

"""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      =		"""scheduler_config.json"""
class    lowercase__     (					lowerCAmelCase__   ):







       '''simple docstring'''


       UpperCamelCase		=  1
       UpperCamelCase		=  2
       UpperCamelCase		=  3
       UpperCamelCase		=  4
       UpperCamelCase		=  5



@dataclass
class    lowercase__     (					lowerCAmelCase__   ):







       '''simple docstring'''


       UpperCamelCase		=  42



class    lowercase__     :







       '''simple docstring'''


       UpperCamelCase		=  SCHEDULER_CONFIG_NAME
       UpperCamelCase		=  ["dtype"]
       UpperCamelCase		=  []
       UpperCamelCase		=  True



       @classmethod
       def   lowercase__       (							cls    :						Any	,       _UpperCAmelCase    :						int = None	,       _UpperCAmelCase    :						int = None	,       _UpperCAmelCase    :						Dict=False	,       **_UpperCAmelCase    :						Dict	,       )				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.load_config(
                 pretrained_model_name_or_path=_lowerCamelCase	,       subfolder=_lowerCamelCase	,       return_unused_kwargs=_lowerCamelCase	,       **_lowerCamelCase	,       )
             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.from_config(_lowerCamelCase	,       return_unused_kwargs=_lowerCamelCase	,       **_lowerCamelCase					)

             if hasattr(_lowerCamelCase	,       "create_state"					) and getattr(_lowerCamelCase	,       "has_state"	,       _lowerCamelCase					):
                   UpperCAmelCase_    				=							scheduler.create_state()

             if return_unused_kwargs:
                   return scheduler, state, unused_kwargs

             return scheduler, state



       def   lowercase__       (							self    :						str	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						str = False	,       **_UpperCAmelCase    :						List[str]					)				->		str:


             '''simple docstring'''




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



       @property
       def   lowercase__       (							self    :						Dict					)				->		List[Any]:


             '''simple docstring'''




             return self._get_compatibles()







       @classmethod
       def   lowercase__       (							cls    :						List[Any]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							list(set([cls.__name__] + cls._compatibles					)					)
             UpperCAmelCase_    				=							importlib.import_module(__name__.split("."					)[0]					)
             UpperCAmelCase_    				=							[
                 getattr(_lowerCamelCase	,       _lowerCamelCase					) for c in compatible_classes_str if hasattr(_lowerCamelCase	,       _lowerCamelCase					)
             ]
             return compatible_classes



def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      assert len(lowerCamelCase_   ) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_   ) - x.ndim)   )			,    lowerCamelCase_   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=0.999			,    lowerCAmelCase__=jnp.floataa   ):

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

      UpperCAmelCase_    				=							[]
      for i in range(lowerCamelCase_   ):
            UpperCAmelCase_    				=							i / num_diffusion_timesteps
            UpperCAmelCase_    				=							(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar(lowerCamelCase_   ) / alpha_bar(lowerCamelCase_   )			,    lowerCamelCase_   )   )
      return jnp.array(lowerCamelCase_			,    dtype=lowerCamelCase_   )
@flax.struct.dataclass
class    lowercase__     :







       '''simple docstring'''


       UpperCamelCase		=  42
       UpperCamelCase		=  42
       UpperCamelCase		=  42



       @classmethod
       def   lowercase__       (							cls    :						List[Any]	,       _UpperCAmelCase    :						Dict					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							scheduler.config

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

             UpperCAmelCase_    				=							1.0 - betas

             UpperCAmelCase_    				=							jnp.cumprod(_lowerCamelCase	,       axis=0					)

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



def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							state.alphas_cumprod

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

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

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							get_sqrt_alpha_prod(lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_   )
      UpperCAmelCase_    				=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples


def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							get_sqrt_alpha_prod(lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_			,    lowerCamelCase_   )
      UpperCAmelCase_    				=							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity









"""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 (
    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_vision_available, logging


if is_vision_available():
  import PIL


lowerCamelCase      =		logging.get_logger(__name__)
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



       def __init__(							self    :						str	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Union[int, float] = 1 / 255	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		None:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							size if size is not None else {"shortest_edge": 384}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size
             # Default value set here for backwards compatibility where the value in config is None
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else 224 / 256
             UpperCAmelCase_    				=							resample
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             UpperCAmelCase_    				=							image_std if image_std is not None else IMAGENET_STANDARD_STD



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Dict[str, int]	,       _UpperCAmelCase    :						float	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BICUBIC	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             if "shortest_edge" not in size:
                   raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
             UpperCAmelCase_    				=							size["shortest_edge"]

             if shortest_edge < 384:
                   # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                   UpperCAmelCase_    				=							int(shortest_edge / crop_pct					)
                   UpperCAmelCase_    				=							get_resize_output_image_size(_UpperCAmelCase	,       size=_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
                   UpperCAmelCase_    				=							resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
                   # then crop to (shortest_edge, shortest_edge)
                   return center_crop(image=_UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
             else:
                   # warping (no cropping) when evaluated at 384 or larger
                   return resize(
                       _UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[int, float]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						List[Any]	,       )				->		Any:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Dict	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						List[str]	,       )				->		np.ndarray:


             '''simple docstring'''




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







       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						ImageInput	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						PILImageResampling = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[str, TensorType]] = None	,       _UpperCAmelCase    :						ChannelDimension = ChannelDimension.FIRST	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		PIL.Image.Image:


             '''simple docstring'''




             UpperCAmelCase_    				=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else self.crop_pct
             UpperCAmelCase_    				=							resample if resample is not None else self.resample
             UpperCAmelCase_    				=							do_rescale if do_rescale is not None else self.do_rescale
             UpperCAmelCase_    				=							rescale_factor if rescale_factor is not None else self.rescale_factor
             UpperCAmelCase_    				=							do_normalize if do_normalize is not None else self.do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else self.image_mean
             UpperCAmelCase_    				=							image_std if image_std is not None else self.image_std

             UpperCAmelCase_    				=							size if size is not None else self.size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							make_list_of_images(_UpperCAmelCase					)

             if not valid_images(_UpperCAmelCase					):
                   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 or resample is None:
                   raise ValueError("Size and resample must be specified if do_resize is True."					)

             if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
                   raise ValueError("crop_pct must be specified if size < 384."					)

             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.
             UpperCAmelCase_    				=							[to_numpy_array(_UpperCAmelCase					) for image in images]

             if do_resize:
                   UpperCAmelCase_    				=							[self.resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       crop_pct=_UpperCAmelCase	,       resample=_UpperCAmelCase					) for image in images]

             if do_rescale:
                   UpperCAmelCase_    				=							[self.rescale(image=_UpperCAmelCase	,       scale=_UpperCAmelCase					) for image in images]

             if do_normalize:
                   UpperCAmelCase_    				=							[self.normalize(image=_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							[to_channel_dimension_format(_UpperCAmelCase	,       _UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							{"pixel_values": images}
             return BatchFeature(data=_UpperCAmelCase	,       tensor_type=_UpperCAmelCase					)




