


"""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


snake_case__		:     List[Any]    				=				logging.get_logger(__name__)



class 		snake_case_(       a__			):
     __UpperCamelCase  =					['''pixel_values''']






     def __init__(	self  :					str					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Dict[str, int] = None					,				UpperCamelCase_  :					PILImageResampling = PILImageResampling.BICUBIC					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Dict[str, int] = None					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Union[int, float] = 1 / 2_5_5					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					,				UpperCamelCase_  :					Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					,				**UpperCamelCase_  :					Optional[Any]					,				):
           super().__init__(**__UpperCamelCase				)
           lowerCAmelCase					:	Dict       =   size if size is not None else {'''shortest_edge''': 2_2_4}
           lowerCAmelCase					:	str       =   get_size_dict(__UpperCamelCase					,				default_to_square=__UpperCamelCase				)
           lowerCAmelCase					:	Optional[Any]       =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           lowerCAmelCase					:	int       =   get_size_dict(__UpperCamelCase					,				param_name='''crop_size'''				)

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






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					np.ndarray					,				UpperCamelCase_  :					Dict[str, int]					,				UpperCamelCase_  :					PILImageResampling = PILImageResampling.BICUBIC					,				UpperCamelCase_  :					Optional[Union[str, ChannelDimension]] = None					,				**UpperCamelCase_  :					str					,				):
           lowerCAmelCase					:	str       =   get_size_dict(__UpperCamelCase					,				default_to_square=__UpperCamelCase				)
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                 lowerCAmelCase					:	Union[str, Any]       =   int((2_5_6 / 2_2_4) * size['''shortest_edge''']				)
                 lowerCAmelCase					:	Optional[int]       =   get_resize_output_image_size(__UpperCamelCase					,				size=__UpperCamelCase					,				default_to_square=__UpperCamelCase				)
                 lowerCAmelCase					:	int       =   {'''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(
               __UpperCamelCase					,				size=(size_dict['''height'''], size_dict['''width'''])					,				resample=__UpperCamelCase					,				data_format=__UpperCamelCase					,				**__UpperCamelCase				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					np.ndarray					,				UpperCamelCase_  :					Dict[str, int]					,				UpperCamelCase_  :					Optional[Union[str, ChannelDimension]] = None					,				**UpperCamelCase_  :					List[Any]					,				):
           lowerCAmelCase					:	List[Any]       =   get_size_dict(__UpperCamelCase				)
           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(__UpperCamelCase					,				size=(size['''height'''], size['''width'''])					,				data_format=__UpperCamelCase					,				**__UpperCamelCase				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					np.ndarray					,				UpperCamelCase_  :					Union[int, float]					,				UpperCamelCase_  :					Optional[Union[str, ChannelDimension]] = None					,				**UpperCamelCase_  :					int					,				):
           return rescale(__UpperCamelCase					,				scale=__UpperCamelCase					,				data_format=__UpperCamelCase					,				**__UpperCamelCase				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					np.ndarray					,				UpperCamelCase_  :					Union[float, List[float]]					,				UpperCamelCase_  :					Union[float, List[float]]					,				UpperCamelCase_  :					Optional[Union[str, ChannelDimension]] = None					,				**UpperCamelCase_  :					Optional[Any]					,				):
           return normalize(__UpperCamelCase					,				mean=__UpperCamelCase					,				std=__UpperCamelCase					,				data_format=__UpperCamelCase					,				**__UpperCamelCase				)






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					ImageInput					,				UpperCamelCase_  :					Optional[bool] = None					,				UpperCamelCase_  :					Optional[Dict[str, int]] = None					,				UpperCamelCase_  :					PILImageResampling = None					,				UpperCamelCase_  :					Optional[bool] = None					,				UpperCamelCase_  :					Optional[Dict[str, int]] = None					,				UpperCamelCase_  :					Optional[bool] = None					,				UpperCamelCase_  :					Optional[float] = None					,				UpperCamelCase_  :					Optional[bool] = None					,				UpperCamelCase_  :					Optional[Union[float, Iterable[float]]] = None					,				UpperCamelCase_  :					Optional[Union[float, Iterable[float]]] = None					,				UpperCamelCase_  :					Optional[TensorType] = None					,				UpperCamelCase_  :					ChannelDimension = ChannelDimension.FIRST					,				**UpperCamelCase_  :					Dict					,				):
           lowerCAmelCase					:	List[Any]       =   do_resize if do_resize is not None else self.do_resize
           lowerCAmelCase					:	str       =   resample if resample is not None else self.resample
           lowerCAmelCase					:	List[Any]       =   do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCAmelCase					:	Dict       =   do_rescale if do_rescale is not None else self.do_rescale
           lowerCAmelCase					:	int       =   rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCAmelCase					:	str       =   do_normalize if do_normalize is not None else self.do_normalize
           lowerCAmelCase					:	int       =   image_mean if image_mean is not None else self.image_mean
           lowerCAmelCase					:	Tuple       =   image_std if image_std is not None else self.image_std

           lowerCAmelCase					:	Optional[int]       =   size if size is not None else self.size
           lowerCAmelCase					:	List[Any]       =   get_size_dict(__UpperCamelCase					,				default_to_square=__UpperCamelCase				)
           lowerCAmelCase					:	Union[str, Any]       =   crop_size if crop_size is not None else self.crop_size
           lowerCAmelCase					:	Optional[Any]       =   get_size_dict(__UpperCamelCase					,				param_name='''crop_size'''				)

           lowerCAmelCase					:	Union[str, Any]       =   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:
                 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.
           lowerCAmelCase					:	Dict       =   [to_numpy_array(__UpperCamelCase				) for image in images]

           if do_resize:
                 lowerCAmelCase					:	Optional[Any]       =   [self.resize(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase				) for image in images]

           if do_center_crop:
                 lowerCAmelCase					:	Tuple       =   [self.center_crop(__UpperCamelCase					,				__UpperCamelCase				) for image in images]

           if do_rescale:
                 lowerCAmelCase					:	int       =   [self.rescale(__UpperCamelCase					,				__UpperCamelCase				) for image in images]

           if do_normalize:
                 lowerCAmelCase					:	Optional[Any]       =   [self.normalize(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase				) for image in images]

           lowerCAmelCase					:	Tuple       =   [to_channel_dimension_format(__UpperCamelCase					,				__UpperCamelCase				) for image in images]

           lowerCAmelCase					:	Optional[int]       =   {'''pixel_values''': images}
           return BatchFeature(data=__UpperCamelCase					,				tensor_type=__UpperCamelCase				)






"""simple docstring"""





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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 		snake_case_(       a__			):
     __UpperCamelCase  =					42
     __UpperCamelCase  =					None



def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			List[str]=0.999 ,   _snake_case				:			Dict="cosine" ,   ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_snake_case				:			List[Any]			):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_snake_case				:			Optional[int]			):
                  return math.exp(t * -12.0			)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''			)

      lowerCAmelCase					:	List[Any]       =   []
      for i in range(_snake_case			):
            lowerCAmelCase					:	int       =   i / num_diffusion_timesteps
            lowerCAmelCase					:	Tuple       =   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_snake_case			) / alpha_bar_fn(_snake_case			) ,   _snake_case			)			)
      return torch.tensor(_snake_case ,   dtype=torch.floataa			)






class 		snake_case_(       a__							,					a__			):






     @register_to_config
     def __init__(	self  :					Any					,				UpperCamelCase_  :					int = 1_0_0_0					,				UpperCamelCase_  :					str = "fixed_small_log"					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[float] = 1.0					,				UpperCamelCase_  :					str = "epsilon"					,				UpperCamelCase_  :					str = "squaredcos_cap_v2"					,				):
           if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''				)

           lowerCAmelCase					:	Any       =   betas_for_alpha_bar(UpperCamelCase_				)

           lowerCAmelCase					:	str       =   1.0 - self.betas
           lowerCAmelCase					:	Union[str, Any]       =   torch.cumprod(self.alphas					,				dim=0				)
           lowerCAmelCase					:	Tuple       =   torch.tensor(1.0				)

           # standard deviation of the initial noise distribution
           lowerCAmelCase					:	Any       =   1.0

           # setable values
           lowerCAmelCase					:	Any       =   None
           lowerCAmelCase					:	Any       =   torch.from_numpy(np.arange(0					,				UpperCamelCase_				)[::-1].copy()				)

           lowerCAmelCase					:	List[str]       =   variance_type






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None				):
           return sample






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, torch.device] = None				):
           lowerCAmelCase					:	Any       =   num_inference_steps
           lowerCAmelCase					:	str       =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           lowerCAmelCase					:	Tuple       =   (np.arange(0					,				UpperCamelCase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
           lowerCAmelCase					:	Any       =   torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					Any=None				):
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	int       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	Dict       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	Tuple       =   self.betas[t]
           else:
                 lowerCAmelCase					:	Union[str, Any]       =   1 - alpha_prod_t / alpha_prod_t_prev

           # 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
           lowerCAmelCase					:	Optional[Any]       =   beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                 lowerCAmelCase					:	List[str]       =   self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                 lowerCAmelCase					:	Any       =   torch.log(torch.clamp(UpperCamelCase_					,				min=1E-20				)				)
                 lowerCAmelCase					:	Union[str, Any]       =   torch.exp(0.5 * variance				)
           elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 lowerCAmelCase					:	Optional[Any]       =   variance.log()
                 lowerCAmelCase					:	Union[str, Any]       =   beta.log()

                 lowerCAmelCase					:	Dict       =   (predicted_variance + 1) / 2
                 lowerCAmelCase					:	Union[str, Any]       =   frac * max_log + (1 - frac) * min_log

           return variance






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					bool = True					,				):
           lowerCAmelCase					:	Optional[Any]       =   timestep

           if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   torch.split(UpperCamelCase_					,				sample.shape[1]					,				dim=1				)
           else:
                 lowerCAmelCase					:	Optional[int]       =   None

           # 1. compute alphas, betas
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Optional[int]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	int       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	List[Any]       =   self.betas[t]
                 lowerCAmelCase					:	Optional[int]       =   self.alphas[t]
           else:
                 lowerCAmelCase					:	List[Any]       =   1 - alpha_prod_t / alpha_prod_t_prev
                 lowerCAmelCase					:	Dict       =   1 - beta

           # 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":
                 lowerCAmelCase					:	List[Any]       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                 lowerCAmelCase					:	Tuple       =   model_output
           else:
                 raise ValueError(
                     F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                     ''' for the UnCLIPScheduler.'''				)

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                 lowerCAmelCase					:	Dict       =   torch.clamp(
                     UpperCamelCase_					,				-self.config.clip_sample_range					,				self.config.clip_sample_range				)

           # 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
           lowerCAmelCase					:	int       =   (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
           lowerCAmelCase					:	List[Any]       =   alpha ** 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
           lowerCAmelCase					:	str       =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           lowerCAmelCase					:	int       =   0
           if t > 0:
                 lowerCAmelCase					:	Union[str, Any]       =   randn_tensor(
                     model_output.shape					,				dtype=model_output.dtype					,				generator=UpperCamelCase_					,				device=model_output.device				)

                 lowerCAmelCase					:	Any       =   self._get_variance(
                     UpperCamelCase_					,				predicted_variance=UpperCamelCase_					,				prev_timestep=UpperCamelCase_					,				)

                 if self.variance_type == "fixed_small_log":
                       lowerCAmelCase					:	str       =   variance
                 elif self.variance_type == "learned_range":
                       lowerCAmelCase					:	Optional[Any]       =   (0.5 * variance).exp()
                 else:
                       raise ValueError(
                           F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                           ''' for the UnCLIPScheduler.'''				)

                 lowerCAmelCase					:	List[Any]       =   variance * variance_noise

           lowerCAmelCase					:	int       =   pred_prev_sample + variance

           if not return_dict:
                 return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase_					,				pred_original_sample=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.IntTensor					,				):
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           lowerCAmelCase					:	Tuple       =   self.alphas_cumprod.to(device=original_samples.device					,				dtype=original_samples.dtype				)
           lowerCAmelCase					:	int       =   timesteps.to(original_samples.device				)

           lowerCAmelCase					:	Dict       =   alphas_cumprod[timesteps] ** 0.5
           lowerCAmelCase					:	str       =   sqrt_alpha_prod.flatten()
           while len(sqrt_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	Any       =   sqrt_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	List[str]       =   (1 - alphas_cumprod[timesteps]) ** 0.5
           lowerCAmelCase					:	Tuple       =   sqrt_one_minus_alpha_prod.flatten()
           while len(sqrt_one_minus_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	int       =   sqrt_one_minus_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	Dict       =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples


