





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,
)


_A					:				List[Any]				=			logging.get_logger(__name__)  # pylint: disable=invalid-name

_A					:				Optional[int]				=			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=8	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    SCREAMING_SNAKE_CASE__											=			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        super().__init__()

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

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

        SCREAMING_SNAKE_CASE__											=			latents * scheduler.init_noise_sigma
        return latents



    def  lowercase_					(       self   ,				A_=0     ):





        '''simple docstring'''

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

        SCREAMING_SNAKE_CASE__											=			torch.device(f'''cuda:{gpu_id}'''     )

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



    def  lowercase_					(       self   ,				A_=0     ):





        '''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.'''     )

        SCREAMING_SNAKE_CASE__											=			torch.device(f'''cuda:{gpu_id}'''     )

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

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

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



    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def  lowercase_					(       self     ):





        '''simple docstring'''

        if not hasattr(self.unet   ,				'''_hf_hook'''     ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(A_   ,				'''_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(A_     )
    def __call__(       self   ,				A_   ,				A_   ,				A_ = 5_12   ,				A_ = 5_12   ,				A_ = 1_00   ,				A_ = 4.0   ,				A_ = 1   ,				A_ = None   ,				A_ = None   ,				A_ = "pil"   ,				A_ = True   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._execution_device

        SCREAMING_SNAKE_CASE__											=			guidance_scale > 1.0

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

        if do_classifier_free_guidance:
            SCREAMING_SNAKE_CASE__											=			image_embeds.repeat_interleave(A_   ,				dim=0     )
            SCREAMING_SNAKE_CASE__											=			negative_image_embeds.repeat_interleave(A_   ,				dim=0     )

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

        self.scheduler.set_timesteps(A_   ,				device=A_     )
        SCREAMING_SNAKE_CASE__											=			self.scheduler.timesteps

        SCREAMING_SNAKE_CASE__											=			self.unet.config.in_channels

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			downscale_height_and_width(A_   ,				A_   ,				self.movq_scale_factor     )

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

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

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

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

            # compute the previous noisy sample x_t -> x_t-1
            SCREAMING_SNAKE_CASE__											=			self.scheduler.step(
                A_   ,				A_   ,				A_   ,				generator=A_   ,				)[0]
        # post-processing
        SCREAMING_SNAKE_CASE__											=			self.movq.decode(A_   ,				force_not_quantize=A_     )['''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"]:
            SCREAMING_SNAKE_CASE__											=			image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__											=			image.clamp(0   ,				1     )
            SCREAMING_SNAKE_CASE__											=			image.cpu().permute(0   ,				2   ,				3   ,				1     ).float().numpy()

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

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=A_     )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
