
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


__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		List[Any]						     =     ["pixel_values"]



   def __init__(self		:					Optional[int]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					int = 0.9    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					Union[int, float] = 1 / 2_55    ,						snake_case__		:					bool = True    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	None:


          '''simple docstring'''


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

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[float] = None    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Dict    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      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:
                        snake_case       :   Optional[int]							=      int(size["shortest_edge"] / crop_pct )
                 elif "height" in size and "width" in size:
                        if size["height"] == size["width"]:
                               snake_case       :   List[Any]							=      int(size["height"] / crop_pct )
                        else:
                               snake_case       :   Any							=      (int(size["height"] / crop_pct ), int(size["width"] / crop_pct ))
                 else:
                        raise ValueError("Invalid size for resize: {}".format(A_ ) )

                 snake_case       :   Union[str, Any]							=      get_resize_output_image_size(A_    ,						size=A_    ,						default_to_square=A_ )
          else:
                 if "shortest_edge" in size:
                        snake_case       :   int							=      get_resize_output_image_size(A_    ,						size=size["shortest_edge"]    ,						default_to_square=A_ )
                 elif "height" in size and "width" in size:
                        snake_case       :   Optional[Any]							=      (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 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   int							=      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 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[int, float]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					List[Any]    ,						)		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Tuple    ,						)		->	np.ndarray:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					ImageInput    ,						snake_case__		:					bool = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					int = None    ,						snake_case__		:					PILImageResampling = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					float = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					ChannelDimension = ChannelDimension.FIRST    ,						**snake_case__		:					Any    ,						)		->	PIL.Image.Image:


          '''simple docstring'''


          snake_case       :   List[Any]							=      do_resize if do_resize is not None else self.do_resize
          snake_case       :   Optional[int]							=      crop_pct if crop_pct is not None else self.crop_pct
          snake_case       :   Optional[Any]							=      resample if resample is not None else self.resample
          snake_case       :   str							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case       :   Tuple							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   List[Any]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   Any							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   Optional[Any]							=      image_mean if image_mean is not None else self.image_mean
          snake_case       :   str							=      image_std if image_std is not None else self.image_std

          snake_case       :   Optional[Any]							=      size if size is not None else self.size
          snake_case       :   List[Any]							=      get_size_dict(A_    ,						default_to_square=A_ )
          snake_case       :   str							=      crop_size if crop_size is not None else self.crop_size
          snake_case       :   List[Any]							=      get_size_dict(A_    ,						param_name="crop_size" )

          snake_case       :   str							=      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.
          snake_case       :   List[str]							=      [to_numpy_array(A_ ) for image in images]

          if do_resize:
                 snake_case       :   Union[str, Any]							=      [self.resize(image=A_    ,						size=A_    ,						crop_pct=A_    ,						resample=A_ ) for image in images]

          if do_center_crop:
                 snake_case       :   str							=      [self.center_crop(image=A_    ,						size=A_ ) for image in images]

          if do_rescale:
                 snake_case       :   Optional[Any]							=      [self.rescale(image=A_    ,						scale=A_ ) for image in images]

          if do_normalize:
                 snake_case       :   str							=      [self.normalize(image=A_    ,						mean=A_    ,						std=A_ ) for image in images]

          snake_case       :   Dict							=      [to_channel_dimension_format(A_    ,						A_ ) for image in images]

          snake_case       :   List[str]							=      {"pixel_values": images}
          return BatchFeature(data=A_    ,						tensor_type=A_ )











from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  TransformeraDModel						,							A_					:  AutoencoderKL						,							A_					:  KarrasDiffusionSchedulers						,							A_					:  Optional[Dict[int, str]] = None						,							)   ->	str:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_						,							vae=A_						,							scheduler=A_  )

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_		     =    {}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','  ):
                         lowerCamelCase_		     =    int(A_  )
             lowerCamelCase_		     =    dict(sorted(self.labels.items()  )  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, List[str]]  )   ->	List[int]:

       """simple docstring"""






       if not isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    list(A_  )

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(				self					:  Any						,							A_					:  List[int]						,							A_					:  float = 4.0						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    len(A_  )
       lowerCamelCase_		     =    self.transformer.config.sample_size
       lowerCamelCase_		     =    self.transformer.config.in_channels

       lowerCamelCase_		     =    randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)						,							generator=A_						,							device=self.device						,							dtype=self.transformer.dtype						,							)
       lowerCamelCase_		     =    torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

       lowerCamelCase_		     =    torch.tensor(A_						,							device=self.device  ).reshape(-1  )
       lowerCamelCase_		     =    torch.tensor([1000] * batch_size						,							device=self.device  )
       lowerCamelCase_		     =    torch.cat([class_labels, class_null]						,							0  ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             if guidance_scale > 1:
                   lowerCamelCase_		     =    latent_model_input[: len(A_  ) // 2]
                   lowerCamelCase_		     =    torch.cat([half, half]						,							dim=0  )
             lowerCamelCase_		     =    self.scheduler.scale_model_input(A_						,							A_  )

             lowerCamelCase_		     =    t
             if not torch.is_tensor(A_  ):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_		     =    latent_model_input.device.type == 'mps'
                   if isinstance(A_						,							A_  ):
                         lowerCamelCase_		     =    torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_		     =    torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_		     =    torch.tensor([timesteps]						,							dtype=A_						,							device=latent_model_input.device  )
             elif len(timesteps.shape  ) == 0:
                   lowerCamelCase_		     =    timesteps[None].to(latent_model_input.device  )
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_		     =    timesteps.expand(latent_model_input.shape[0]  )
             # predict noise model_output
             lowerCamelCase_		     =    self.transformer(
                 A_						,							timestep=A_						,							class_labels=A_  ).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							len(A_  ) // 2						,							dim=0  )

                   lowerCamelCase_		     =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_		     =    torch.cat([half_eps, half_eps]						,							dim=0  )

                   lowerCamelCase_		     =    torch.cat([eps, rest]						,							dim=1  )

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							A_						,							dim=1  )
             else:
                   lowerCamelCase_		     =    noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_     ,				lowerCamelCase_		     =    latent_model_input.chunk(2						,							dim=0  )
       else:
             lowerCamelCase_		     =    latent_model_input

       lowerCamelCase_		     =    1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_		     =    self.vae.decode(A_  ).sample

       lowerCamelCase_		     =    (samples / 2 + 0.5).clamp(0						,							1  )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_		     =    samples.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()

       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(A_  )

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_  )



