






'''simple docstring'''
from typing import Dict, Iterable, List, Optional, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Dict		       =    ['pixel_values']




    def __init__(			self  :       Tuple  ,     a  :       bool = True  ,     a  :       Dict[str, int] = None  ,     a  :       PILImageResampling = PILImageResampling.BICUBIC  ,     a  :       bool = True  ,     a  :       Dict[str, int] = None  ,     a  :       bool = True  ,     a  :       Union[int, float] = 1 / 2_5_5  ,     a  :       bool = True  ,     a  :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN  ,     a  :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD  ,     **a  :       Dict  ,     ):






       '''simple docstring'''






       super().__init__(**a			)
       lowercase_	:    Any           =     size if size is not None else {"shortest_edge": 2_2_4}
       lowercase_	:    Optional[Any]           =     get_size_dict(a  ,     default_to_square=a			)
       lowercase_	:    Union[str, Any]           =     crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
       lowercase_	:    List[str]           =     get_size_dict(a  ,     param_name="crop_size"			)

       lowercase_	:    Union[str, Any]           =     do_resize
       lowercase_	:    Union[str, Any]           =     size
       lowercase_	:    Dict           =     resample
       lowercase_	:    Optional[int]           =     do_center_crop
       lowercase_	:    int           =     crop_size
       lowercase_	:    Any           =     do_rescale
       lowercase_	:    Union[str, Any]           =     rescale_factor
       lowercase_	:    Tuple           =     do_normalize
       lowercase_	:    Any           =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowercase_	:    Tuple           =     image_std if image_std is not None else IMAGENET_DEFAULT_STD




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       np.ndarray  ,     a  :       Dict[str, int]  ,     a  :       PILImageResampling = PILImageResampling.BICUBIC  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       lowercase_	:    int           =     get_size_dict(a  ,     default_to_square=a			)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
          lowercase_	:    Dict           =     int((2_5_6 / 2_2_4) * size["shortest_edge"]			)
          lowercase_	:    Tuple           =     get_resize_output_image_size(a  ,     size=a  ,     default_to_square=a			)
          lowercase_	:    Any           =     {"height": output_size[0], "width": output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
          raise ValueError(
              f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""			)
       return resize(
           a  ,     size=(size_dict["height"], size_dict["width"])  ,     resample=a  ,     data_format=a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       np.ndarray  ,     a  :       Dict[str, int]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Optional[Any]  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     get_size_dict(a			)
       if "height" not in size or "width" not in size:
          raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""			)
       return center_crop(a  ,     size=(size["height"], size["width"])  ,     data_format=a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       np.ndarray  ,     a  :       Union[int, float]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       np.ndarray  ,     a  :       Union[float, List[float]]  ,     a  :       Union[float, List[float]]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Any  ,     ):






       '''simple docstring'''






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





    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       ImageInput  ,     a  :       Optional[bool] = None  ,     a  :       Optional[Dict[str, int]] = None  ,     a  :       PILImageResampling = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[Dict[str, int]] = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[float] = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[Union[float, Iterable[float]]] = None  ,     a  :       Optional[Union[float, Iterable[float]]] = None  ,     a  :       Optional[TensorType] = None  ,     a  :       ChannelDimension = ChannelDimension.FIRST  ,     **a  :       Dict  ,     ):






       '''simple docstring'''






       lowercase_	:    Any           =     do_resize if do_resize is not None else self.do_resize
       lowercase_	:    Dict           =     resample if resample is not None else self.resample
       lowercase_	:    Optional[int]           =     do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase_	:    List[Any]           =     do_rescale if do_rescale is not None else self.do_rescale
       lowercase_	:    str           =     rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase_	:    Optional[int]           =     do_normalize if do_normalize is not None else self.do_normalize
       lowercase_	:    List[str]           =     image_mean if image_mean is not None else self.image_mean
       lowercase_	:    List[Any]           =     image_std if image_std is not None else self.image_std

       lowercase_	:    Union[str, Any]           =     size if size is not None else self.size
       lowercase_	:    Dict           =     get_size_dict(a  ,     default_to_square=a			)
       lowercase_	:    Any           =     crop_size if crop_size is not None else self.crop_size
       lowercase_	:    Any           =     get_size_dict(a  ,     param_name="crop_size"			)

       lowercase_	:    Union[str, Any]           =     make_list_of_images(a			)

       if not valid_images(a			):
          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.
       lowercase_	:    Optional[int]           =     [to_numpy_array(a			) for image in images]

       if do_resize:
          lowercase_	:    Any           =     [self.resize(a  ,     a  ,     a			) for image in images]

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

       if do_rescale:
          lowercase_	:    Optional[int]           =     [self.rescale(a  ,     a			) for image in images]

       if do_normalize:
          lowercase_	:    Dict           =     [self.normalize(a  ,     a  ,     a			) for image in images]

       lowercase_	:    Tuple           =     [to_channel_dimension_format(a  ,     a			) for image in images]

       lowercase_	:    int           =     {"pixel_values": images}
       return BatchFeature(data=a  ,     tensor_type=a			)












'''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


UpperCamelCase__ =			'scheduler_config.json'


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    1
    __lowerCamelCase:   List[Any]		       =    2
    __lowerCamelCase:   Optional[Any]		       =    3
    __lowerCamelCase:   int		       =    4
    __lowerCamelCase:   Optional[int]		       =    5


@dataclass
class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   jnp.ndarray


class        _UpperCAmelCase						:
    __lowerCamelCase:   List[str]		       =    SCHEDULER_CONFIG_NAME
    __lowerCamelCase:   Optional[int]		       =    ['dtype']
    __lowerCamelCase:   int		       =    []
    __lowerCamelCase:   Dict		       =    True




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Dict[str, Any] = None  ,     a  :       Optional[str] = None  ,     a  :       Union[str, Any]=False  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     cls.load_config(
           pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     **a  ,     )
       lowercase_    ,		lowercase_	:    Union[str, Any]           =     cls.from_config(a  ,     return_unused_kwargs=a  ,     **a			)

       if hasattr(a  ,     "create_state"			) and getattr(a  ,     "has_state"  ,     a			):
          lowercase_	:    Tuple           =     scheduler.create_state()

       if return_unused_kwargs:
          return scheduler, state, unused_kwargs

       return scheduler, state




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, os.PathLike]  ,     a  :       bool = False  ,     **a  :       int			):






       '''simple docstring'''






       self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return self._get_compatibles()





    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     list(set([cls.__name__] + cls._compatibles			)			)
       lowercase_	:    str           =     importlib.import_module(__name__.split("."			)[0]			)
       lowercase_	:    Optional[Any]           =     [
           getattr(a  ,     a			) for c in compatible_classes_str if hasattr(a  ,     a			)
       ]
       return compatible_classes




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   assert len(_UpperCamelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCamelCase							) - x.ndim)							)						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase=jnp.floataa							):


   """simple docstring"""




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

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


@flax.struct.dataclass
class        _UpperCAmelCase						:
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Any           =     scheduler.config

       if config.trained_betas is not None:
          lowercase_	:    Union[str, Any]           =     jnp.asarray(config.trained_betas  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
          lowercase_	:    List[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.
          lowercase_	:    Tuple           =     (
              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
          lowercase_	:    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__}"""			)

       lowercase_	:    str           =     1.0 - betas

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

       return cls(
           alphas=a  ,     betas=a  ,     alphas_cumprod=a  ,     )




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     state.alphas_cumprod

   lowercase_	:    Optional[Any]           =     alphas_cumprod[timesteps] ** 0.5
   lowercase_	:    int           =     sqrt_alpha_prod.flatten()
   lowercase_	:    Union[str, Any]           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   lowercase_	:    Optional[int]           =     (1 - alphas_cumprod[timesteps]) ** 0.5
   lowercase_	:    Union[str, Any]           =     sqrt_one_minus_alpha_prod.flatten()
   lowercase_	:    Any           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    str           =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Any           =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity




