


'''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 (
    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


_UpperCamelCase          =     logging.get_logger(__name__)


if is_vision_available():
      import PIL







class        __magic_name__  (					UpperCAmelCase_   ):

     """simple docstring"""






     lowerCamelCase__     :   Dict						=							['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 ,    ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A  :					Any						     =       size if size is not None else {"height": 2560, "width": 1920}
          if isinstance(lowerCamelCase ,    (tuple, list)		):
               # The previous feature extractor size parameter was in (width, height) format
               __A  :					Dict						     =       size[::-1]
          __A  :					Optional[int]						     =       get_size_dict(lowerCamelCase		)

          __A  :					str						     =       do_resize
          __A  :					Optional[int]						     =       size
          __A  :					List[str]						     =       resample
          __A  :					Union[str, Any]						     =       do_thumbnail
          __A  :					int						     =       do_align_long_axis
          __A  :					Union[str, Any]						     =       do_pad
          __A  :					int						     =       do_rescale
          __A  :					List[str]						     =       rescale_factor
          __A  :					Dict						     =       do_normalize
          __A  :					Tuple						     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __A  :					Optional[Any]						     =       image_std if image_std is not None else IMAGENET_STANDARD_STD


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A     ,__A  :					Optional[int]						     =       get_image_size(lowerCamelCase		)
          __A     ,__A  :					Optional[Any]						     =       size["height"], size["width"]

          if (output_width < output_height and input_width > input_height) or (
              output_width > output_height and input_width < input_height
          ):
               __A  :					Dict						     =       np.rotaa(lowerCamelCase ,    3		)

          if data_format is not None:
               __A  :					Optional[int]						     =       to_channel_dimension_format(lowerCamelCase ,    lowerCamelCase		)

          return image


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = False ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A     ,__A  :					Tuple						     =       size["height"], size["width"]
          __A     ,__A  :					Dict						     =       get_image_size(lowerCamelCase		)

          __A  :					List[Any]						     =       output_width - input_width
          __A  :					str						     =       output_height - input_height

          if random_padding:
               __A  :					List[Any]						     =       np.random.randint(low=0 ,    high=delta_height + 1		)
               __A  :					str						     =       np.random.randint(low=0 ,    high=delta_width + 1		)
          else:
               __A  :					str						     =       delta_height // 2
               __A  :					Optional[Any]						     =       delta_width // 2

          __A  :					Tuple						     =       delta_height - pad_top
          __A  :					List[str]						     =       delta_width - pad_left

          __A  :					List[Any]						     =       ((pad_top, pad_bottom), (pad_left, pad_right))
          return pad(lowerCamelCase ,    lowerCamelCase ,    data_format=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = PILImageResampling.BICUBIC ,    lowerCamelCase = None ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A     ,__A  :					Dict						     =       get_image_size(lowerCamelCase		)
          __A     ,__A  :					Union[str, Any]						     =       size["height"], size["width"]

          # We always resize to the smallest of either the input or output size.
          __A  :					int						     =       min(lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[str]						     =       min(lowerCamelCase ,    lowerCamelCase		)

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

          if input_height > input_width:
               __A  :					Tuple						     =       int(input_width * height / input_height		)
          elif input_width > input_height:
               __A  :					List[Any]						     =       int(input_height * width / input_width		)

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = PILImageResampling.BICUBIC ,    lowerCamelCase = None ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       get_size_dict(lowerCamelCase		)
          __A  :					Optional[Any]						     =       min(size["height"] ,    size["width"]		)
          __A  :					str						     =       get_resize_output_image_size(lowerCamelCase ,    size=lowerCamelCase ,    default_to_square=lowerCamelCase		)
          __A  :					Optional[int]						     =       resize(lowerCamelCase ,    size=lowerCamelCase ,    resample=lowerCamelCase ,    data_format=lowerCamelCase ,    **lowerCamelCase		)
          return resized_image


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          return rescale(lowerCamelCase ,    scale=lowerCamelCase ,    data_format=lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          return normalize(lowerCamelCase ,    mean=lowerCamelCase ,    std=lowerCamelCase ,    data_format=lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      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 ,    ):


          '''simple docstring'''





          __A  :					List[str]						     =       do_resize if do_resize is not None else self.do_resize
          __A  :					Dict						     =       size if size is not None else self.size
          if isinstance(lowerCamelCase ,    (tuple, list)		):
               # Previous feature extractor had size in (width, height) format
               __A  :					Tuple						     =       size[::-1]
          __A  :					Optional[Any]						     =       get_size_dict(lowerCamelCase		)
          __A  :					Any						     =       resample if resample is not None else self.resample
          __A  :					int						     =       do_thumbnail if do_thumbnail is not None else self.do_thumbnail
          __A  :					Optional[int]						     =       do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
          __A  :					List[Any]						     =       do_pad if do_pad is not None else self.do_pad
          __A  :					Optional[Any]						     =       do_rescale if do_rescale is not None else self.do_rescale
          __A  :					Dict						     =       rescale_factor if rescale_factor is not None else self.rescale_factor
          __A  :					Dict						     =       do_normalize if do_normalize is not None else self.do_normalize
          __A  :					Optional[int]						     =       image_mean if image_mean is not None else self.image_mean
          __A  :					Union[str, Any]						     =       image_std if image_std is not None else self.image_std

          __A  :					Dict						     =       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.
          __A  :					str						     =       [to_numpy_array(lowerCamelCase		) for image in images]

          if do_align_long_axis:
               __A  :					Tuple						     =       [self.align_long_axis(lowerCamelCase ,    size=lowerCamelCase		) for image in images]

          if do_resize:
               __A  :					Optional[Any]						     =       [self.resize(image=lowerCamelCase ,    size=lowerCamelCase ,    resample=lowerCamelCase		) for image in images]

          if do_thumbnail:
               __A  :					List[str]						     =       [self.thumbnail(image=lowerCamelCase ,    size=lowerCamelCase		) for image in images]

          if do_pad:
               __A  :					Tuple						     =       [self.pad_image(image=lowerCamelCase ,    size=lowerCamelCase ,    random_padding=lowerCamelCase		) for image in images]

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

          if do_normalize:
               __A  :					Optional[Any]						     =       [self.normalize(image=lowerCamelCase ,    mean=lowerCamelCase ,    std=lowerCamelCase		) for image in images]

          __A  :					Tuple						     =       [to_channel_dimension_format(lowerCamelCase ,    lowerCamelCase		) for image in images]

          __A  :					int						     =       {"pixel_values": images}
          return BatchFeature(data=lowerCamelCase ,    tensor_type=lowerCamelCase		)





'''simple docstring'''


# 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        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							None
     lowerCamelCase__						=							None
     lowerCamelCase__						=							None


     @classmethod
     def lowerCAmelCase__     (      cls		):


          '''simple docstring'''





          return cls()







@dataclass
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							42
     lowerCamelCase__						=							None







class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase   ):

     """simple docstring"""






     @property
     def lowerCAmelCase__     (      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 lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = () ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Tuple						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

          __A  :					Optional[int]						     =       jnp.linspace(1 ,    lowerCamelCase ,    lowerCamelCase		)
          return state.replace(timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = None ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A  :					str						     =       sigma_min if sigma_min is not None else self.config.sigma_min
          __A  :					Union[str, Any]						     =       sigma_max if sigma_max is not None else self.config.sigma_max
          __A  :					Dict						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
               __A  :					Union[str, Any]						     =       self.set_timesteps(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

          __A  :					Dict						     =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase		) ,    jnp.log(lowerCamelCase		) ,    lowerCamelCase		)		)
          __A  :					int						     =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      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  :					Any						     =       timestep * jnp.ones(
              sample.shape[0] ,    )
          __A  :					Any						     =       (timestep * (len(state.timesteps		) - 1)).long()

          __A  :					Any						     =       state.discrete_sigmas[timesteps]
          __A  :					Union[str, Any]						     =       self.get_adjacent_sigma(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[str]						     =       jnp.zeros_like(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       (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  :					List[Any]						     =       diffusion.flatten()
          __A  :					List[Any]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					str						     =       drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          __A  :					Any						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					Union[str, Any]						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)
          __A  :					Dict						     =       sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          __A  :					int						     =       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 lowerCAmelCase__     (      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  :					int						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					int						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)

          # compute step size from the model_output, the noise, and the snr
          __A  :					Optional[Any]						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Dict						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Any						     =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          __A  :					List[str]						     =       step_size * jnp.ones(sample.shape[0]		)

          # compute corrected sample: model_output term and noise term
          __A  :					Optional[Any]						     =       step_size.flatten()
          __A  :					Optional[int]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					Dict						     =       sample + step_size * model_output
          __A  :					List[Any]						     =       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

