

"""simple docstring"""




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,
)


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

SCREAMING_SNAKE_CASE_     :			str	  =			'''
    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 	UpperCAmelCase__   (				A__	,    A__	,    A__=8   )      ->      Optional[Any]:




   """simple docstring"""
   lowerCamelCase__						=			height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   lowerCamelCase__						=			width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						)   ->					Any:
         super().__init__()

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         if latents is None:
            lowerCamelCase__						=			randn_tensor(SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__    ,						device=SCREAMING_SNAKE_CASE__    ,						dtype=SCREAMING_SNAKE_CASE__		)
         else:
            if latents.shape != shape:
               raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
            lowerCamelCase__						=			latents.to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			latents * scheduler.init_noise_sigma
         return latents





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=0		)   ->					Dict:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("Please install accelerate via `pip install accelerate`"		)

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

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=0		)   ->					Optional[int]:
         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."		)

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

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

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

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





      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         if not hasattr(self.unet    ,						"_hf_hook"		):
            return self.device
         for module in self.unet.modules():
            if (
                hasattr(SCREAMING_SNAKE_CASE__    ,						"_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(SCREAMING_SNAKE_CASE__		)
      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = 512    ,						SCREAMING_SNAKE_CASE__ = 512    ,						SCREAMING_SNAKE_CASE__ = 100    ,						SCREAMING_SNAKE_CASE__ = 4.0    ,						SCREAMING_SNAKE_CASE__ = 1    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = "pil"    ,						SCREAMING_SNAKE_CASE__ = True    ,						)   ->					Optional[int]:
         lowerCamelCase__						=			self._execution_device

         lowerCamelCase__						=			guidance_scale > 1.0

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

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

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

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

         lowerCamelCase__						=			self.unet.config.in_channels

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

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

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

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

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

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

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

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




