
'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class 			lowerCAmelCase__ :



  '''simple docstring'''






  _UpperCamelCase							=			42

  # setable values
  _UpperCamelCase							=			42
  _UpperCamelCase							=			42
  _UpperCamelCase							=			42
  _UpperCamelCase							=			None

  # running values
  _UpperCamelCase							=			None

  @classmethod
  def 		_lowerCamelCase						(cls	:			List[str]						,			lowerCAmelCase_	:			CommonSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray     ) ->							Optional[Any]:
        return cls(common=lowerCAmelCase_						,			init_noise_sigma=lowerCAmelCase_						,			timesteps=lowerCAmelCase_						,			sigmas=lowerCAmelCase_     )



@dataclass
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			42



class 			lowerCAmelCase__ (							UpperCamelCase       ,				UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			[e.name for e in FlaxKarrasDiffusionSchedulers]

  _UpperCamelCase							=			42

  @property
  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Tuple:
        return True

  @register_to_config
  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			int = 10_00						,			lowerCAmelCase_	:			float = 0.0_001						,			lowerCAmelCase_	:			float = 0.02						,			lowerCAmelCase_	:			str = "linear"						,			lowerCAmelCase_	:			Optional[jnp.ndarray] = None						,			lowerCAmelCase_	:			str = "epsilon"						,			lowerCAmelCase_	:			jnp.dtype = jnp.floataa						,			) ->							Dict:
        snake_case_    							=				dtype

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			Optional[CommonSchedulerState] = None     ) ->							LMSDiscreteSchedulerState:
        if common is None:
              snake_case_    							=				CommonSchedulerState.create(self     )

        snake_case_    							=				jnp.arange(0						,			self.config.num_train_timesteps     ).round()[::-1]
        snake_case_    							=				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case_    							=				sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowerCAmelCase_						,			init_noise_sigma=lowerCAmelCase_						,			timesteps=lowerCAmelCase_						,			sigmas=lowerCAmelCase_						,			)

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			int     ) ->							jnp.ndarray:
        ((snake_case_)							,						)    							=				jnp.where(state.timesteps == timestep						,			size=1     )
        snake_case_    							=				step_index[0]

        snake_case_    							=				state.sigmas[step_index]
        snake_case_    							=				sample / ((sigma**2 + 1) ** 0.5)
        return sample

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Any     ) ->							Optional[Any]:

        def lms_derivative(lowerCAmelCase_	:			Tuple     ):
              snake_case_    							=				1.0
              for k in range(lowerCAmelCase_     ):
                    if current_order == k:
                          continue
                    prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
              return prod

        snake_case_    							=				integrate.quad(lowerCAmelCase_						,			state.sigmas[t]						,			state.sigmas[t + 1]						,			epsrel=1E-4     )[0]

        return integrated_coeff

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Tuple = ()     ) ->							LMSDiscreteSchedulerState:
        snake_case_    							=				jnp.linspace(self.config.num_train_timesteps - 1						,			0						,			lowerCAmelCase_						,			dtype=self.dtype     )

        snake_case_    							=				jnp.floor(lowerCAmelCase_     ).astype(jnp.intaa     )
        snake_case_    							=				jnp.ceil(lowerCAmelCase_     ).astype(jnp.intaa     )

        snake_case_    							=				jnp.mod(lowerCAmelCase_						,			1.0     )

        snake_case_    							=				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case_    							=				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case_    							=				jnp.concatenate([sigmas, jnp.array([0.0]						,			dtype=self.dtype     )]     )

        snake_case_    							=				timesteps.astype(jnp.intaa     )

        # initial running values
        snake_case_    							=				jnp.zeros((0,) + shape						,			dtype=self.dtype     )

        return state.replace(
            timesteps=lowerCAmelCase_						,			sigmas=lowerCAmelCase_						,			num_inference_steps=lowerCAmelCase_						,			derivatives=lowerCAmelCase_						,			)

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			int = 4						,			lowerCAmelCase_	:			bool = True						,			) ->							Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
              raise ValueError(
                  "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"     )

        snake_case_    							=				state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
              snake_case_    							=				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              snake_case_    							=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
              raise ValueError(
                  f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

        # 2. Convert to an ODE derivative
        snake_case_    							=				(sample - pred_original_sample) / sigma
        snake_case_    							=				state.replace(derivatives=jnp.append(state.derivatives						,			lowerCAmelCase_     )     )
        if len(state.derivatives     ) > order:
              snake_case_    							=				state.replace(derivatives=jnp.delete(state.derivatives						,			0     )     )

        # 3. Compute linear multistep coefficients
        snake_case_    							=				min(timestep + 1						,			lowerCAmelCase_     )
        snake_case_    							=				[self.get_lms_coefficient(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     ) for curr_order in range(lowerCAmelCase_     )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_    							=				sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCAmelCase_						,			reversed(state.derivatives     )     )     )

        if not return_dict:
              return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowerCAmelCase_						,			state=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray						,			) ->							jnp.ndarray:
        snake_case_    							=				state.sigmas[timesteps].flatten()
        snake_case_    							=				broadcast_to_shape_from_left(lowerCAmelCase_						,			noise.shape     )

        snake_case_    							=				original_samples + noise * sigma

        return noisy_samples


  def __len__(self	:			Dict     ) ->							Dict:
        return self.config.num_train_timesteps







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, 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


__A						:		Dict          =						logging.get_logger(__name__)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= ["pixel_values"]




					def __init__(   self:		str,     _lowercase:		bool = True,     _lowercase:		Dict[str, int] = None,     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		bool = False,     _lowercase:		Dict[str, int] = None,     _lowercase:		Union[int, float] = 1 / 255,     _lowercase:		bool = False,     _lowercase:		bool = True,     _lowercase:		bool = True,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = True,     **_lowercase:		Union[str, Any],     ):


									'''simple docstring'''


									super().__init__(**_lowercase)
									__lowerCAmelCase					=  size if size is not None else {"""height""": 346, """width""": 346}
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  resample
									__lowerCAmelCase					=  do_center_crop
									__lowerCAmelCase					=  crop_size
									__lowerCAmelCase					=  do_rescale
									__lowerCAmelCase					=  rescale_factor
									__lowerCAmelCase					=  rescale_offset
									__lowerCAmelCase					=  do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__lowerCAmelCase					=  image_std if image_std is not None else IMAGENET_STANDARD_STD
									__lowerCAmelCase					=  include_top




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return resize(
									    _lowercase,     size=(size["""height"""], size["""width"""]),     resample=_lowercase,     data_format=_lowercase,     **_lowercase)




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


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return center_crop(_lowercase,     size=(size["""height"""], size["""width"""]),     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		np.ndarray,     _lowercase:		Union[int, float],     _lowercase:		bool = True,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									if offset:
													__lowerCAmelCase					=  (image - 127.5) * scale
													if data_format is not None:
																	__lowerCAmelCase					=  to_channel_dimension_format(_lowercase,     _lowercase)
													__lowerCAmelCase					=  rescaled_image.astype(np.floataa)
									else:
													__lowerCAmelCase					=  rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase)
									return rescaled_image




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


									'''simple docstring'''


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







					def     _lowercase						(   self:		Dict,     _lowercase:		ImageInput,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		str=None,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		bool = None,     _lowercase:		float = None,     _lowercase:		bool = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[str, TensorType]] = None,     _lowercase:		ChannelDimension = ChannelDimension.FIRST,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  do_resize if do_resize is not None else self.do_resize
									__lowerCAmelCase					=  resample if resample is not None else self.resample
									__lowerCAmelCase					=  do_center_crop if do_center_crop is not None else self.do_center_crop
									__lowerCAmelCase					=  do_rescale if do_rescale is not None else self.do_rescale
									__lowerCAmelCase					=  rescale_factor if rescale_factor is not None else self.rescale_factor
									__lowerCAmelCase					=  rescale_offset if rescale_offset is not None else self.rescale_offset
									__lowerCAmelCase					=  do_normalize if do_normalize is not None else self.do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else self.image_mean
									__lowerCAmelCase					=  image_std if image_std is not None else self.image_std
									__lowerCAmelCase					=  include_top if include_top is not None else self.include_top

									__lowerCAmelCase					=  size if size is not None else self.size
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else self.crop_size
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  make_list_of_images(_lowercase)

									if not valid_images(_lowercase):
													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_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.
									__lowerCAmelCase					=  [to_numpy_array(_lowercase) for image in images]

									if do_resize:
													__lowerCAmelCase					=  [self.resize(image=_lowercase,     size=_lowercase,     resample=_lowercase) for image in images]

									if do_center_crop:
													__lowerCAmelCase					=  [self.center_crop(image=_lowercase,     size=_lowercase) for image in images]

									if do_rescale:
													__lowerCAmelCase					=  [self.rescale(image=_lowercase,     scale=_lowercase,     offset=_lowercase) for image in images]

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

									if include_top:
													__lowerCAmelCase					=  [self.normalize(image=_lowercase,     mean=[0, 0, 0],     std=_lowercase) for image in images]

									__lowerCAmelCase					=  [to_channel_dimension_format(_lowercase,     _lowercase) for image in images]

									__lowerCAmelCase					=  {"""pixel_values""": images}
									return BatchFeature(data=_lowercase,     tensor_type=_lowercase)





