



import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							CONFIG_NAME




	@register_to_config
	def __init__(self	, lowerCamelCase_ = True	, lowerCamelCase_ = 8	, lowerCamelCase_ = "lanczos"	, lowerCamelCase_ = True	, lowerCamelCase_ = False	, ):
						"""simple docstring"""

						super().__init__()




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						if images.ndim == 3:
											a        =     images[None, ...]
						a        =     (images * 255).round().astype("uint8"	)
						if images.shape[-1] == 1:
											# special case for grayscale (single channel) images
											a        =     [Image.fromarray(image.squeeze()	, mode="L"	) for image in images]
						else:
											a        =     [Image.fromarray(lowerCamelCase_	) for image in images]

						return pil_images




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						if not isinstance(lowerCamelCase_	, lowerCamelCase_	):
											a        =     [images]
						a        =     [np.array(lowerCamelCase_	).astype(np.floataa	) / 255.0 for image in images]
						a        =     np.stack(lowerCamelCase_	, axis=0	)

						return images




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						if images.ndim == 3:
											a        =     images[..., None]

						a        =     torch.from_numpy(images.transpose(0	, 3	, 1	, 2	)	)
						return images




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						a        =     images.cpu().permute(0	, 2	, 3	, 1	).float().numpy()
						return images




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						return 2.0 * images - 1.0




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						return (images / 2 + 0.5).clamp(0	, 1	)




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						a        =     image.convert("RGB"	)
						return image




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						if height is None:
											a        =     image.height
						if width is None:
											a        =     image.width

						a  ,       a        =     (
						    x - x % self.config.vae_scale_factor for x in (width, height)
						)  # resize to integer multiple of vae_scale_factor
						a        =     image.resize((width, height)	, resample=PIL_INTERPOLATION[self.config.resample]	)
						return image




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     (PIL.Image.Image, np.ndarray, torch.Tensor)
						if isinstance(lowerCamelCase_	, lowerCamelCase_	):
											a        =     [image]
						elif not (isinstance(lowerCamelCase_	, lowerCamelCase_	) and all(isinstance(lowerCamelCase_	, lowerCamelCase_	) for i in image	)):
											raise ValueError(
											    F'''Input is in incorrect format: {[type(lowerCamelCase_	) for i in image]}. Currently, we only support {', '.join(lowerCamelCase_	)}'''	)

						if isinstance(image[0]	, PIL.Image.Image	):
											if self.config.do_convert_rgb:
																a        =     [self.convert_to_rgb(lowerCamelCase_	) for i in image]
											if self.config.do_resize:
																a        =     [self.resize(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	) for i in image]
											a        =     self.pil_to_numpy(lowerCamelCase_	)  # to np
											a        =     self.numpy_to_pt(lowerCamelCase_	)  # to pt

						elif isinstance(image[0]	, np.ndarray	):
											a        =     np.concatenate(lowerCamelCase_	, axis=0	) if image[0].ndim == 4 else np.stack(lowerCamelCase_	, axis=0	)
											a        =     self.numpy_to_pt(lowerCamelCase_	)
											a  ,       a  ,       a  ,       a        =     image.shape
											if self.config.do_resize and (
											    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
											):
																raise ValueError(
																    F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
																    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''	)

						elif isinstance(image[0]	, torch.Tensor	):
											a        =     torch.cat(lowerCamelCase_	, axis=0	) if image[0].ndim == 4 else torch.stack(lowerCamelCase_	, axis=0	)
											a  ,       a  ,       a  ,       a        =     image.shape

											# don't need any preprocess if the image is latents
											if channel == 4:
																return image

											if self.config.do_resize and (
											    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
											):
																raise ValueError(
																    F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
																    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''	)

        # expected range [0,1], normalize to [-1,1]
						a        =     self.config.do_normalize
						if image.min() < 0:
											warnings.warn(
											    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
											    F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''	, lowerCamelCase_	, )
											a        =     False

						if do_normalize:
											a        =     self.normalize(lowerCamelCase_	)

						return image




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = "pil"	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						if not isinstance(lowerCamelCase_	, torch.Tensor	):
											raise ValueError(
											    F'''Input for postprocessing is in incorrect format: {type(lowerCamelCase_	)}. We only support pytorch tensor'''	)
						if output_type not in ["latent", "pt", "np", "pil"]:
											a        =     (
											    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
											    "`pil`, `np`, `pt`, `latent`"
											)
											deprecate("Unsupported output_type"	, "1.0.0"	, lowerCamelCase_	, standard_warn=lowerCamelCase_	)
											a        =     "np"

						if output_type == "latent":
											return image

						if do_denormalize is None:
											a        =     [self.config.do_normalize] * image.shape[0]

						a        =     torch.stack(
						    [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

						if output_type == "pt":
											return image

						a        =     self.pt_to_numpy(lowerCamelCase_	)

						if output_type == "np":
											return image

						if output_type == "pil":
											return self.numpy_to_pil(lowerCamelCase_	)




class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							CONFIG_NAME




	@register_to_config
	def __init__(self	, lowerCamelCase_ = True	, lowerCamelCase_ = 8	, lowerCamelCase_ = "lanczos"	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						super().__init__()




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						if images.ndim == 3:
											a        =     images[None, ...]
						a        =     (images * 255).round().astype("uint8"	)
						if images.shape[-1] == 1:
											# special case for grayscale (single channel) images
											a        =     [Image.fromarray(image.squeeze()	, mode="L"	) for image in images]
						else:
											a        =     [Image.fromarray(image[:, :, :3]	) for image in images]

						return pil_images




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	):
						"""simple docstring"""

						return image[:, :, 1] * 2**8 + image[:, :, 2]




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						if images.ndim == 3:
											a        =     images[None, ...]
						a        =     images[:, :, :, 3:]
						if images.shape[-1] == 6:
											a        =     (images_depth * 255).round().astype("uint8"	)
											a        =     [
											    Image.fromarray(self.rgblike_to_depthmap(lowerCamelCase_	)	, mode="I;16"	) for image_depth in images_depth
											]
						elif images.shape[-1] == 4:
											a        =     (images_depth * 6_5535.0).astype(np.uintaa	)
											a        =     [Image.fromarray(lowerCamelCase_	, mode="I;16"	) for image_depth in images_depth]
						else:
											raise Exception("Not supported"	)

						return pil_images




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = "pil"	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						if not isinstance(lowerCamelCase_	, torch.Tensor	):
											raise ValueError(
											    F'''Input for postprocessing is in incorrect format: {type(lowerCamelCase_	)}. We only support pytorch tensor'''	)
						if output_type not in ["latent", "pt", "np", "pil"]:
											a        =     (
											    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
											    "`pil`, `np`, `pt`, `latent`"
											)
											deprecate("Unsupported output_type"	, "1.0.0"	, lowerCamelCase_	, standard_warn=lowerCamelCase_	)
											a        =     "np"

						if do_denormalize is None:
											a        =     [self.config.do_normalize] * image.shape[0]

						a        =     torch.stack(
						    [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

						a        =     self.pt_to_numpy(lowerCamelCase_	)

						if output_type == "np":
											if image.shape[-1] == 6:
																a        =     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]	) for im in image]	, axis=0	)
											else:
																a        =     image[:, :, :, 3:]
											return image[:, :, :, :3], image_depth

						if output_type == "pil":
											return self.numpy_to_pil(lowerCamelCase_	), self.numpy_to_depth(lowerCamelCase_	)
						else:
											raise Exception(F'''This type {output_type} is not supported'''	)





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

						a        =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_	)	, jnp.log(lowerCamelCase_	)	, lowerCamelCase_	)	)
						a        =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowerCamelCase_	, sigmas=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						a        =     timestep * jnp.ones(
						    sample.shape[0]	, )
						a        =     (timestep * (len(state.timesteps	) - 1)).long()

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)
						a        =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, prev_sample_mean=lowerCamelCase_	, state=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a        =     step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						a        =     step_size.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     sample + step_size * model_output
						a        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
