"""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							(				__UpperCAmelCase	):
     _UpperCamelCase							:    int	      =	["""vqvae"""]






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()
       self.register_modules(unet=UpperCAmelCase_       ,  scheduler=UpperCAmelCase_       ,  mel=UpperCAmelCase_       ,  vqvae=UpperCAmelCase_						)






     def 					__A						(	self						):
       return 50 if isinstance(self.scheduler       ,  UpperCAmelCase_						) else 1000






     @torch.no_grad()
     def __call__(	self       ,  a__ = 1       ,  a__ = None       ,  a__ = None       ,  a__ = 0       ,  a__ = 0       ,  a__ = None       ,  a__ = None       ,  a__ = 0       ,  a__ = 0       ,  a__ = None       ,  a__ = 0       ,  a__ = None       ,  a__ = None       ,  a__=True       ,  ):
       _lowerCAmelCase		: Tuple						    =  steps or self.get_default_steps()
       self.scheduler.set_timesteps(UpperCAmelCase_						)
       _lowerCAmelCase		: str						    =  step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size						) == int:
         _lowerCAmelCase		: Tuple						    =  (self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
         _lowerCAmelCase		: List[Any]						    =  randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )       ,  generator=UpperCAmelCase_       ,  device=self.device       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  noise
       _lowerCAmelCase		: List[str]						    =  None

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

         if self.vqvae is not None:
           _lowerCAmelCase		: Union[str, Any]						    =  self.vqvae.encode(torch.unsqueeze(UpperCAmelCase_       ,  0						)						).latent_dist.sample(
               generator=UpperCAmelCase_						)[0]
           _lowerCAmelCase		: Any						    =  self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
           _lowerCAmelCase		: Tuple						    =  self.scheduler.add_noise(UpperCAmelCase_       ,  UpperCAmelCase_       ,  self.scheduler.timesteps[start_step - 1]						)

         _lowerCAmelCase		: Dict						    =  (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         _lowerCAmelCase		: Any						    =  int(mask_start_secs * pixels_per_second						)
         _lowerCAmelCase		: int						    =  int(mask_end_secs * pixels_per_second						)
         _lowerCAmelCase		: Union[str, Any]						    =  self.scheduler.add_noise(UpperCAmelCase_       ,  UpperCAmelCase_       ,  torch.tensor(self.scheduler.timesteps[start_step:]						)						)

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
         if isinstance(self.unet       ,  UpperCAmelCase_						):
           _lowerCAmelCase		: Optional[Any]						    =  self.unet(UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_						)["""sample"""]
         else:
           _lowerCAmelCase		: List[Any]						    =  self.unet(UpperCAmelCase_       ,  UpperCAmelCase_						)["""sample"""]

         if isinstance(self.scheduler       ,  UpperCAmelCase_						):
           _lowerCAmelCase		: int						    =  self.scheduler.step(
               model_output=UpperCAmelCase_       ,  timestep=UpperCAmelCase_       ,  sample=UpperCAmelCase_       ,  eta=UpperCAmelCase_       ,  generator=UpperCAmelCase_       ,  )["""prev_sample"""]
         else:
           _lowerCAmelCase		: Tuple						    =  self.scheduler.step(
               model_output=UpperCAmelCase_       ,  timestep=UpperCAmelCase_       ,  sample=UpperCAmelCase_       ,  generator=UpperCAmelCase_       ,  )["""prev_sample"""]

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

       if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         _lowerCAmelCase		: List[str]						    =  1 / self.vqvae.config.scaling_factor * images
         _lowerCAmelCase		: Optional[Any]						    =  self.vqvae.decode(UpperCAmelCase_						)["""sample"""]

       _lowerCAmelCase		: Union[str, Any]						    =  (images / 2 + 0.5).clamp(0       ,  1						)
       _lowerCAmelCase		: Optional[Any]						    =  images.cpu().permute(0       ,  2       ,  3       ,  1						).numpy()
       _lowerCAmelCase		: List[Any]						    =  (images * 255).round().astype("""uint8"""						)
       _lowerCAmelCase		: Union[str, Any]						    =  list(
           (Image.fromarray(_[:, :, 0]						) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(UpperCAmelCase_       ,  mode="""RGB"""						).convert("""L"""						) for _ in images)						)

       _lowerCAmelCase		: List[str]						    =  [self.mel.image_to_audio(UpperCAmelCase_						) for _ in images]
       if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(UpperCAmelCase_						)[:, np.newaxis, :]						)       ,  **ImagePipelineOutput(UpperCAmelCase_						)						)






     @torch.no_grad()
     def 					__A						(	self       ,  a__       ,  a__ = 50						):
       assert isinstance(self.scheduler       ,  UpperCAmelCase_						)
       self.scheduler.set_timesteps(UpperCAmelCase_						)
       _lowerCAmelCase		: Optional[int]						    =  np.array(
           [np.frombuffer(image.tobytes()       ,  dtype="""uint8"""						).reshape((1, image.height, image.width)						) for image in images]						)
       _lowerCAmelCase		: Optional[int]						    =  (sample / 255) * 2 - 1
       _lowerCAmelCase		: Tuple						    =  torch.Tensor(UpperCAmelCase_						).to(self.device						)

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps       ,  (0,)						)						):
         _lowerCAmelCase		: Any						    =  t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         _lowerCAmelCase		: List[Any]						    =  self.scheduler.alphas_cumprod[t]
         _lowerCAmelCase		: List[Any]						    =  (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         _lowerCAmelCase		: List[Any]						    =  1 - alpha_prod_t
         _lowerCAmelCase		: Optional[Any]						    =  self.unet(UpperCAmelCase_       ,  UpperCAmelCase_						)["""sample"""]
         _lowerCAmelCase		: Union[str, Any]						    =  (1 - alpha_prod_t_prev) ** 0.5 * model_output
         _lowerCAmelCase		: str						    =  (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         _lowerCAmelCase		: Union[str, Any]						    =  sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample






     @staticmethod
     def 					__A						(	a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: int						    =  acos(torch.dot(torch.flatten(UpperCAmelCase_						)       ,  torch.flatten(UpperCAmelCase_						)						) / torch.norm(UpperCAmelCase_						) / torch.norm(UpperCAmelCase_						)						)
       return sin((1 - alpha) * theta						) * xa / sin(UpperCAmelCase_						) + sin(alpha * theta						) * xa / sin(UpperCAmelCase_						)





"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple			) ->   Dict:
  _lowerCAmelCase		: List[str]						    =  int(_lowerCamelCase			)
  assert noofclusters < len(_lowerCamelCase			)

  # Find out the dimensionality
  _lowerCAmelCase		: Any						    =  len(vectors[0]			)

  # Will help select random centroids from among the available vectors
  _lowerCAmelCase		: Any						    =  list(range(len(_lowerCamelCase			)			)			)
  shuffle(_lowerCamelCase			)

  # GRAPH OF COMPUTATION
  # We initialize a new graph and set it as the default during each run
  # of this algorithm. This ensures that as this function is called
  # multiple times, the default graph doesn't keep getting crowded with
  # unused ops and Variables from previous function calls.

  _lowerCAmelCase		: List[Any]						    =  tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    _lowerCAmelCase		: str						    =  tf.Session()

    ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

    ##First lets ensure we have a Variable vector for each centroid,
    ##initialized to one of the vectors from the available data points
    _lowerCAmelCase		: List[str]						    =  [
        tf.Variable(vectors[vector_indices[i]]			) for i in range(_lowerCamelCase			)
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float64"""    ,[dim]			)
    _lowerCAmelCase		: Optional[int]						    =  []
    for centroid in centroids:
      cent_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Variables for cluster assignments of individual vectors(initialized
    ##to 0 at first)
    _lowerCAmelCase		: Dict						    =  [tf.Variable(0			) for i in range(len(_lowerCamelCase			)			)]
    ##These nodes will assign an assignment Variable the appropriate
    ##value
    _lowerCAmelCase		: List[Any]						    =  tf.placeholder("""int32"""			)
    _lowerCAmelCase		: Any						    =  []
    for assignment in assignments:
      cluster_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float"""    ,[None, dim]			)
    # The Node/op takes the input and computes a mean along the 0th
    # dimension, i.e. the list of input vectors
    _lowerCAmelCase		: Optional[int]						    =  tf.reduce_mean(_lowerCamelCase    ,0			)

    ##Node for computing Euclidean distances
    # Placeholders for input
    _lowerCAmelCase		: Dict						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: List[Any]						    =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase    ,_lowerCamelCase			)    ,2			)			)			)

    ##This node will figure out which cluster to assign a vector to,
    ##based on Euclidean distances of the vector from the centroids.
    # Placeholder for input
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[noofclusters]			)
    _lowerCAmelCase		: str						    =  tf.argmin(_lowerCamelCase    ,0			)

    ##INITIALIZING STATE VARIABLES

    ##This will help initialization of all Variables defined with respect
    ##to the graph. The Variable-initializer should be defined after
    ##all the Variables have been constructed, so that each of them
    ##will be included in the initialization.
    _lowerCAmelCase		: Optional[Any]						    =  tf.initialize_all_variables()

    # Initialize all variables
    sess.run(_lowerCamelCase			)

    ##CLUSTERING ITERATIONS

    # Now perform the Expectation-Maximization steps of K-Means clustering
    # iterations. To keep things simple, we will only do a set number of
    # iterations, instead of using a Stopping Criterion.
    _lowerCAmelCase		: List[str]						    =  100
    for _ in range(_lowerCamelCase			):
      ##EXPECTATION STEP
      ##Based on the centroid locations till last iteration, compute
      ##the _expected_ centroid assignments.
      # Iterate over each vector
      for vector_n in range(len(_lowerCamelCase			)			):
        _lowerCAmelCase		: int						    =  vectors[vector_n]
        # Compute Euclidean distance between this vector and each
        # centroid. Remember that this list cannot be named
        #'centroid_distances', since that is the input to the
        # cluster assignment node.
        _lowerCAmelCase		: Any						    =  [
            sess.run(_lowerCamelCase    ,feed_dict={va: vect, va: sess.run(_lowerCamelCase			)}			)
            for centroid in centroids
        ]
        # Now use the cluster assignment node, with the distances
        # as the input
        _lowerCAmelCase		: Any						    =  sess.run(
            _lowerCamelCase    ,feed_dict={centroid_distances: distances}			)
        # Now assign the value to the appropriate state variable
        sess.run(
            cluster_assigns[vector_n]    ,feed_dict={assignment_value: assignment}			)

      ##MAXIMIZATION STEP
      # Based on the expected state computed from the Expectation Step,
      # compute the locations of the centroids so as to maximize the
      # overall objective of minimizing within-cluster Sum-of-Squares
      for cluster_n in range(_lowerCamelCase			):
        # Collect all the vectors assigned to this cluster
        _lowerCAmelCase		: List[Any]						    =  [
            vectors[i]
            for i in range(len(_lowerCamelCase			)			)
            if sess.run(assignments[i]			) == cluster_n
        ]
        # Compute new centroid location
        _lowerCAmelCase		: Optional[int]						    =  sess.run(
            _lowerCamelCase    ,feed_dict={mean_input: array(_lowerCamelCase			)}			)
        # Assign value to appropriate variable
        sess.run(
            cent_assigns[cluster_n]    ,feed_dict={centroid_value: new_location}			)

        # Return centroids and assignments
    _lowerCAmelCase		: Optional[int]						    =  sess.run(_lowerCamelCase			)
    _lowerCAmelCase		: List[Any]						    =  sess.run(_lowerCamelCase			)
    return centroids, assignments




