


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

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

@flax.struct.dataclass
class 		__a				:
		_a     :  CommonSchedulerState

		# setable values
		_a     :  jnp.ndarray
		_a     :  jnp.ndarray
		_a     :  Optional[int]           =     None
		@classmethod
		def      UpperCAmelCase__    (      cls      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									return cls(common=_SCREAMING_SNAKE_CASE      ,   init_noise_sigma=_SCREAMING_SNAKE_CASE      ,   timesteps=_SCREAMING_SNAKE_CASE						)



@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  DDPMSchedulerState



class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase      ):
		_a     :  Any           =     [e.name for e in FlaxKarrasDiffusionSchedulers]

		_a     :  jnp.dtype
		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return True
		@register_to_config
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = 1000      ,   _SCREAMING_SNAKE_CASE = 0.0001      ,   _SCREAMING_SNAKE_CASE = 0.02      ,   _SCREAMING_SNAKE_CASE = "linear"      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = "fixed_small"      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = "epsilon"      ,   _SCREAMING_SNAKE_CASE = jnp.floataa      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      dtype
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE = None						)			->	DDPMSchedulerState:




									"""simple docstring"""





									if common is None:
																_UpperCAmelCase					=      CommonSchedulerState.create(self						)

									# standard deviation of the initial noise distribution
									_UpperCAmelCase					=      jnp.array(1.0      ,   dtype=self.dtype						)

									_UpperCAmelCase					=      jnp.arange(0      ,   self.config.num_train_timesteps						).round()[::-1]

									return DDPMSchedulerState.create(
									    common=_SCREAMING_SNAKE_CASE      ,   init_noise_sigma=_SCREAMING_SNAKE_CASE      ,   timesteps=_SCREAMING_SNAKE_CASE      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None						)			->	jnp.ndarray:




									"""simple docstring"""





									return sample
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = ()						)			->	DDPMSchedulerState:




									"""simple docstring"""





									_UpperCAmelCase					=      self.config.num_train_timesteps // num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# rounding to avoid issues when num_inference_step is power of 3
									_UpperCAmelCase					=      (jnp.arange(0      ,   _SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1]

									return state.replace(
									    num_inference_steps=_SCREAMING_SNAKE_CASE      ,   timesteps=_SCREAMING_SNAKE_CASE      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      state.common.alphas_cumprod[t]
									_UpperCAmelCase					=      jnp.where(t > 0      ,   state.common.alphas_cumprod[t - 1]      ,   jnp.array(1.0      ,   dtype=self.dtype						)						)

									# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
									# and sample from it to get previous sample
									# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
									_UpperCAmelCase					=      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

									if variance_type is None:
																_UpperCAmelCase					=      self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
																_UpperCAmelCase					=      jnp.clip(_SCREAMING_SNAKE_CASE      ,   a_min=1e-2_0						)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
																_UpperCAmelCase					=      jnp.log(jnp.clip(_SCREAMING_SNAKE_CASE      ,   a_min=1e-2_0						)						)
									elif variance_type == "fixed_large":
																_UpperCAmelCase					=      state.common.betas[t]
									elif variance_type == "fixed_large_log":
																# Glide max_log
																_UpperCAmelCase					=      jnp.log(state.common.betas[t]						)
									elif variance_type == "learned":
																return predicted_variance
									elif variance_type == "learned_range":
																_UpperCAmelCase					=      variance
																_UpperCAmelCase					=      state.common.betas[t]
																_UpperCAmelCase					=      (predicted_variance + 1) / 2
																_UpperCAmelCase					=      frac * max_log + (1 - frac) * min_log

									return variance
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = True      ,   )			->	Union[FlaxDDPMSchedulerOutput, Tuple]:




									"""simple docstring"""





									_UpperCAmelCase					=      timestep

									if key is None:
																_UpperCAmelCase					=      jax.random.PRNGKey(0						)

									if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																_UpperCAmelCase		,	_UpperCAmelCase					=      jnp.split(_SCREAMING_SNAKE_CASE      ,   sample.shape[1]      ,   axis=1						)
									else:
																_UpperCAmelCase					=      None

									# 1. compute alphas, betas
									_UpperCAmelCase					=      state.common.alphas_cumprod[t]
									_UpperCAmelCase					=      jnp.where(t > 0      ,   state.common.alphas_cumprod[t - 1]      ,   jnp.array(1.0      ,   dtype=self.dtype						)						)
									_UpperCAmelCase					=      1 - alpha_prod_t
									_UpperCAmelCase					=      1 - alpha_prod_t_prev

									# 2. compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
									if self.config.prediction_type == "epsilon":
																_UpperCAmelCase					=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
																_UpperCAmelCase					=      model_output
									elif self.config.prediction_type == "v_prediction":
																_UpperCAmelCase					=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
																    ' for the FlaxDDPMScheduler.'						)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
																_UpperCAmelCase					=      jnp.clip(_SCREAMING_SNAKE_CASE      ,   -1      ,   1						)

									# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									_UpperCAmelCase					=      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									_UpperCAmelCase					=      state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

									# 5. Compute predicted previous sample µ_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									_UpperCAmelCase					=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
																_UpperCAmelCase					=      jax.random.split(_SCREAMING_SNAKE_CASE      ,   num=1						)
																_UpperCAmelCase					=      jax.random.normal(_SCREAMING_SNAKE_CASE      ,   shape=model_output.shape      ,   dtype=self.dtype						)
																return (self._get_variance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   predicted_variance=_SCREAMING_SNAKE_CASE						) ** 0.5) * noise

									_UpperCAmelCase					=      jnp.where(t > 0      ,   random_variance()      ,   jnp.zeros(model_output.shape      ,   dtype=self.dtype						)						)

									_UpperCAmelCase					=      pred_prev_sample + variance

									if not return_dict:
																return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE      ,   state=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	jnp.ndarray:




									"""simple docstring"""





									return add_noise_common(state.common      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	jnp.ndarray:




									"""simple docstring"""





									return get_velocity_common(state.common      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)






		def __len__(      self						)			->	int:




									"""simple docstring"""





									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


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									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(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									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(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_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(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_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 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.
									_UpperCAmelCase					=      [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

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

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

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

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

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

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
