

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase_   							=  logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_   							=  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'
def 	a (     A__      :  Any ,  A__      :  Optional[Any] ,  A__      :  Optional[Any]=8	)       -> int:





      """simple docstring"""



      _lowercase      			=height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _lowercase      			=width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  )    ->					Any:
        '''simple docstring'''






        super().__init__()

        self.register_modules(
            unet=lowerCAmelCase				,  scheduler=lowerCAmelCase				,  movq=lowerCAmelCase				,  )
        _lowercase      			=2 ** (len(self.movq.config.block_out_channels   ) - 1)




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        if latents is None:
              _lowercase      			=randn_tensor(lowerCAmelCase				,  generator=lowerCAmelCase				,  device=lowerCAmelCase				,  dtype=lowerCAmelCase   )
        else:
              if latents.shape != shape:
                    raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''   )
              _lowercase      			=latents.to(lowerCAmelCase   )

        _lowercase      			=latents * scheduler.init_noise_sigma
        return latents




  def     A__	(							self				,  lowerCAmelCase=0   )    ->					Optional[int]:
        '''simple docstring'''






        if is_accelerate_available():
              from accelerate import cpu_offload
        else:
              raise ImportError('Please install accelerate via `pip install accelerate`'   )

        _lowercase      			=torch.device(F'''cuda:{gpu_id}'''   )

        _lowercase      			=[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                    cpu_offload(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase=0   )    ->					str:
        '''simple docstring'''






        if is_accelerate_available() and is_accelerate_version('>='				,  '0.17.0.dev0'   ):
              from accelerate import cpu_offload_with_hook
        else:
              raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'   )

        _lowercase      			=torch.device(F'''cuda:{gpu_id}'''   )

        if self.device.type != "cpu":
              self.to('cpu'				,  silence_dtype_warnings=lowerCAmelCase   )
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        _lowercase      			=None
        for cpu_offloaded_model in [self.unet, self.movq]:
              _lowercase			,			_lowercase      			=cpu_offload_with_hook(lowerCAmelCase				,  lowerCAmelCase				,  prev_module_hook=lowerCAmelCase   )

        # We'll offload the last model manually.
        _lowercase      			=hook




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        if not hasattr(self.unet				,  '_hf_hook'   ):
              return self.device
        for module in self.unet.modules():
              if (
                  hasattr(lowerCAmelCase				,  '_hf_hook'   )
                  and hasattr(module._hf_hook				,  'execution_device'   )
                  and module._hf_hook.execution_device is not None
              ):
                    return torch.device(module._hf_hook.execution_device   )
        return self.device




  @torch.no_grad()
  @replace_example_docstring(lowerCAmelCase   )
  def __call__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 512				,  lowerCAmelCase = 512				,  lowerCAmelCase = 100				,  lowerCAmelCase = 4.0				,  lowerCAmelCase = 1				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = "pil"				,  lowerCAmelCase = True				,  )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self._execution_device

        _lowercase      			=guidance_scale > 1.0

        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=torch.cat(lowerCAmelCase				,  dim=0   )
        _lowercase      			=image_embeds.shape[0] * num_images_per_prompt
        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=torch.cat(lowerCAmelCase				,  dim=0   )

        if do_classifier_free_guidance:
              _lowercase      			=image_embeds.repeat_interleave(lowerCAmelCase				,  dim=0   )
              _lowercase      			=negative_image_embeds.repeat_interleave(lowerCAmelCase				,  dim=0   )

        _lowercase      			=torch.cat([negative_image_embeds, image_embeds]				,  dim=0   ).to(dtype=self.unet.dtype				,  device=lowerCAmelCase   )

        self.scheduler.set_timesteps(lowerCAmelCase				,  device=lowerCAmelCase   )
        _lowercase      			=self.scheduler.timesteps

        _lowercase      			=self.unet.config.in_channels

        _lowercase			,			_lowercase      			=downscale_height_and_width(lowerCAmelCase				,  lowerCAmelCase				,  self.movq_scale_factor   )

        # create initial latent
        _lowercase      			=self.prepare_latents(
            (batch_size, num_channels_latents, height, width)				,  image_embeds.dtype				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  self.scheduler				,  )

        for i, t in enumerate(self.progress_bar(lowerCAmelCase   )   ):
              # expand the latents if we are doing classifier free guidance
              _lowercase      			=torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

              _lowercase      			={'image_embeds': image_embeds}
              _lowercase      			=self.unet(
                  sample=lowerCAmelCase				,  timestep=lowerCAmelCase				,  encoder_hidden_states=lowerCAmelCase				,  added_cond_kwargs=lowerCAmelCase				,  return_dict=lowerCAmelCase				,  )[0]

              if do_classifier_free_guidance:
                    _lowercase			,			_lowercase      			=noise_pred.split(latents.shape[1]				,  dim=1   )
                    _lowercase			,			_lowercase      			=noise_pred.chunk(2   )
                    _lowercase			,			_lowercase      			=variance_pred.chunk(2   )
                    _lowercase      			=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    _lowercase      			=torch.cat([noise_pred, variance_pred_text]				,  dim=1   )

              if not (
                  hasattr(self.scheduler.config				,  'variance_type'   )
                  and self.scheduler.config.variance_type in ["learned", "learned_range"]
              ):
                    _lowercase			,			_lowercase      			=noise_pred.split(latents.shape[1]				,  dim=1   )

              # compute the previous noisy sample x_t -> x_t-1
              _lowercase      			=self.scheduler.step(
                  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  generator=lowerCAmelCase				,  )[0]
        # post-processing
        _lowercase      			=self.movq.decode(lowerCAmelCase				,  force_not_quantize=lowerCAmelCase   )['sample']

        if output_type not in ["pt", "np", "pil"]:
              raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''   )

        if output_type in ["np", "pil"]:
              _lowercase      			=image * 0.5 + 0.5
              _lowercase      			=image.clamp(0				,  1   )
              _lowercase      			=image.cpu().permute(0				,  2				,  3				,  1   ).float().numpy()

        if output_type == "pil":
              _lowercase      			=self.numpy_to_pil(lowerCAmelCase   )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=lowerCAmelCase   )





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowercase_   							=  datasets.utils.logging.get_logger(__name__)





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilderConfig				):
  _a		     =    None
  _a		     =    None





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilder				):
  _a		     =    datasets.Audio()
  _a		     =    """audio"""
  _a		     =    AudioFolderConfig
  _a		     =    42  # definition at the bottom of the script
  _a		     =    AudioClassification(audio_column="""audio"""     ,     label_column="""label"""				)
lowercase_   							=  [
    '.aiff',
    '.au',
    '.avr',
    '.caf',
    '.flac',
    '.htk',
    '.svx',
    '.mat4',
    '.mat5',
    '.mpc2k',
    '.ogg',
    '.paf',
    '.pvf',
    '.raw',
    '.rf64',
    '.sd2',
    '.sds',
    '.ircam',
    '.voc',
    '.w64',
    '.wav',
    '.nist',
    '.wavex',
    '.wve',
    '.xi',
    '.mp3',
    '.opus',
]
lowercase_   							=  AUDIO_EXTENSIONS


