     List[str]		,			)			->		None:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       Any 							=		size if size is not None else {'''shortest_edge''': 224}
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		,			param_name='''crop_size'''		)

								UpperCAmelCase :       Any 							=		do_resize
								UpperCAmelCase :       List[Any] 							=		size
								UpperCAmelCase :       str 							=		resample
								UpperCAmelCase :       Tuple 							=		do_center_crop
								UpperCAmelCase :       Any 							=		crop_size
								UpperCAmelCase :       str 							=		do_rescale
								UpperCAmelCase :       int 							=		rescale_factor
								UpperCAmelCase :       Union[str, Any] 							=		do_normalize
								UpperCAmelCase :       Optional[Any] 							=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCAmelCase :       Dict 							=		image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCAmelCase :       List[Any] 							=		do_convert_rgb







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PILImageResampling.BICUBIC		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     List[Any]		,			)			->		np.ndarray:
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								if "shortest_edge" not in size:
															raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
								UpperCAmelCase :       Union[str, Any] 							=		get_resize_output_image_size(__snake_case		,			size=size['''shortest_edge''']		,			default_to_square=__snake_case		)
								return resize(__snake_case		,			size=__snake_case		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Tuple		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Union[str, Any]		,			)			->		np.ndarray:
								UpperCAmelCase :       Tuple 							=		get_size_dict(__snake_case		)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
								return center_crop(__snake_case		,			size=(size['''height'''], size['''width'''])		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[int, float]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[Any]		,			)			->		List[str]:
								return rescale(__snake_case		,			scale=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[float, List[float]]		,			__snake_case			:     Union[float, List[float]]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Dict		,			)			->		np.ndarray:
								return normalize(__snake_case		,			mean=__snake_case		,			std=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[str]		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = None		,			__snake_case			:     bool = None		,			__snake_case			:     int = None		,			__snake_case			:     bool = None		,			__snake_case			:     float = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[Union[str, TensorType]] = None		,			__snake_case			:     Optional[ChannelDimension] = ChannelDimension.FIRST		,			**__snake_case			:     Optional[int]		,			)			->		PIL.Image.Image:
								UpperCAmelCase :       Any 							=		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase :       str 							=		size if size is not None else self.size
								UpperCAmelCase :       Dict 							=		get_size_dict(__snake_case		,			param_name='''size'''		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[str] 							=		resample if resample is not None else self.resample
								UpperCAmelCase :       Tuple 							=		do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase :       Optional[Any] 							=		crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase :       Any 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase :       Union[str, Any] 							=		rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase :       Tuple 							=		do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase :       Any 							=		image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase :       Union[str, Any] 							=		image_std if image_std is not None else self.image_std
								UpperCAmelCase :       Any 							=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase :       List[str] 							=		make_list_of_images(__snake_case		)

								if not valid_images(__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_center_crop and crop_size is None:
															raise ValueError('''Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.'''		)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
															UpperCAmelCase :       str 							=		[convert_to_rgb(__snake_case		) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase :       Union[str, Any] 							=		[to_numpy_array(__snake_case		) for image in images]

								if do_resize:
															UpperCAmelCase :       int 							=		[self.resize(image=__snake_case		,			size=__snake_case		,			resample=__snake_case		) for image in images]

								if do_center_crop:
															UpperCAmelCase :       Optional[Any] 							=		[self.center_crop(image=__snake_case		,			size=__snake_case		) for image in images]

								if do_rescale:
															UpperCAmelCase :       Optional[int] 							=		[self.rescale(image=__snake_case		,			scale=__snake_case		) for image in images]

								if do_normalize:
															UpperCAmelCase :       int 							=		[self.normalize(image=__snake_case		,			mean=__snake_case		,			std=__snake_case		) for image in images]

								UpperCAmelCase :       List[Any] 							=		[to_channel_dimension_format(__snake_case		,			__snake_case		) for image in images]

								UpperCAmelCase :       Tuple 							=		{'''pixel_values''': images}
								return BatchFeature(data=__snake_case		,			tensor_type=__snake_case		)













"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



