





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   _UpperCAmelCase	:
					__A          =   42

					# setable values
					__A          =   42
					__A          =   42
					__A          =   42
					__A          =   None

					# running values
					__A          =   None




					@classmethod
					def 	snake_case_		(						cls    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  List[Any]:
							return cls(common=__UpperCamelCase    ,      init_noise_sigma=__UpperCamelCase    ,      timesteps=__UpperCamelCase    ,      sigmas=__UpperCamelCase       )



@dataclass
class   _UpperCAmelCase	(   UpperCAmelCase_					):
					__A          =   42



class   _UpperCAmelCase	(   UpperCAmelCase_ ,       UpperCAmelCase_					):
					__A          =   [e.name for e in FlaxKarrasDiffusionSchedulers]

					__A          =   42




					@property
					def 	snake_case_		(						self       )						->  Optional[Any]:
							return True




					@register_to_config
					def __init__(						self    ,      __lowerCAmelCase = 1_000    ,      __lowerCAmelCase = 0.0_0_0_1    ,      __lowerCAmelCase = 0.0_2    ,      __lowerCAmelCase = "linear"    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = "epsilon"    ,      __lowerCAmelCase = jnp.floataa    ,      )						->  Optional[Any]:
							UpperCamelCase__         =	dtype




					def 	snake_case_		(						self    ,      __lowerCAmelCase = None       )						->  List[str]:
							if common is None:
									UpperCamelCase__         =	CommonSchedulerState.create(self       )

							UpperCamelCase__         =	jnp.arange(0    ,      self.config.num_train_timesteps       ).round()[::-1]
							UpperCamelCase__         =	((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

							# standard deviation of the initial noise distribution
							UpperCamelCase__         =	sigmas.max()

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




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Dict:
							((UpperCamelCase__					)  ,								)         =	jnp.where(state.timesteps == timestep    ,      size=1       )
							UpperCamelCase__         =	step_index[0]

							UpperCamelCase__         =	state.sigmas[step_index]
							UpperCamelCase__         =	sample / ((sigma**2 + 1) ** 0.5)
							return sample




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Union[str, Any]:

							def lms_derivative(__lowerCAmelCase       ):
									UpperCamelCase__         =	1.0
									for k in range(__UpperCamelCase       ):
											if current_order == k:
													continue
											prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
									return prod

							UpperCamelCase__         =	integrate.quad(__UpperCamelCase    ,      state.sigmas[t]    ,      state.sigmas[t + 1]    ,      epsrel=1E-4       )[0]

							return integrated_coeff




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = ()       )						->  Dict:
							UpperCamelCase__         =	jnp.linspace(self.config.num_train_timesteps - 1    ,      0    ,      __UpperCamelCase    ,      dtype=self.dtype       )

							UpperCamelCase__         =	jnp.floor(__UpperCamelCase       ).astype(jnp.intaa       )
							UpperCamelCase__         =	jnp.ceil(__UpperCamelCase       ).astype(jnp.intaa       )

							UpperCamelCase__         =	jnp.mod(__UpperCamelCase    ,      1.0       )

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

							UpperCamelCase__         =	timesteps.astype(jnp.intaa       )

							# initial running values
							UpperCamelCase__         =	jnp.zeros((0,) + shape    ,      dtype=self.dtype       )

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




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = 4    ,      __lowerCAmelCase = True    ,      )						->  List[Any]:
							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__         =	state.sigmas[timestep]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
									UpperCamelCase__         =	sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									UpperCamelCase__         =	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__         =	(sample - pred_original_sample) / sigma
							UpperCamelCase__         =	state.replace(derivatives=jnp.append(state.derivatives    ,      __UpperCamelCase       )       )
							if len(state.derivatives       ) > order:
									UpperCamelCase__         =	state.replace(derivatives=jnp.delete(state.derivatives    ,      0       )       )

							# 3. Compute linear multistep coefficients
							UpperCamelCase__         =	min(timestep + 1    ,      __UpperCamelCase       )
							UpperCamelCase__         =	[self.get_lms_coefficient(__UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase       ) for curr_order in range(__UpperCamelCase       )]

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

							if not return_dict:
									return (prev_sample, state)

							return FlaxLMSSchedulerOutput(prev_sample=__UpperCamelCase    ,      state=__UpperCamelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      )						->  str:
							UpperCamelCase__         =	state.sigmas[timesteps].flatten()
							UpperCamelCase__         =	broadcast_to_shape_from_left(__UpperCamelCase    ,      noise.shape       )

							UpperCamelCase__         =	original_samples + noise * sigma

							return noisy_samples




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












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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowerCAmelCase     =			logging.get_logger(__name__)


if is_vision_available():
				import PIL




class   _UpperCAmelCase	(   _lowercase					):
					__A          =   ["pixel_values"]




					def __init__(						self    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = PILImageResampling.BILINEAR    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = 1 / 255    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  None:
							super().__init__(**__lowerCAmelCase       )

							UpperCamelCase__         =	size if size is not None else {"""height""": 2_560, """width""": 1_920}
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# The previous feature extractor size parameter was in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )

							UpperCamelCase__         =	do_resize
							UpperCamelCase__         =	size
							UpperCamelCase__         =	resample
							UpperCamelCase__         =	do_thumbnail
							UpperCamelCase__         =	do_align_long_axis
							UpperCamelCase__         =	do_pad
							UpperCamelCase__         =	do_rescale
							UpperCamelCase__         =	rescale_factor
							UpperCamelCase__         =	do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__         =	image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None       )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
									UpperCamelCase__         =	np.rotaa(__lowerCAmelCase    ,      3       )

							if data_format is not None:
									UpperCamelCase__         =	to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       )

							return image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )

							UpperCamelCase__         =	output_width - input_width
							UpperCamelCase__         =	output_height - input_height

							if random_padding:
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_height + 1       )
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_width + 1       )
							else:
									UpperCamelCase__         =	delta_height // 2
									UpperCamelCase__         =	delta_width // 2

							UpperCamelCase__         =	delta_height - pad_top
							UpperCamelCase__         =	delta_width - pad_left

							UpperCamelCase__         =	((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(__lowerCAmelCase    ,      __lowerCAmelCase    ,      data_format=__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      *__lowerCAmelCase    ,      **__lowerCAmelCase       )						->  List[Any]:
							logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""       )
							return self.pad_image(*__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							# We always resize to the smallest of either the input or output size.
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )

							if height == input_height and width == input_width:
									return image

							if input_height > input_width:
									UpperCamelCase__         =	int(input_width * height / input_height       )
							elif input_width > input_height:
									UpperCamelCase__         =	int(input_height * width / input_width       )

							return resize(
							    __lowerCAmelCase    ,      size=(height, width)    ,      resample=__lowerCAmelCase    ,      reducing_gap=2.0    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	min(size["""height"""]    ,      size["""width"""]       )
							UpperCamelCase__         =	get_resize_output_image_size(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      default_to_square=__lowerCAmelCase       )
							UpperCamelCase__         =	resize(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      resample=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )
							return resized_image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  List[str]:
							return rescale(__lowerCAmelCase    ,      scale=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




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




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = ChannelDimension.FIRST    ,      **__lowerCAmelCase    ,      )						->  PIL.Image.Image:
							UpperCamelCase__         =	do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__         =	size if size is not None else self.size
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# Previous feature extractor had size in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	resample if resample is not None else self.resample
							UpperCamelCase__         =	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							UpperCamelCase__         =	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							UpperCamelCase__         =	do_pad if do_pad is not None else self.do_pad
							UpperCamelCase__         =	do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__         =	rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__         =	do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__         =	image_std if image_std is not None else self.image_std

							UpperCamelCase__         =	make_list_of_images(__lowerCAmelCase       )

							if not valid_images(__lowerCAmelCase       ):
									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."""       )

							if do_pad and size is None:
									raise ValueError("""Size must be specified if do_pad 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."""       )

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

							if do_align_long_axis:
									UpperCamelCase__         =	[self.align_long_axis(__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

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

							if do_thumbnail:
									UpperCamelCase__         =	[self.thumbnail(image=__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

							if do_pad:
									UpperCamelCase__         =	[self.pad_image(image=__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      random_padding=__lowerCAmelCase       ) for image in images]

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

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

							UpperCamelCase__         =	[to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       ) for image in images]

							UpperCamelCase__         =	{"""pixel_values""": images}
							return BatchFeature(data=__lowerCAmelCase    ,      tensor_type=__lowerCAmelCase       )





