






"""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,
)


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

_UpperCamelCase	     =    """\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					(						_snake_case ,			_snake_case ,			_snake_case=8				):







      """simple docstring"""






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







class    lowerCamelCase__   (			snake_case					):






    def __init__(					self      ,A      ,A      ,A      ,):
          super().__init__()

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






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A      ,A      ,A      ,A   ):
          if latents is None:
                UpperCAmelCase							     =			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}'''   )
                UpperCAmelCase							     =			latents.to(_lowerCAmelCase   )

          UpperCAmelCase							     =			latents * scheduler.init_noise_sigma
          return latents






    def 							_UpperCamelCase				(					self      ,A=0   ):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

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

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






    def 							_UpperCamelCase				(					self      ,A=0   ):
          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."""   )

          UpperCAmelCase							     =			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)

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

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






    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 							_UpperCamelCase				(					self   ):
          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      ,A      ,A      ,A = 512      ,A = 512      ,A = 100      ,A = 4.0      ,A = 1      ,A = None      ,A = None      ,A = "pil"      ,A = True      ,):
          UpperCAmelCase							     =			self._execution_device

          UpperCAmelCase							     =			guidance_scale > 1.0

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

          if do_classifier_free_guidance:
                UpperCAmelCase							     =			image_embeds.repeat_interleave(_lowerCAmelCase      ,dim=0   )
                UpperCAmelCase							     =			negative_image_embeds.repeat_interleave(_lowerCAmelCase      ,dim=0   )

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

          self.scheduler.set_timesteps(_lowerCAmelCase      ,device=_lowerCAmelCase   )
          UpperCAmelCase							     =			self.scheduler.timesteps

          UpperCAmelCase							     =			self.unet.config.in_channels

          UpperCAmelCase     ,   UpperCAmelCase							     =			downscale_height_and_width(_lowerCAmelCase      ,_lowerCAmelCase      ,self.movq_scale_factor   )

          # create initial latent
          UpperCAmelCase							     =			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
                UpperCAmelCase							     =			torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

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

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

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

          if output_type == "pil":
                UpperCAmelCase							     =			self.numpy_to_pil(_lowerCAmelCase   )

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=_lowerCAmelCase   )










'''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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[int]								=       "deit"




 def __init__(						self      ,  _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=True      ,  _lowerCAmelCase=16      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   encoder_stride







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)





 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4


