


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case    :   int	    =					logging.get_logger(__name__)


if is_vision_available():
     import PIL







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	['pixel_values']




 def __init__(		self ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  _lowerCamelCase = PILImageResampling.BICUBIC ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  _lowerCamelCase = True ,  _lowerCamelCase = 1 / 255 ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = True ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )
       a						:Optional[int]										=	size if size is not None else {'''shortest_edge''': 224}
       a						:Any										=	get_size_dict(_lowerCamelCase ,  default_to_square=_lowerCamelCase   )
       a						:Optional[Any]										=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       a						:int										=	get_size_dict(_lowerCamelCase ,  default_to_square=_lowerCamelCase ,  param_name='''crop_size'''   )

       a						:Union[str, Any]										=	do_resize
       a						:Optional[int]										=	size
       a						:Optional[Any]										=	resample
       a						:Optional[int]										=	do_center_crop
       a						:Optional[int]										=	crop_size
       a						:Dict										=	do_rescale
       a						:Tuple										=	rescale_factor
       a						:Union[str, Any]										=	do_normalize
       a						:Union[str, Any]										=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       a						:str										=	image_std if image_std is not None else OPENAI_CLIP_STD
       a						:Any										=	do_convert_rgb




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = PILImageResampling.BICUBIC ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       a						:Tuple										=	get_size_dict(_lowerCamelCase ,  default_to_square=_lowerCamelCase   )
       if "shortest_edge" not in size:
             raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
       a						:int										=	get_resize_output_image_size(_lowerCamelCase ,  size=size['''shortest_edge'''] ,  default_to_square=_lowerCamelCase   )
       return resize(_lowerCamelCase ,  size=_lowerCamelCase ,  resample=_lowerCamelCase ,  data_format=_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       a						:str										=	get_size_dict(_lowerCamelCase   )
       if "height" not in size or "width" not in size:
             raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''   )
       return center_crop(_lowerCamelCase ,  size=(size['''height'''], size['''width''']) ,  data_format=_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       return rescale(_lowerCamelCase ,  scale=_lowerCamelCase ,  data_format=_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       return normalize(_lowerCamelCase ,  mean=_lowerCamelCase ,  std=_lowerCamelCase ,  data_format=_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		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 ,  ):
       a						:Optional[int]										=	do_resize if do_resize is not None else self.do_resize
       a						:Optional[Any]										=	size if size is not None else self.size
       a						:Tuple										=	get_size_dict(_lowerCamelCase ,  param_name='''size''' ,  default_to_square=_lowerCamelCase   )
       a						:Optional[Any]										=	resample if resample is not None else self.resample
       a						:str										=	do_center_crop if do_center_crop is not None else self.do_center_crop
       a						:Union[str, Any]										=	crop_size if crop_size is not None else self.crop_size
       a						:Union[str, Any]										=	get_size_dict(_lowerCamelCase ,  param_name='''crop_size''' ,  default_to_square=_lowerCamelCase   )
       a						:Union[str, Any]										=	do_rescale if do_rescale is not None else self.do_rescale
       a						:List[Any]										=	rescale_factor if rescale_factor is not None else self.rescale_factor
       a						:List[Any]										=	do_normalize if do_normalize is not None else self.do_normalize
       a						:List[str]										=	image_mean if image_mean is not None else self.image_mean
       a						:List[str]										=	image_std if image_std is not None else self.image_std
       a						:List[str]										=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       a						:List[Any]										=	make_list_of_images(_lowerCamelCase   )

       if not valid_images(_lowerCamelCase   ):
             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.'''   )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             a						:Union[str, Any]										=	[convert_to_rgb(_lowerCamelCase   ) for image in images]

       # All transformations expect numpy arrays.
       a						:List[Any]										=	[to_numpy_array(_lowerCamelCase   ) for image in images]

       if do_resize:
             a						:Optional[int]										=	[self.resize(image=_lowerCamelCase ,  size=_lowerCamelCase ,  resample=_lowerCamelCase   ) for image in images]

       if do_center_crop:
             a						:Dict										=	[self.center_crop(image=_lowerCamelCase ,  size=_lowerCamelCase   ) for image in images]

       if do_rescale:
             a						:Tuple										=	[self.rescale(image=_lowerCamelCase ,  scale=_lowerCamelCase   ) for image in images]

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

       a						:int										=	[to_channel_dimension_format(_lowerCamelCase ,  _lowerCamelCase   ) for image in images]

       a						:int										=	{'''pixel_values''': images}
       return BatchFeature(data=_lowerCamelCase ,  tensor_type=_lowerCamelCase   )









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  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	42
 SCREAMING_SNAKE_CASE__				=	None







def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		List[str]=0.999			,  UpperCAmelCase_						:		str="cosine"			,  ):




      """simple docstring"""





      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(UpperCAmelCase_						:		List[Any]						):
                  return math.exp(t * -12.0						)

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

      a						:Union[str, Any]										=	[]
      for i in range(UpperCAmelCase_						):
            a						:Optional[Any]										=	i / num_diffusion_timesteps
            a						:int										=	(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 		_snake_case  (				_snake_case						,   _snake_case							):




 @register_to_config
 def __init__(		self ,  _lowerCamelCase = 1000 ,  _lowerCamelCase = "fixed_small_log" ,  _lowerCamelCase = True ,  _lowerCamelCase = 1.0 ,  _lowerCamelCase = "epsilon" ,  _lowerCamelCase = "squaredcos_cap_v2" ,  ):
       if beta_schedule != "squaredcos_cap_v2":
             raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''   )

       a						:List[Any]										=	betas_for_alpha_bar(_lowerCamelCase   )

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

       # standard deviation of the initial noise distribution
       a						:List[Any]										=	1.0

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

       a						:List[str]										=	variance_type




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       return sample




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Optional[int]										=	num_inference_steps
       a						:Union[str, Any]										=	(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       a						:int										=	(np.arange(0 ,  _lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
       a						:List[str]										=	torch.from_numpy(_lowerCamelCase   ).to(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None   ):
       if prev_timestep is None:
             a						:Union[str, Any]										=	t - 1

       a						:Dict										=	self.alphas_cumprod[t]
       a						:str										=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a						:Optional[Any]										=	1 - alpha_prod_t
       a						:Tuple										=	1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
             a						:int										=	self.betas[t]
       else:
             a						:Tuple										=	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
       a						:str										=	beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
             a						:Optional[int]										=	self.config.variance_type

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

             a						:List[Any]										=	(predicted_variance + 1) / 2
             a						:Dict										=	frac * max_log + (1 - frac) * min_log

       return variance




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase=None ,  _lowerCamelCase = True ,  ):
       a						:Optional[int]										=	timestep

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

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

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

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

       # 6. Add noise
       a						:Dict										=	0
       if t > 0:
             a						:Optional[int]										=	randn_tensor(
                 model_output.shape ,  dtype=model_output.dtype ,  generator=_lowerCamelCase ,  device=model_output.device   )

             a						:Dict										=	self._get_variance(
                 _lowerCamelCase ,  predicted_variance=_lowerCamelCase ,  prev_timestep=_lowerCamelCase ,  )

             if self.variance_type == "fixed_small_log":
                   a						:Optional[int]										=	variance
             elif self.variance_type == "learned_range":
                   a						:Union[str, 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.'''   )

             a						:Optional[int]										=	variance * variance_noise

       a						:List[str]										=	pred_prev_sample + variance

       if not return_dict:
             return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=_lowerCamelCase ,  pred_original_sample=_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  ):
       # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
       a						:List[Any]										=	self.alphas_cumprod.to(device=original_samples.device ,  dtype=original_samples.dtype   )
       a						:Optional[Any]										=	timesteps.to(original_samples.device   )

       a						:Tuple										=	alphas_cumprod[timesteps] ** 0.5
       a						:List[Any]										=	sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape   ) < len(original_samples.shape   ):
             a						:Any										=	sqrt_alpha_prod.unsqueeze(-1   )

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

       a						:str										=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples





