




"""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


__lowercase		:		Optional[int]														=							"scheduler_config.json"



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					int							=					1
    UpperCamelCase_	:					List[str]							=					2
    UpperCamelCase_	:					Union[str, Any]							=					3
    UpperCamelCase_	:					Dict							=					4
    UpperCamelCase_	:					Dict							=					5






@dataclass
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					jnp.ndarray






class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					SCHEDULER_CONFIG_NAME
    UpperCamelCase_	:					Dict							=					['''dtype''']
    UpperCamelCase_	:					Tuple							=					[]
    UpperCamelCase_	:					List[str]							=					True
    @classmethod
    def  lowercase					(		cls							:     List[Any]	,      A_							:     Dict[str, Any] = None	,      A_							:     Optional[str] = None	,      A_							:     str=False	,      **A_							:     Union[str, Any]	,      )    ->       Any:
         __snake_case		,	__snake_case				=							cls.load_config(
             pretrained_model_name_or_path=A_	,      subfolder=A_	,      return_unused_kwargs=A_	,      **A_	,      )
         __snake_case		,	__snake_case				=							cls.from_config(A_	,      return_unused_kwargs=A_	,      **A_    )

         if hasattr(A_	,      '''create_state'''    ) and getattr(A_	,      '''has_state'''	,      A_    ):
              __snake_case				=							scheduler.create_state()

         if return_unused_kwargs:
              return scheduler, state, unused_kwargs

         return scheduler, state
    def  lowercase					(		self							:     List[Any]	,      A_							:     Union[str, os.PathLike]	,      A_							:     bool = False	,      **A_							:     Dict    )    ->       int:
         self.save_config(save_directory=A_	,      push_to_hub=A_	,      **A_    )
    @property
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         return self._get_compatibles()







    @classmethod
    def  lowercase					(		cls							:     Any    )    ->       Union[str, Any]:
         __snake_case				=							list(set([cls.__name__] + cls._compatibles    )    )
         __snake_case				=							importlib.import_module(__name__.split('''.'''    )[0]    )
         __snake_case				=							[
             getattr(A_	,      A_    ) for c in compatible_classes_str if hasattr(A_	,      A_    )
         ]
         return compatible_classes






def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     assert len(snake_case) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case) - x.ndim)),      snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=0.999,      snake_case=jnp.floataa):

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

     __snake_case				=							[]
     for i in range(snake_case):
          __snake_case				=							i / num_diffusion_timesteps
          __snake_case				=							(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(snake_case) / alpha_bar(snake_case),      snake_case))
     return jnp.array(snake_case,      dtype=snake_case)



@flax.struct.dataclass
class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					jnp.ndarray
    UpperCamelCase_	:					jnp.ndarray
    UpperCamelCase_	:					jnp.ndarray
    @classmethod
    def  lowercase					(		cls							:     List[str]	,      A_							:     Union[str, Any]    )    ->       str:
         __snake_case				=							scheduler.config

         if config.trained_betas is not None:
              __snake_case				=							jnp.asarray(config.trained_betas	,      dtype=scheduler.dtype    )
         elif config.beta_schedule == "linear":
              __snake_case				=							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.
              __snake_case				=							(
                  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
              __snake_case				=							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__}"    )

         __snake_case				=							1.0 - betas

         __snake_case				=							jnp.cumprod(A_	,      axis=0    )

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






def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case):
     __snake_case				=							state.alphas_cumprod

     __snake_case				=							alphas_cumprod[timesteps] ** 0.5
     __snake_case				=							sqrt_alpha_prod.flatten()
     __snake_case				=							broadcast_to_shape_from_left(snake_case,      original_samples.shape)

     __snake_case				=							(1 - alphas_cumprod[timesteps]) ** 0.5
     __snake_case				=							sqrt_one_minus_alpha_prod.flatten()
     __snake_case				=							broadcast_to_shape_from_left(snake_case,      original_samples.shape)

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case):
     __snake_case		,	__snake_case				=							get_sqrt_alpha_prod(snake_case,      snake_case,      snake_case,      snake_case)
     __snake_case				=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case):
     __snake_case		,	__snake_case				=							get_sqrt_alpha_prod(snake_case,      snake_case,      snake_case,      snake_case)
     __snake_case				=							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity
"""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


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      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 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							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:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''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  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							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  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		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_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __snake_case				=							resample if resample is not None else self.resample
         __snake_case				=							do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=							do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=							rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=							do_normalize if do_normalize is not None else self.do_normalize
         __snake_case				=							image_mean if image_mean is not None else self.image_mean
         __snake_case				=							image_std if image_std is not None else self.image_std

         __snake_case				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							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.
         __snake_case				=							[to_numpy_array(A_    ) for image in images]

         if do_resize:
              __snake_case				=							[self.resize(A_	,      A_	,      A_    ) for image in images]

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

         if do_rescale:
              __snake_case				=							[self.rescale(A_	,      A_    ) for image in images]

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      A_    ) for image in images]

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

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