

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

a_						     =    '\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  lowerCamelCase__ (					_a							,			_a							,			_a=8):
 SCREAMING_SNAKE_CASE   :      Dict				     =       height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 SCREAMING_SNAKE_CASE   :      Tuple				     =       width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor






class _UpperCamelCase							(							__A   ):
   '''simple docstring'''







   def __init__(			self							: Optional[Any]      ,      a							: UNetaDConditionModel      ,      a							: DDPMScheduler      ,      a							: VQModel      ,      )							->       Any:

    """simple docstring"""

    super().__init__()

    self.register_modules(
        unet=a      ,      scheduler=a      ,      movq=a      ,      )
    SCREAMING_SNAKE_CASE   :      Any				     =       2 ** (len(self.movq.config.block_out_channels	) - 1)
   def 							__UpperCamelCase (			self							: int      ,      a							: Dict      ,      a							: Any      ,      a							: Optional[int]      ,      a							: Union[str, Any]      ,      a							: Optional[int]      ,      a							: Optional[int]	)							->       List[Any]:

    """simple docstring"""

    if latents is None:
     SCREAMING_SNAKE_CASE   :      Dict				     =       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   :      Dict				     =       latents.to(a	)

    SCREAMING_SNAKE_CASE   :      str				     =       latents * scheduler.init_noise_sigma
    return latents
   def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[int]=0	)							->       Optional[int]:

    """simple docstring"""

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

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.device(F"cuda:{gpu_id}"	)

    SCREAMING_SNAKE_CASE   :      Tuple				     =       [
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
      cpu_offload(a      ,      a	)
   def 							__UpperCamelCase (			self							: Any      ,      a							: Optional[Any]=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."	)

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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   :      Tuple				     =       None
    for cpu_offloaded_model in [self.unet, self.movq]:
     SCREAMING_SNAKE_CASE   :      Any				     =       cpu_offload_with_hook(a      ,      a      ,      prev_module_hook=a	)

    # We'll offload the last model manually.
    SCREAMING_SNAKE_CASE   :      List[str]				     =       hook
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[str]:

    """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							: List[Any]      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: int = 512      ,      a							: int = 512      ,      a							: int = 100      ,      a							: float = 4.0      ,      a							: int = 1      ,      a							: Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      a							: Optional[torch.FloatTensor] = None      ,      a							: Optional[str] = "pil"      ,      a							: bool = True      ,      )							->       Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      Tuple				     =       self._execution_device

    SCREAMING_SNAKE_CASE   :      List[Any]				     =       guidance_scale > 1.0

    if isinstance(a      ,      a	):
     SCREAMING_SNAKE_CASE   :      int				     =       torch.cat(a      ,      dim=0	)
    SCREAMING_SNAKE_CASE   :      Tuple				     =       image_embeds.shape[0] * num_images_per_prompt
    if isinstance(a      ,      a	):
     SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(a      ,      dim=0	)

    if do_classifier_free_guidance:
     SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_embeds.repeat_interleave(a      ,      dim=0	)
     SCREAMING_SNAKE_CASE   :      Optional[int]				     =       negative_image_embeds.repeat_interleave(a      ,      dim=0	)

    SCREAMING_SNAKE_CASE   :      Dict				     =       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   :      Dict				     =       self.scheduler.timesteps

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.unet.config.in_channels

    SCREAMING_SNAKE_CASE   :      Dict				     =       downscale_height_and_width(a      ,      a      ,      self.movq_scale_factor	)

    # create initial latent
    SCREAMING_SNAKE_CASE   :      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
     SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

     SCREAMING_SNAKE_CASE   :      int				     =       {"image_embeds": image_embeds}
     SCREAMING_SNAKE_CASE   :      int				     =       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   :      Tuple				     =       noise_pred.split(latents.shape[1]      ,      dim=1	)
      SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       noise_pred.chunk(2	)
      SCREAMING_SNAKE_CASE   :      List[Any]				     =       variance_pred.chunk(2	)
      SCREAMING_SNAKE_CASE   :      int				     =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
      SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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   :      int				     =       noise_pred.split(latents.shape[1]      ,      dim=1	)

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

    if output_type == "pil":
     SCREAMING_SNAKE_CASE   :      Dict				     =       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, is_vision_available


a_						     =    {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    ['BridgeTowerImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BridgeTowerForContrastiveLearning',
			    'BridgeTowerForImageAndTextRetrieval',
			    'BridgeTowerForMaskedLM',
			    'BridgeTowerModel',
			    'BridgeTowerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_bridgetower import (
			    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BridgeTowerConfig,
			    BridgeTowerTextConfig,
			    BridgeTowerVisionConfig,
			)
			from .processing_bridgetower import BridgeTowerProcessor

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_bridgetower import BridgeTowerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bridgetower import (
						    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BridgeTowerForContrastiveLearning,
						    BridgeTowerForImageAndTextRetrieval,
						    BridgeTowerForMaskedLM,
						    BridgeTowerModel,
						    BridgeTowerPreTrainedModel,
						)


else:
			import sys

			a_						     =    _LazyModule(__name__, globals()['__file__'], _import_structure)