






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       (					lowercase__  ):



      """simple docstring"""


      a    :					int							=      ['vqvae']
      def __init__( self   :     str						, __lowercase   :     List[Any]						, __lowercase   :     str						, __lowercase   :     List[Any]						, __lowercase   :     int						, )				-> Union[str, Any]:
        super().__init__()
        self.register_modules(unet=__lowercase						, scheduler=__lowercase						, mel=__lowercase						, vqvae=__lowercase   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Union[str, Any]:
        return 50 if isinstance(self.scheduler						, __lowercase   ) else 1000
      @torch.no_grad()
      def __call__( self   :     str						, __lowercase   :     List[str] = 1						, __lowercase   :     int = None						, __lowercase   :     List[Any] = None						, __lowercase   :     Tuple = 0						, __lowercase   :     Optional[Any] = 0						, __lowercase   :     Tuple = None						, __lowercase   :     Union[str, Any] = None						, __lowercase   :     List[Any] = 0						, __lowercase   :     Union[str, Any] = 0						, __lowercase   :     Dict = None						, __lowercase   :     Union[str, Any] = 0						, __lowercase   :     int = None						, __lowercase   :     Optional[int] = None						, __lowercase   :     Optional[int]=True						, )				-> int:
        __UpperCAmelCase				:      Dict	 =    steps or self.get_default_steps()
        self.scheduler.set_timesteps(__lowercase   )
        __UpperCAmelCase				:      Dict	 =    step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size   ) == int:
          __UpperCAmelCase				:      Optional[int]	 =    (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
          __UpperCAmelCase				:      int	 =    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				:      Tuple	 =    noise
        __UpperCAmelCase				:      List[str]	 =    None

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

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

          if start_step > 0:
            __UpperCAmelCase				:      Any	 =    self.scheduler.add_noise(__lowercase						, __lowercase						, self.scheduler.timesteps[start_step - 1]   )

          __UpperCAmelCase				:      Tuple	 =    (
              self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
          )
          __UpperCAmelCase				:      Union[str, Any]	 =    int(mask_start_secs * pixels_per_second   )
          __UpperCAmelCase				:      Tuple	 =    int(mask_end_secs * pixels_per_second   )
          __UpperCAmelCase				:      Any	 =    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				:      List[Any]	 =    self.unet(__lowercase						, __lowercase						, __lowercase   )["sample"]
          else:
            __UpperCAmelCase				:      Tuple	 =    self.unet(__lowercase						, __lowercase   )["sample"]

          if isinstance(self.scheduler						, __lowercase   ):
            __UpperCAmelCase				:      Dict	 =    self.scheduler.step(
                model_output=__lowercase						, timestep=__lowercase						, sample=__lowercase						, eta=__lowercase						, generator=__lowercase						, )["prev_sample"]
          else:
            __UpperCAmelCase				:      Any	 =    self.scheduler.step(
                model_output=__lowercase						, timestep=__lowercase						, sample=__lowercase						, generator=__lowercase						, )["prev_sample"]

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

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

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

        __UpperCAmelCase				:      Dict	 =    [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       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     Tuple						, __lowercase   :     str = 50   )				-> Tuple:
        assert isinstance(self.scheduler						, __lowercase   )
        self.scheduler.set_timesteps(__lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    np.array(
            [np.frombuffer(image.tobytes()						, dtype="""uint8"""   ).reshape((1, image.height, image.width)   ) for image in images]   )
        __UpperCAmelCase				:      List[str]	 =    (sample / 255) * 2 - 1
        __UpperCAmelCase				:      List[Any]	 =    torch.Tensor(__lowercase   ).to(self.device   )

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

        return sample




      @staticmethod
      def       UpperCAmelCase ( __lowercase   :     str						, __lowercase   :     Dict						, __lowercase   :     Any   )				-> str:
        __UpperCAmelCase				:      Optional[Any]	 =    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   )






'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'beit'


     def __init__(   self			,     lowerCamelCase=8192			,     lowerCamelCase=768			,     lowerCamelCase=12			,     lowerCamelCase=12			,     lowerCamelCase=3072			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.0			,     lowerCamelCase=0.0			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=224			,     lowerCamelCase=16			,     lowerCamelCase=3			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=True			,     lowerCamelCase=[3, 5, 7, 11]			,     lowerCamelCase=[1, 2, 3, 6]			,     lowerCamelCase=True			,     lowerCamelCase=0.4			,     lowerCamelCase=256			,     lowerCamelCase=1			,     lowerCamelCase=False			,     lowerCamelCase=255			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A	:       int      =							vocab_size
          __A	:       Tuple      =							hidden_size
          __A	:       List[str]      =							num_hidden_layers
          __A	:       Optional[int]      =							num_attention_heads
          __A	:       Dict      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       str      =							hidden_dropout_prob
          __A	:       Dict      =							attention_probs_dropout_prob
          __A	:       int      =							initializer_range
          __A	:       List[str]      =							layer_norm_eps

          __A	:       str      =							image_size
          __A	:       Optional[int]      =							patch_size
          __A	:       List[Any]      =							num_channels
          __A	:       List[str]      =							use_mask_token
          __A	:       Union[str, Any]      =							use_absolute_position_embeddings
          __A	:       Optional[int]      =							use_relative_position_bias
          __A	:       int      =							use_shared_relative_position_bias
          __A	:       int      =							layer_scale_init_value
          __A	:       Union[str, Any]      =							drop_path_rate
          __A	:       Dict      =							use_mean_pooling
          # decode head attributes (semantic segmentation)
          __A	:       List[str]      =							out_indices
          __A	:       str      =							pool_scales
          # auxiliary head attributes (semantic segmentation)
          __A	:       List[str]      =							use_auxiliary_head
          __A	:       Tuple      =							auxiliary_loss_weight
          __A	:       Dict      =							auxiliary_channels
          __A	:       Tuple      =							auxiliary_num_convs
          __A	:       List[str]      =							auxiliary_concat_input
          __A	:       int      =							semantic_loss_ignore_index







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				version.parse('1.11' )


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return 1E-4

