



'''simple docstring'''






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowerCAmelCase:      Any		 =		'scheduler_config.json'

class 							a__(      lowerCamelCase__			):
    lowercase__				=					1
    lowercase__				=					2
    lowercase__				=					3
    lowercase__				=					4
    lowercase__				=					5

@dataclass
class 							a__(      lowerCamelCase__			):
    lowercase__				=					42

class 							a__:
    lowercase__				=					SCHEDULER_CONFIG_NAME
    lowercase__				=					["""dtype"""]
    lowercase__				=					[]
    lowercase__				=					True







    @classmethod
    def     lowercase_ (   cls				:						Tuple		,    __snake_case				:						Dict[str, Any] = None		,    __snake_case				:						Optional[str] = None		,    __snake_case				:						Dict=False		,    **__snake_case				:						Dict		,    ):
      a     ,			a		:      Dict       =  cls.load_config(
          pretrained_model_name_or_path=__snake_case		,    subfolder=__snake_case		,    return_unused_kwargs=__snake_case		,    **__snake_case		,    )
      a     ,			a		:      Tuple       =  cls.from_config(__snake_case		,    return_unused_kwargs=__snake_case		,    **__snake_case			)

      if hasattr(__snake_case		,    'create_state'			) and getattr(__snake_case		,    'has_state'		,    __snake_case			):
        a		:      Optional[Any]       =  scheduler.create_state()

      if return_unused_kwargs:
        return scheduler, state, unused_kwargs

      return scheduler, state







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Union[str, os.PathLike]		,    __snake_case				:						bool = False		,    **__snake_case				:						Any			):
      self.save_config(save_directory=__snake_case		,    push_to_hub=__snake_case		,    **__snake_case			)







    @property
    def     lowercase_ (   self				:						Union[str, Any]			):
      return self._get_compatibles()







    @classmethod
    def     lowercase_ (   cls				:						str			):
      a		:      Tuple       =  list(set([cls.__name__] + cls._compatibles			)			)
      a		:      Tuple       =  importlib.import_module(__name__.split('.'			)[0]			)
      a		:      Tuple       =  [
          getattr(__snake_case		,    __snake_case			) for c in compatible_classes_str if hasattr(__snake_case		,    __snake_case			)
      ]
      return compatible_classes
def    lowerCamelCase__ (		_A				,    _A       ):
  assert len(_A       ) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_A       ) - x.ndim)       )				,    _A       )
def    lowerCamelCase__ (		_A				,    _A=0.999				,    _A=jnp.floataa       ):

  def alpha_bar(_A       ):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2       ) ** 2

  a		:      Any       =  []
  for i in range(_A       ):
    a		:      int       =  i / num_diffusion_timesteps
    a		:      str       =  (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar(_A       ) / alpha_bar(_A       )				,    _A       )       )
  return jnp.array(_A				,    dtype=_A       )

@flax.struct.dataclass
class 							a__:
    lowercase__				=					42
    lowercase__				=					42
    lowercase__				=					42







    @classmethod
    def     lowercase_ (   cls				:						int		,    __snake_case				:						Optional[Any]			):
      a		:      int       =  scheduler.config

      if config.trained_betas is not None:
        a		:      Dict       =  jnp.asarray(config.trained_betas		,    dtype=scheduler.dtype			)
      elif config.beta_schedule == "linear":
        a		:      Union[str, Any]       =  jnp.linspace(config.beta_start		,    config.beta_end		,    config.num_train_timesteps		,    dtype=scheduler.dtype			)
      elif config.beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        a		:      int       =  (
            jnp.linspace(
                config.beta_start**0.5		,    config.beta_end**0.5		,    config.num_train_timesteps		,    dtype=scheduler.dtype			)
            ** 2
        )
      elif config.beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        a		:      Union[str, Any]       =  betas_for_alpha_bar(config.num_train_timesteps		,    dtype=scheduler.dtype			)
      else:
        raise NotImplementedError(
            F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

      a		:      str       =  1.0 - betas

      a		:      Dict       =  jnp.cumprod(__snake_case		,    axis=0			)

      return cls(
          alphas=__snake_case		,    betas=__snake_case		,    alphas_cumprod=__snake_case		,    )
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A       ):
  a		:      Dict       =  state.alphas_cumprod

  a		:      str       =  alphas_cumprod[timesteps] ** 0.5
  a		:      str       =  sqrt_alpha_prod.flatten()
  a		:      Any       =  broadcast_to_shape_from_left(_A				,    original_samples.shape       )

  a		:      int       =  (1 - alphas_cumprod[timesteps]) ** 0.5
  a		:      List[str]       =  sqrt_one_minus_alpha_prod.flatten()
  a		:      Union[str, Any]       =  broadcast_to_shape_from_left(_A				,    original_samples.shape       )

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A       ):
  a     ,			a		:      Dict       =  get_sqrt_alpha_prod(_A				,    _A				,    _A				,    _A       )
  a		:      Union[str, Any]       =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples

def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A       ):
  a     ,			a		:      Optional[Any]       =  get_sqrt_alpha_prod(_A				,    _A				,    _A				,    _A       )
  a		:      Dict       =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity




'''simple docstring'''






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


lowerCAmelCase:      Union[str, Any]		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):
    lowercase__				=					["""pixel_values"""]







    def __init__(   self				:						int		,    __snake_case				:						bool = True		,    __snake_case				:						Optional[Dict[str, int]] = None		,    __snake_case				:						PILImageResampling = PILImageResampling.BILINEAR		,    __snake_case				:						bool = True		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						bool = True		,    __snake_case				:						Union[int, float] = 1 / 2_55		,    __snake_case				:						bool = True		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    **__snake_case				:						List[str]		,    ):
      super().__init__(**__snake_case			)
      a		:      Dict       =  size if size is not None else {'shortest_edge': 2_56}
      a		:      Optional[int]       =  get_size_dict(__snake_case		,    default_to_square=__snake_case			)
      a		:      List[str]       =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      a		:      Dict       =  get_size_dict(__snake_case		,    param_name='crop_size'			)
      a		:      Optional[int]       =  do_resize
      a		:      Union[str, Any]       =  size
      a		:      Optional[Any]       =  resample
      a		:      int       =  do_center_crop
      a		:      List[str]       =  crop_size
      a		:      Tuple       =  do_rescale
      a		:      Any       =  rescale_factor
      a		:      str       =  do_normalize
      a		:      int       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a		:      List[str]       =  image_std if image_std is not None else IMAGENET_STANDARD_STD







    def     lowercase_ (   self				:						str		,    __snake_case				:						np.ndarray		,    __snake_case				:						Dict[str, int]		,    __snake_case				:						PILImageResampling = PILImageResampling.BICUBIC		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						Any		,    ):
      a		:      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()}"""			)
      a		:      str       =  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     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						np.ndarray		,    __snake_case				:						Dict[str, int]		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						Any		,    ):
      a		:      Any       =  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` and `width`. Got {size.keys()}"""			)
      return center_crop(__snake_case		,    size=(size['height'], size['width'])		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						np.ndarray		,    __snake_case				:						float		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						str			):
      return rescale(__snake_case		,    scale=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Optional[int]		,    __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				:						List[str]		,    ):
      return normalize(__snake_case		,    mean=__snake_case		,    std=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







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

      a		:      int       =  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.'			)

      # All transformations expect numpy arrays.
      a		:      int       =  [to_numpy_array(__snake_case			) for image in images]

      if do_resize:
        a		:      List[Any]       =  [self.resize(image=__snake_case		,    size=__snake_case		,    resample=__snake_case			) for image in images]

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

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

      if do_normalize:
        a		:      List[str]       =  [self.normalize(image=__snake_case		,    mean=__snake_case		,    std=__snake_case			) for image in images]

      a		:      str       =  [to_channel_dimension_format(__snake_case		,    __snake_case			) for image in images]

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







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Tuple		,    __snake_case				:						List[Tuple] = None			):
      a		:      Union[str, Any]       =  outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
        if len(__snake_case			) != len(__snake_case			):
          raise ValueError(
              'Make sure that you pass in as many target sizes as the batch dimension of the logits'			)

        if is_torch_tensor(__snake_case			):
          a		:      Optional[int]       =  target_sizes.numpy()

        a		:      Union[str, Any]       =  []

        for idx in range(len(__snake_case			)			):
          a		:      Dict       =  torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0			)		,    size=target_sizes[idx]		,    mode='bilinear'		,    align_corners=__snake_case			)
          a		:      Optional[Any]       =  resized_logits[0].argmax(dim=0			)
          semantic_segmentation.append(__snake_case			)
      else:
        a		:      List[Any]       =  logits.argmax(dim=1			)
        a		:      List[str]       =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

      return semantic_segmentation