
"""simple docstring"""





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


_a		       =							logging.get_logger(__name__)


if is_vision_available():
    import PIL


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		["""pixel_values"""]







  def __init__(		self				,   lowercase_ = True				,   lowercase_ = None				,   lowercase_ = PILImageResampling.BICUBIC				,   lowercase_ = True				,   lowercase_ = None				,   lowercase_ = True				,   lowercase_ = 1 / 255				,   lowercase_ = True				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = True				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )
     UpperCAmelCase_					:					Dict			     =    size if size is not None else {"shortest_edge": 224}
     UpperCAmelCase_					:					str			     =    get_size_dict(lowercase_				,   default_to_square=lowercase_  )
     UpperCAmelCase_					:					Dict			     =    crop_size if crop_size is not None else {"height": 224, "width": 224}
     UpperCAmelCase_					:					List[Any]			     =    get_size_dict(lowercase_				,   default_to_square=lowercase_				,   param_name="crop_size"  )

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_ = PILImageResampling.BICUBIC				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    get_size_dict(lowercase_				,   default_to_square=lowercase_  )
     if "shortest_edge" not in size:
        raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
     UpperCAmelCase_					:					Optional[Any]			     =    get_resize_output_image_size(lowercase_				,   size=size["shortest_edge"]				,   default_to_square=lowercase_  )
     return resize(lowercase_				,   size=lowercase_				,   resample=lowercase_				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    get_size_dict(lowercase_  )
     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(lowercase_				,   size=(size["height"], size["width"])				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     return rescale(lowercase_				,   scale=lowercase_				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     return normalize(lowercase_				,   mean=lowercase_				,   std=lowercase_				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = ChannelDimension.FIRST				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase_					:					Optional[Any]			     =    size if size is not None else self.size
     UpperCAmelCase_					:					Any			     =    get_size_dict(lowercase_				,   param_name="size"				,   default_to_square=lowercase_  )
     UpperCAmelCase_					:					str			     =    resample if resample is not None else self.resample
     UpperCAmelCase_					:					List[Any]			     =    do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase_					:					Optional[int]			     =    crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase_					:					Union[str, Any]			     =    get_size_dict(lowercase_				,   param_name="crop_size"				,   default_to_square=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase_					:					Union[str, Any]			     =    rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase_					:					Any			     =    do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase_					:					Union[str, Any]			     =    image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase_					:					Any			     =    image_std if image_std is not None else self.image_std
     UpperCAmelCase_					:					List[str]			     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     UpperCAmelCase_					:					Optional[Any]			     =    make_list_of_images(lowercase_  )

     if not valid_images(lowercase_  ):
        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:
        UpperCAmelCase_					:					Optional[int]			     =    [convert_to_rgb(lowercase_  ) for image in images]

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

     if do_resize:
        UpperCAmelCase_					:					str			     =    [self.resize(image=lowercase_				,   size=lowercase_				,   resample=lowercase_  ) for image in images]

     if do_center_crop:
        UpperCAmelCase_					:					List[str]			     =    [self.center_crop(image=lowercase_				,   size=lowercase_  ) for image in images]

     if do_rescale:
        UpperCAmelCase_					:					Optional[int]			     =    [self.rescale(image=lowercase_				,   scale=lowercase_  ) for image in images]

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

     UpperCAmelCase_					:					Dict			     =    [to_channel_dimension_format(lowercase_				,   lowercase_  ) for image in images]

     UpperCAmelCase_					:					Union[str, Any]			     =    {"pixel_values": images}
     return BatchFeature(data=lowercase_				,   tensor_type=lowercase_  )




'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				UNetaDModel
  __UpperCAmelCase				:				KarrasVeScheduler


  def __init__(		self      :     Union[str, Any]							,_a      :     UNetaDModel							,_a      :     KarrasVeScheduler       ):


     '''simple docstring'''






     super().__init__()
     self.register_modules(unet=_a							,scheduler=_a       )


  @torch.no_grad()
  def __call__(		self      :     List[Any]							,_a      :     int = 1							,_a      :     int = 50							,_a      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,_a      :     Optional[str] = "pil"							,_a      :     bool = True							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Any			=   self.unet.config.sample_size
     _a				:	Optional[int]			=   (batch_size, 3, img_size, img_size)

     _a				:	Dict			=   self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     _a				:	Dict			=   randn_tensor(_a							,generator=_a							,device=self.device       ) * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(_a       )

     for t in self.progress_bar(self.scheduler.timesteps       ):
        # here sigma_t == t_i from the paper
        _a				:	Optional[int]			=   self.scheduler.schedule[t]
        _a				:	List[str]			=   self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        _a,  _a				:	List[Any]			=   self.scheduler.add_noise_to_input(_a							,_a							,generator=_a       )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        _a				:	Optional[int]			=   (sigma_hat / 2) * model((sample_hat + 1) / 2							,sigma_hat / 2       ).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        _a				:	Tuple			=   self.scheduler.step(_a							,_a							,_a							,_a       )

        if sigma_prev != 0:
           # 6. Apply 2nd order correction
           # The model inputs and output are adjusted by following eq. (213) in [1].
           _a				:	Optional[int]			=   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,sigma_prev / 2       ).sample
           _a				:	Optional[Any]			=   self.scheduler.step_correct(
               _a							,_a							,_a							,_a							,step_output.prev_sample							,step_output['derivative']							,)
        _a				:	Dict			=   step_output.prev_sample

     _a				:	Tuple			=   (sample / 2 + 0.5).clamp(0							,1       )
     _a				:	Optional[Any]			=   sample.cpu().permute(0							,2							,3							,1       ).numpy()
     if output_type == "pil":
        _a				:	List[str]			=   self.numpy_to_pil(_a       )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_a       )



