
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


lowerCamelCase__    :		Union[str, Any]          =   logging.get_logger(__name__)


if is_vision_available():
     import PIL



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Dict        =			['pixel_values']


 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = 1 / 2_55					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = True					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      size if size is not None else {"""shortest_edge""": 2_24}
   lowercase__  :   Optional[int]    		=      get_size_dict(SCREAMING_SNAKE_CASE_					,  default_to_square=SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
   lowercase__  :   int    		=      get_size_dict(SCREAMING_SNAKE_CASE_					,  default_to_square=SCREAMING_SNAKE_CASE_					,  param_name="""crop_size""")

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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC					,  SCREAMING_SNAKE_CASE_ = None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Any    		=      get_size_dict(SCREAMING_SNAKE_CASE_					,  default_to_square=SCREAMING_SNAKE_CASE_)
   if "shortest_edge" not in size:
     raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
   lowercase__  :   str    		=      get_resize_output_image_size(SCREAMING_SNAKE_CASE_					,  size=size["""shortest_edge"""]					,  default_to_square=SCREAMING_SNAKE_CASE_)
   return resize(SCREAMING_SNAKE_CASE_					,  size=SCREAMING_SNAKE_CASE_					,  resample=SCREAMING_SNAKE_CASE_					,  data_format=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      get_size_dict(SCREAMING_SNAKE_CASE_)
   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(SCREAMING_SNAKE_CASE_					,  size=(size["""height"""], size["""width"""])					,  data_format=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   return rescale(SCREAMING_SNAKE_CASE_					,  scale=SCREAMING_SNAKE_CASE_					,  data_format=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




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



 def      lowercase__     (					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_ = None					,  SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      do_resize if do_resize is not None else self.do_resize
   lowercase__  :   Optional[Any]    		=      size if size is not None else self.size
   lowercase__  :   Optional[Any]    		=      get_size_dict(SCREAMING_SNAKE_CASE_					,  param_name="""size"""					,  default_to_square=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      resample if resample is not None else self.resample
   lowercase__  :   Union[str, Any]    		=      do_center_crop if do_center_crop is not None else self.do_center_crop
   lowercase__  :   Optional[Any]    		=      crop_size if crop_size is not None else self.crop_size
   lowercase__  :   Union[str, Any]    		=      get_size_dict(SCREAMING_SNAKE_CASE_					,  param_name="""crop_size"""					,  default_to_square=SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      do_rescale if do_rescale is not None else self.do_rescale
   lowercase__  :   str    		=      rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__  :   List[Any]    		=      do_normalize if do_normalize is not None else self.do_normalize
   lowercase__  :   Optional[Any]    		=      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__  :   Optional[int]    		=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   lowercase__  :   Union[str, Any]    		=      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:
     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:
     lowercase__  :   Union[str, Any]    		=      [convert_to_rgb(SCREAMING_SNAKE_CASE_) for image in images]

   # All transformations expect numpy arrays.
   lowercase__  :   Tuple    		=      [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_resize:
     lowercase__  :   int    		=      [self.resize(image=SCREAMING_SNAKE_CASE_					,  size=SCREAMING_SNAKE_CASE_					,  resample=SCREAMING_SNAKE_CASE_) for image in images]

   if do_center_crop:
     lowercase__  :   Tuple    		=      [self.center_crop(image=SCREAMING_SNAKE_CASE_					,  size=SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__  :   List[Any]    		=      [self.rescale(image=SCREAMING_SNAKE_CASE_					,  scale=SCREAMING_SNAKE_CASE_) for image in images]

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

   lowercase__  :   str    		=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) for image in images]

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








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
class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     Optional[torch.FloatTensor]        =			None



class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			2


 @register_to_config
 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = 0.0_2					,  SCREAMING_SNAKE_CASE_ = 1_00					,  SCREAMING_SNAKE_CASE_ = 1.0_0_7					,  SCREAMING_SNAKE_CASE_ = 80					,  SCREAMING_SNAKE_CASE_ = 0.0_5					,  SCREAMING_SNAKE_CASE_ = 50					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      sigma_max

   # setable values
   lowercase__  :   int    		=      None
   lowercase__  :   np.IntTensor    		=      None
   lowercase__  :   torch.FloatTensor    		=      None  # sigma(t_i)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   return sample


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__  :   Dict    		=      num_inference_steps
   lowercase__  :   List[Any]    		=      np.arange(0					,  self.num_inference_steps)[::-1].copy()
   lowercase__  :   Optional[Any]    		=      torch.from_numpy(SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      [
       (
           self.config.sigma_max**2
           * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
       )
       for i in self.timesteps
   ]
   lowercase__  :   int    		=      torch.tensor(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa					,  device=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if self.config.s_min <= sigma <= self.config.s_max:
     lowercase__  :   Optional[Any]    		=      min(self.config.s_churn / self.num_inference_steps					,  2**0.5 - 1)
   else:
     lowercase__  :   Tuple    		=      0

   # sample eps ~ N(0, S_noise^2 * I)
   lowercase__  :   Dict    		=      self.config.s_noise * randn_tensor(sample.shape					,  generator=SCREAMING_SNAKE_CASE_).to(sample.device)
   lowercase__  :   int    		=      sigma + gamma * sigma
   lowercase__  :   Optional[int]    		=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_hat + sigma_hat * model_output
   lowercase__  :   str    		=      (sample_hat - pred_original_sample) / sigma_hat
   lowercase__  :   Optional[Any]    		=      sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_prev + sigma_prev * model_output
   lowercase__  :   Dict    		=      (sample_prev - pred_original_sample) / sigma_prev
   lowercase__  :   Optional[int]    		=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()






