





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

UpperCAmelCase	:	Optional[int]		     =       """\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       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   List[Any]=8						):





  """simple docstring"""



  a__	:					List[str]										=height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  a__	:					Any										=width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class 				__lowerCAmelCase       (    A_):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      )   ->						int:



        '''simple docstring'''




        super().__init__()

        self.register_modules(
            unet=A_		,      scheduler=A_		,      movq=A_		,      )
        a__	:					List[str]										=2 ** (len(self.movq.config.block_out_channels							) - 1)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        if latents is None:
          a__	:					List[str]										=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}'''							)
          a__	:					Union[str, Any]										=latents.to(A_							)

        a__	:					int										=latents * scheduler.init_noise_sigma
        return latents







      def    _lowercase  (    self		,      lowerCAmelCase__=0							)   ->						List[str]:



        '''simple docstring'''




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

        a__	:					Optional[int]										=torch.device(F'''cuda:{gpu_id}'''							)

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







      def    _lowercase  (    self		,      lowerCAmelCase__=0							)   ->						Tuple:



        '''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."							)

        a__	:					Optional[int]										=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)

        a__	:					Dict										=None
        for cpu_offloaded_model in [self.unet, self.movq]:
          a__					,							a__	:					Optional[Any]										=cpu_offload_with_hook(A_		,      A_		,      prev_module_hook=A_							)

        # We'll offload the last model manually.
        a__	:					Any										=hook







      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def    _lowercase  (    self							)   ->						Dict:



        '''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		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__ = 5_1_2		,      lowerCAmelCase__ = 5_1_2		,      lowerCAmelCase__ = 1_0_0		,      lowerCAmelCase__ = 4.0		,      lowerCAmelCase__ = 1		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = "pil"		,      lowerCAmelCase__ = True		,      )   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self._execution_device

        a__	:					Optional[Any]										=guidance_scale > 1.0

        if isinstance(A_		,      A_							):
          a__	:					Optional[int]										=torch.cat(A_		,      dim=0							)
        a__	:					Optional[Any]										=image_embeds.shape[0] * num_images_per_prompt
        if isinstance(A_		,      A_							):
          a__	:					Optional[int]										=torch.cat(A_		,      dim=0							)

        if do_classifier_free_guidance:
          a__	:					Any										=image_embeds.repeat_interleave(A_		,      dim=0							)
          a__	:					Union[str, Any]										=negative_image_embeds.repeat_interleave(A_		,      dim=0							)

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

        self.scheduler.set_timesteps(A_		,      device=A_							)
        a__	:					Union[str, Any]										=self.scheduler.timesteps

        a__	:					str										=self.unet.config.in_channels

        a__					,							a__	:					Union[str, Any]										=downscale_height_and_width(A_		,      A_		,      self.movq_scale_factor							)

        # create initial latent
        a__	:					Optional[Any]										=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
          a__	:					Optional[Any]										=torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

          a__	:					List[str]										={"image_embeds": image_embeds}
          a__	:					List[str]										=self.unet(
              sample=A_		,      timestep=A_		,      encoder_hidden_states=A_		,      added_cond_kwargs=A_		,      return_dict=A_		,      )[0]

          if do_classifier_free_guidance:
            a__					,							a__	:					Union[str, Any]										=noise_pred.split(latents.shape[1]		,      dim=1							)
            a__					,							a__	:					Optional[int]										=noise_pred.chunk(2							)
            a__					,							a__	:					Dict										=variance_pred.chunk(2							)
            a__	:					Union[str, Any]										=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            a__	:					Tuple										=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"]
          ):
            a__					,							a__	:					List[str]										=noise_pred.split(latents.shape[1]		,      dim=1							)

          # compute the previous noisy sample x_t -> x_t-1
          a__	:					List[Any]										=self.scheduler.step(
              A_		,      A_		,      A_		,      generator=A_		,      )[0]
        # post-processing
        a__	:					Dict										=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"]:
          a__	:					Tuple										=image * 0.5 + 0.5
          a__	:					Optional[int]										=image.clamp(0		,      1							)
          a__	:					str										=image.cpu().permute(0		,      2		,      3		,      1							).float().numpy()

        if output_type == "pil":
          a__	:					Optional[Any]										=self.numpy_to_pil(A_							)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=A_							)










import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





