



"""simple docstring"""
from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
   import torch


_UpperCAmelCase   =	logging.get_logger(__name__)
class     a		(     __a				):
  UpperCamelCase		: str     =    ["""pixel_values"""]



  def __init__(  self       :    Optional[int]				,						lowerCAmelCase       :    bool = True				,						lowerCAmelCase       :    Optional[Dict[str, int]] = None				,						lowerCAmelCase       :    PILImageResampling = PILImageResampling.BILINEAR				,						lowerCAmelCase       :    bool = True				,						lowerCAmelCase       :    Dict[str, int] = None				,						lowerCAmelCase       :    bool = True				,						lowerCAmelCase       :    Union[int, float] = 1 / 255				,						lowerCAmelCase       :    bool = True				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						**lowerCAmelCase       :    Union[str, Any]				,						)   ->    str:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=size if size is not None else {"""shortest_edge""": 256}
     SCREAMING_SNAKE_CASE_:       Dict   	=get_size_dict(lowerCAmelCase				,						default_to_square=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       str   	=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
     SCREAMING_SNAKE_CASE_:       Dict   	=get_size_dict(lowerCAmelCase				,						param_name="""crop_size"""						)
     SCREAMING_SNAKE_CASE_:       Dict   	=do_resize
     SCREAMING_SNAKE_CASE_:       Tuple   	=size
     SCREAMING_SNAKE_CASE_:       List[Any]   	=resample
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=do_center_crop
     SCREAMING_SNAKE_CASE_:       List[str]   	=crop_size
     SCREAMING_SNAKE_CASE_:       List[str]   	=do_rescale
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=rescale_factor
     SCREAMING_SNAKE_CASE_:       int   	=do_normalize
     SCREAMING_SNAKE_CASE_:       str   	=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE_:       str   	=image_std if image_std is not None else IMAGENET_STANDARD_STD



  def 		lowerCamelCase__				(  self       :    Union[str, Any]				,						lowerCAmelCase       :    np.ndarray				,						lowerCAmelCase       :    Dict[str, int]				,						lowerCAmelCase       :    PILImageResampling = PILImageResampling.BICUBIC				,						lowerCAmelCase       :    Optional[Union[str, ChannelDimension]] = None				,						**lowerCAmelCase       :    Any				,						)   ->    Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=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()}'''						)
     SCREAMING_SNAKE_CASE_:       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 		lowerCamelCase__				(  self       :    Union[str, Any]				,						lowerCAmelCase       :    np.ndarray				,						lowerCAmelCase       :    Dict[str, int]				,						lowerCAmelCase       :    Optional[Union[str, ChannelDimension]] = None				,						**lowerCAmelCase       :    int				,						)   ->    Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=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` and `width`. Got {size.keys()}'''						)
     return center_crop(lowerCAmelCase				,						size=(size["""height"""], size["""width"""])				,						data_format=lowerCAmelCase				,						**lowerCAmelCase						)



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


     '''simple docstring'''




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



  def 		lowerCamelCase__				(  self       :    Optional[int]				,						lowerCAmelCase       :    np.ndarray				,						lowerCAmelCase       :    Union[float, List[float]]				,						lowerCAmelCase       :    Union[float, List[float]]				,						lowerCAmelCase       :    Optional[Union[str, ChannelDimension]] = None				,						**lowerCAmelCase       :    Union[str, Any]				,						)   ->    List[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__				(  self       :    Optional[int]				,						lowerCAmelCase       :    ImageInput				,						lowerCAmelCase       :    Optional[bool] = None				,						lowerCAmelCase       :    Dict[str, int] = None				,						lowerCAmelCase       :    PILImageResampling = None				,						lowerCAmelCase       :    bool = None				,						lowerCAmelCase       :    Dict[str, int] = None				,						lowerCAmelCase       :    Optional[bool] = None				,						lowerCAmelCase       :    Optional[float] = None				,						lowerCAmelCase       :    Optional[bool] = None				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						lowerCAmelCase       :    Optional[Union[str, TensorType]] = None				,						lowerCAmelCase       :    Union[str, ChannelDimension] = ChannelDimension.FIRST				,						**lowerCAmelCase       :    Dict				,						)   ->    Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE_:       Any   	=size if size is not None else self.size
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=get_size_dict(lowerCAmelCase				,						default_to_square=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       str   	=resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE_:       int   	=crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE_:       str   	=get_size_dict(lowerCAmelCase				,						param_name="""crop_size"""						)
     SCREAMING_SNAKE_CASE_:       int   	=do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE_:       Tuple   	=rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE_:       str   	=do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE_:       Dict   	=image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE_:       Dict   	=image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE_:       List[str]   	=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."""						)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=[to_numpy_array(lowerCAmelCase						) for image in images]

     if do_resize:
        SCREAMING_SNAKE_CASE_:       List[Any]   	=[self.resize(image=lowerCAmelCase				,						size=lowerCAmelCase				,						resample=lowerCAmelCase						) for image in images]

     if do_center_crop:
        SCREAMING_SNAKE_CASE_:       Optional[int]   	=[self.center_crop(image=lowerCAmelCase				,						size=lowerCAmelCase						) for image in images]

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

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

     SCREAMING_SNAKE_CASE_:       List[Any]   	=[to_channel_dimension_format(lowerCAmelCase				,						lowerCAmelCase						) for image in images]

     SCREAMING_SNAKE_CASE_:       Tuple   	={"""pixel_values""": images}
     return BatchFeature(data=lowerCAmelCase				,						tensor_type=lowerCAmelCase						)





  def 		lowerCamelCase__				(  self       :    Tuple				,						lowerCAmelCase       :    str				,						lowerCAmelCase       :    List[Tuple] = None						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
        if len(lowerCAmelCase						) != len(lowerCAmelCase						):
           raise ValueError(
               """Make sure that you pass in as many target sizes as the batch dimension of the logits"""						)

        if is_torch_tensor(lowerCAmelCase						):
           SCREAMING_SNAKE_CASE_:       List[Any]   	=target_sizes.numpy()

        SCREAMING_SNAKE_CASE_:       str   	=[]

        for idx in range(len(lowerCAmelCase						)						):
           SCREAMING_SNAKE_CASE_:       int   	=torch.nn.functional.interpolate(
               logits[idx].unsqueeze(dim=0						)				,						size=target_sizes[idx]				,						mode="""bilinear"""				,						align_corners=lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:       int   	=resized_logits[0].argmax(dim=0						)
           semantic_segmentation.append(lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:       Any   	=logits.argmax(dim=1						)
        SCREAMING_SNAKE_CASE_:       List[str]   	=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

     return semantic_segmentation









'''simple docstring'''







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class 			_a  (	__a ):
       __a				:     str   				=					["""vqvae"""]
       def __init__(	self						:	str   ,				lowercase						:	AutoencoderKL   ,				lowercase						:	UNetaDConditionModel   ,				lowercase						:	Mel   ,				lowercase						:	Union[DDIMScheduler, DDPMScheduler]   ,				):



           '''simple docstring'''

           super().__init__()
           self.register_modules(unet=lowercase   ,				scheduler=lowercase   ,				mel=lowercase   ,				vqvae=lowercase				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return 50 if isinstance(self.scheduler   ,				lowercase				) else 1_000
       @torch.no_grad()
       def __call__(	self						:	Optional[Any]   ,				lowercase						:	int = 1   ,				lowercase						:	str = None   ,				lowercase						:	np.ndarray = None   ,				lowercase						:	int = 0   ,				lowercase						:	int = 0   ,				lowercase						:	int = None   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	Tuple=True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		steps or self.get_default_steps()
           self.scheduler.set_timesteps(lowercase				)
           UpperCAmelCase			       =		step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size				) == int:
               UpperCAmelCase			       =		(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
               UpperCAmelCase			       =		randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )   ,				generator=lowercase   ,				device=self.device   ,				)
           UpperCAmelCase			       =		noise
           UpperCAmelCase			       =		None

           if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		self.mel.audio_slice_to_image(lowercase				)
               UpperCAmelCase			       =		np.frombuffer(input_image.tobytes()   ,				dtype='''uint8'''				).reshape(
                   (input_image.height, input_image.width)				)
               UpperCAmelCase			       =		(input_image / 255) * 2 - 1
               UpperCAmelCase			       =		torch.tensor(input_image[np.newaxis, :, :]   ,				dtype=torch.float				).to(self.device				)

               if self.vqvae is not None:
                   UpperCAmelCase			       =		self.vqvae.encode(torch.unsqueeze(lowercase   ,				0				)				).latent_dist.sample(
                       generator=lowercase				)[0]
                   UpperCAmelCase			       =		self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                   UpperCAmelCase			       =		self.scheduler.add_noise(lowercase   ,				lowercase   ,				self.scheduler.timesteps[start_step - 1]				)

               UpperCAmelCase			       =		(
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               UpperCAmelCase			       =		int(mask_start_secs * pixels_per_second				)
               UpperCAmelCase			       =		int(mask_end_secs * pixels_per_second				)
               UpperCAmelCase			       =		self.scheduler.add_noise(lowercase   ,				lowercase   ,				torch.tensor(self.scheduler.timesteps[start_step:]				)				)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
               if isinstance(self.unet   ,				lowercase				):
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase   ,				lowercase				)['''sample''']
               else:
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase				)['''sample''']

               if isinstance(self.scheduler   ,				lowercase				):
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				eta=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']
               else:
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']

               if mask is not None:
                   if mask_start > 0:
                       UpperCAmelCase			       =		mask[:, step, :, :mask_start]
                   if mask_end > 0:
                       UpperCAmelCase			       =		mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
               # 0.18215 was scaling factor used in training to ensure unit variance
               UpperCAmelCase			       =		1 / self.vqvae.config.scaling_factor * images
               UpperCAmelCase			       =		self.vqvae.decode(lowercase				)['''sample''']

           UpperCAmelCase			       =		(images / 2 + 0.5).clamp(0   ,				1				)
           UpperCAmelCase			       =		images.cpu().permute(0   ,				2   ,				3   ,				1				).numpy()
           UpperCAmelCase			       =		(images * 255).round().astype('''uint8'''				)
           UpperCAmelCase			       =		list(
               (Image.fromarray(_[:, :, 0]				) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(lowercase   ,				mode='''RGB'''				).convert('''L'''				) for _ in images)				)

           UpperCAmelCase			       =		[self.mel.image_to_audio(lowercase				) for _ in images]
           if not return_dict:
               return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(lowercase				)[:, np.newaxis, :]				)   ,				**ImagePipelineOutput(lowercase				)				)
       @torch.no_grad()
       def     A      (	self						:	Dict   ,				lowercase						:	List[Image.Image]   ,				lowercase						:	int = 50				):



           '''simple docstring'''

           assert isinstance(self.scheduler   ,				lowercase				)
           self.scheduler.set_timesteps(lowercase				)
           UpperCAmelCase			       =		np.array(
               [np.frombuffer(image.tobytes()   ,				dtype='''uint8'''				).reshape((1, image.height, image.width)				) for image in images]				)
           UpperCAmelCase			       =		(sample / 255) * 2 - 1
           UpperCAmelCase			       =		torch.Tensor(lowercase				).to(self.device				)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,				(0,)				)				):
               UpperCAmelCase			       =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
               UpperCAmelCase			       =		self.scheduler.alphas_cumprod[t]
               UpperCAmelCase			       =		(
                   self.scheduler.alphas_cumprod[prev_timestep]
                   if prev_timestep >= 0
                   else self.scheduler.final_alpha_cumprod
               )
               UpperCAmelCase			       =		1 - alpha_prod_t
               UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase				)['''sample''']
               UpperCAmelCase			       =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
               UpperCAmelCase			       =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
               UpperCAmelCase			       =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample







       @staticmethod
       def     A      (	lowercase						:	torch.Tensor   ,				lowercase						:	torch.Tensor   ,				lowercase						:	float				):



           '''simple docstring'''

           UpperCAmelCase			       =		acos(torch.dot(torch.flatten(lowercase				)   ,				torch.flatten(lowercase				)				) / torch.norm(lowercase				) / torch.norm(lowercase				)				)
           return sin((1 - alpha) * theta				) * xa / sin(lowercase				) + sin(alpha * theta				) * xa / sin(lowercase				)




