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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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:			int	       =       logging.get_logger(__name__)



class       UpperCAmelCase  (						SCREAMING_SNAKE_CASE_ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   ["pixel_values"]

       def __init__(						self     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 0.9     ,       __lowerCAmelCase = PILImageResampling.BICUBIC     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 1 / 255     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  None:
        super().__init__(**__a			)
        lowercase__    :   str						= size if size is not None else {'shortest_edge': 224}
        lowercase__    :   List[str]						= get_size_dict(__a     ,       default_to_square=__a			)
        lowercase__    :   Tuple						= crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase__    :   Optional[int]						= get_size_dict(__a     ,       param_name='''crop_size'''			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = PILImageResampling.BICUBIC     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  np.ndarray:
        lowercase__    :   Dict						= get_size_dict(__a     ,       default_to_square=__a			)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F"""size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""			)
        if crop_pct is not None:
         if "shortest_edge" in size:
          lowercase__    :   List[Any]						= int(size['''shortest_edge'''] / crop_pct			)
         elif "height" in size and "width" in size:
          if size["height"] == size["width"]:
           lowercase__    :   int						= int(size['''height'''] / crop_pct			)
          else:
           lowercase__    :   List[str]						= (int(size['''height'''] / crop_pct			), int(size['''width'''] / crop_pct			))
         else:
          raise ValueError('''Invalid size for resize: {}'''.format(__a			)			)

         lowercase__    :   Optional[Any]						= get_resize_output_image_size(__a     ,       size=__a     ,       default_to_square=__a			)
        else:
         if "shortest_edge" in size:
          lowercase__    :   Dict						= get_resize_output_image_size(__a     ,       size=size['''shortest_edge''']     ,       default_to_square=__a			)
         elif "height" in size and "width" in size:
          lowercase__    :   int						= (size['height'], size['width'])
         else:
          raise ValueError('''Invalid size for resize: {}'''.format(__a			)			)

        return resize(__a     ,       size=__a     ,       resample=__a     ,       data_format=__a     ,       **__a			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  np.ndarray:
        lowercase__    :   Dict						= get_size_dict(__a			)
        if "height" not in size or "width" not in size:
         raise ValueError(F"""size must contain \'height\' and \'width\' as keys. Got {size.keys()}"""			)
        return center_crop(__a     ,       size=(size['''height'''], size['''width'''])     ,       data_format=__a     ,       **__a			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  Any:
        return rescale(__a     ,       scale=__a     ,       data_format=__a     ,       **__a			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  np.ndarray:
        return normalize(__a     ,       mean=__a     ,       std=__a     ,       data_format=__a     ,       **__a			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = ChannelDimension.FIRST     ,       **__lowerCAmelCase     ,       )  ->  PIL.Image.Image:
        lowercase__    :   Union[str, Any]						= do_resize if do_resize is not None else self.do_resize
        lowercase__    :   Optional[int]						= crop_pct if crop_pct is not None else self.crop_pct
        lowercase__    :   Any						= resample if resample is not None else self.resample
        lowercase__    :   Tuple						= do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase__    :   List[Any]						= do_rescale if do_rescale is not None else self.do_rescale
        lowercase__    :   Dict						= rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__    :   Optional[int]						= do_normalize if do_normalize is not None else self.do_normalize
        lowercase__    :   int						= image_mean if image_mean is not None else self.image_mean
        lowercase__    :   str						= image_std if image_std is not None else self.image_std

        lowercase__    :   int						= size if size is not None else self.size
        lowercase__    :   Optional[int]						= get_size_dict(__a     ,       default_to_square=__a			)
        lowercase__    :   Tuple						= crop_size if crop_size is not None else self.crop_size
        lowercase__    :   Tuple						= get_size_dict(__a     ,       param_name='''crop_size'''			)

        lowercase__    :   Dict						= 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 or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

        if do_center_crop and crop_pct is None:
         raise ValueError('''Crop_pct 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.
        lowercase__    :   int						= [to_numpy_array(__a			) for image in images]

        if do_resize:
         lowercase__    :   Dict						= [self.resize(image=__a     ,       size=__a     ,       crop_pct=__a     ,       resample=__a			) for image in images]

        if do_center_crop:
         lowercase__    :   Union[str, Any]						= [self.center_crop(image=__a     ,       size=__a			) for image in images]

        if do_rescale:
         lowercase__    :   int						= [self.rescale(image=__a     ,       scale=__a			) for image in images]

        if do_normalize:
         lowercase__    :   Union[str, Any]						= [self.normalize(image=__a     ,       mean=__a     ,       std=__a			) for image in images]

        lowercase__    :   List[Any]						= [to_channel_dimension_format(__a     ,       __a			) for image in images]

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


'''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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   None


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.exp(t * -1_2.0					)

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

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





class       UpperCAmelCase  (						a__   ,						a__ ):





       '''simple docstring'''



       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = "fixed_small_log"     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "squaredcos_cap_v2"     ,       )  ->  Optional[int]:
        if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

        lowercase__    :   Union[str, Any]						= betas_for_alpha_bar(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= 1.0 - self.betas
        lowercase__    :   int						= torch.cumprod(self.alphas     ,       dim=0			)
        lowercase__    :   str						= torch.tensor(1.0			)

        # standard deviation of the initial noise distribution
        lowercase__    :   Optional[Any]						= 1.0

        # setable values
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   List[Any]						= torch.from_numpy(np.arange(0     ,       __lowerCAmelCase			)[::-1].copy()			)

        lowercase__    :   Tuple						= variance_type

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  torch.FloatTensor:
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps
        lowercase__    :   List[str]						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowercase__    :   List[str]						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
        lowercase__    :   str						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Tuple:
        if prev_timestep is None:
         lowercase__    :   Any						= t - 1

        lowercase__    :   Any						= self.alphas_cumprod[t]
        lowercase__    :   List[Any]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   str						= 1 - alpha_prod_t
        lowercase__    :   int						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Tuple						= self.betas[t]
        else:
         lowercase__    :   Dict						= 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
        lowercase__    :   Any						= beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
         lowercase__    :   Union[str, Any]						= self.config.variance_type

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

         lowercase__    :   Tuple						= (predicted_variance + 1) / 2
         lowercase__    :   Dict						= frac * max_log + (1 - frac) * min_log

        return variance

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase = True     ,       )  ->  Union[UnCLIPSchedulerOutput, Tuple]:
        lowercase__    :   Tuple						= timestep

        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         lowercase__			,							lowercase__    :   str						= torch.split(__lowerCAmelCase     ,       sample.shape[1]     ,       dim=1			)
        else:
         lowercase__    :   Dict						= None

        # 1. compute alphas, betas
        if prev_timestep is None:
         lowercase__    :   int						= t - 1

        lowercase__    :   Optional[int]						= self.alphas_cumprod[t]
        lowercase__    :   Optional[int]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   Optional[int]						= 1 - alpha_prod_t
        lowercase__    :   List[Any]						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Optional[int]						= self.betas[t]
         lowercase__    :   Optional[Any]						= self.alphas[t]
        else:
         lowercase__    :   Any						= 1 - alpha_prod_t / alpha_prod_t_prev
         lowercase__    :   Optional[int]						= 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":
         lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
         lowercase__    :   Dict						= 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:
         lowercase__    :   List[Any]						= torch.clamp(
             __lowerCAmelCase     ,       -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
        lowercase__    :   Optional[Any]						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowercase__    :   Tuple						= 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
        lowercase__    :   List[Any]						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowercase__    :   List[Any]						= 0
        if t > 0:
         lowercase__    :   Dict						= randn_tensor(
             model_output.shape     ,       dtype=model_output.dtype     ,       generator=__lowerCAmelCase     ,       device=model_output.device			)

         lowercase__    :   Union[str, Any]						= self._get_variance(
             __lowerCAmelCase     ,       predicted_variance=__lowerCAmelCase     ,       prev_timestep=__lowerCAmelCase     ,       )

         if self.variance_type == "fixed_small_log":
          lowercase__    :   List[Any]						= variance
         elif self.variance_type == "learned_range":
          lowercase__    :   int						= (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.'''			)

         lowercase__    :   List[str]						= variance * variance_noise

        lowercase__    :   Union[str, Any]						= pred_prev_sample + variance

        if not return_dict:
         return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase     ,       pred_original_sample=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        lowercase__    :   Tuple						= self.alphas_cumprod.to(device=original_samples.device     ,       dtype=original_samples.dtype			)
        lowercase__    :   str						= timesteps.to(original_samples.device			)

        lowercase__    :   Union[str, Any]						= alphas_cumprod[timesteps] ** 0.5
        lowercase__    :   List[str]						= sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   List[str]						= sqrt_alpha_prod.unsqueeze(-1			)

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

        lowercase__    :   Optional[int]						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

