'''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,
)


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  _UpperCamelCase (     _a	:	Any				,     _a	:	Tuple				,     _a	:	Optional[int]=8			):




    """simple docstring"""



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






class 			__lowercase		(						_UpperCAmelCase						):







     """simple docstring"""






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

         self.register_modules(
             unet=lowerCamelCase_     ,			scheduler=lowerCamelCase_     ,			movq=lowerCamelCase_     ,			)
         __UpperCamelCase	:	str		    =  2 ** (len(self.movq.config.block_out_channels    ) - 1)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):




         if latents is None:
             __UpperCamelCase	:	Union[str, Any]		    =  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	:	List[str]		    =  latents.to(lowerCamelCase_    )

         __UpperCamelCase	:	Union[str, Any]		    =  latents * scheduler.init_noise_sigma
         return latents







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=0    ):




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

         __UpperCamelCase	:	Union[str, Any]		    =  torch.device(f"""cuda:{gpu_id}"""    )

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=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	:	Any		    =  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	:	str		    =  None
         for cpu_offloaded_model in [self.unet, self.movq]:
             __UpperCamelCase	:	Dict		    =  cpu_offload_with_hook(lowerCamelCase_     ,			lowerCamelCase_     ,			prev_module_hook=lowerCamelCase_    )

         # We'll offload the last model manually.
         __UpperCamelCase	:	str		    =  hook







     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def lowerCAmelCase     (		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     ,			_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     ,			):
         __UpperCamelCase	:	Any		    =  self._execution_device

         __UpperCamelCase	:	Optional[int]		    =  guidance_scale > 1.0

         if isinstance(lowerCamelCase_     ,			lowerCamelCase_    ):
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat(lowerCamelCase_     ,			dim=0    )
         __UpperCamelCase	:	Optional[int]		    =  image_embeds.shape[0] * num_images_per_prompt
         if isinstance(lowerCamelCase_     ,			lowerCamelCase_    ):
             __UpperCamelCase	:	List[str]		    =  torch.cat(lowerCamelCase_     ,			dim=0    )

         if do_classifier_free_guidance:
             __UpperCamelCase	:	Optional[int]		    =  image_embeds.repeat_interleave(lowerCamelCase_     ,			dim=0    )
             __UpperCamelCase	:	str		    =  negative_image_embeds.repeat_interleave(lowerCamelCase_     ,			dim=0    )

         __UpperCamelCase	:	Any		    =  torch.cat([negative_image_embeds, image_embeds]     ,			dim=0    ).to(dtype=self.unet.dtype     ,			device=lowerCamelCase_    )

         self.scheduler.set_timesteps(lowerCamelCase_     ,			device=lowerCamelCase_    )
         __UpperCamelCase	:	str		    =  self.scheduler.timesteps

         __UpperCamelCase	:	Optional[Any]		    =  self.unet.config.in_channels

         __UpperCamelCase	:	List[str]		    =  downscale_height_and_width(lowerCamelCase_     ,			lowerCamelCase_     ,			self.movq_scale_factor    )

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

             __UpperCamelCase	:	Union[str, Any]		    =  {'''image_embeds''': image_embeds}
             __UpperCamelCase	:	Optional[int]		    =  self.unet(
                 sample=lowerCamelCase_     ,			timestep=lowerCamelCase_     ,			encoder_hidden_states=lowerCamelCase_     ,			added_cond_kwargs=lowerCamelCase_     ,			return_dict=lowerCamelCase_     ,			)[0]

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

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

         if output_type == "pil":
             __UpperCamelCase	:	str		    =  self.numpy_to_pil(lowerCamelCase_    )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=lowerCamelCase_    )




'''simple docstring'''
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)



