






"""simple docstring"""
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


snake_case   :   List[str]    		=		logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case   :   List[str]    		=		"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""


def        A	(							__snake_case:   Dict   ,    __snake_case:   List[Any]   ,    __snake_case:   int=8     )						->		Union[str, Any]:

       """simple docstring"""





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


def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]=5_1_2   ,    __snake_case:   Optional[Any]=5_1_2     )						->		Optional[Any]:

       """simple docstring"""





       __magic_name__										=						pil_image.resize((w, h)   ,    resample=Image.BICUBIC   ,    reducing_gap=1     )
       __magic_name__										=						np.array(pil_image.convert('RGB'     )     )
       __magic_name__										=						arr.astype(np.floataa     ) / 127.5 - 1
       __magic_name__										=						np.transpose(__snake_case   ,    [2, 0, 1]     )
       __magic_name__										=						torch.from_numpy(__snake_case     ).unsqueeze(0     )
       return image






class 					UpperCamelCase__   (							lowerCAmelCase__):







    """simple docstring"""







    def __init__(		self   :  Any							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]							,						):





           '''simple docstring'''

           super().__init__()

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







    def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[str]   ):





           '''simple docstring'''

           __magic_name__										=						min(int(num_inference_steps * strength   )							,						_lowerCamelCase   )

           __magic_name__										=						max(num_inference_steps - init_timestep							,						0   )
           __magic_name__										=						self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start







    def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Optional[Any]=None   ):





           '''simple docstring'''

           if not isinstance(_lowerCamelCase							,						(torch.Tensor, PIL.Image.Image, list)   ):
                  raise ValueError(
                      f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase   )}"""   )

           __magic_name__										=						image.to(device=_lowerCamelCase							,						dtype=_lowerCamelCase   )

           __magic_name__										=						batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                  __magic_name__										=						image

           else:
                  if isinstance(_lowerCamelCase							,						_lowerCamelCase   ) and len(_lowerCamelCase   ) != batch_size:
                         raise ValueError(
                             f"""You have passed a list of generators of length {len(_lowerCamelCase   )}, but requested an effective batch"""
                             f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

                  elif isinstance(_lowerCamelCase							,						_lowerCamelCase   ):
                         __magic_name__										=						[
                             self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(_lowerCamelCase   )
                         ]
                         __magic_name__										=						torch.cat(_lowerCamelCase							,						dim=0   )
                  else:
                         __magic_name__										=						self.movq.encode(_lowerCamelCase   ).latent_dist.sample(_lowerCamelCase   )

                  __magic_name__										=						self.movq.config.scaling_factor * init_latents

           __magic_name__										=						torch.cat([init_latents]							,						dim=0   )

           __magic_name__										=						init_latents.shape
           __magic_name__										=						randn_tensor(_lowerCamelCase							,						generator=_lowerCamelCase							,						device=_lowerCamelCase							,						dtype=_lowerCamelCase   )

           # get latents
           __magic_name__										=						self.scheduler.add_noise(_lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase   )

           __magic_name__										=						init_latents

           return latents







    def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Union[str, Any]=0   ):





           '''simple docstring'''

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

           __magic_name__										=						torch.device(f"""cuda:{gpu_id}"""   )

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







    def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  Union[str, Any]=0   ):





           '''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.'   )

           __magic_name__										=						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)

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

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







    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 							a__ (		self   :  Union[str, Any]   ):





           '''simple docstring'''

           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   :  Optional[Any]							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Optional[Any] = 5_1_2							,						UpperCamelCase_   :  Tuple = 5_1_2							,						UpperCamelCase_   :  List[str] = 1_0_0							,						UpperCamelCase_   :  List[Any] = 4.0							,						UpperCamelCase_   :  Tuple = 0.3							,						UpperCamelCase_   :  List[str] = 1							,						UpperCamelCase_   :  List[Any] = None							,						UpperCamelCase_   :  str = "pil"							,						UpperCamelCase_   :  List[Any] = True							,						):





           '''simple docstring'''

           __magic_name__										=						self._execution_device

           __magic_name__										=						guidance_scale > 1.0

           if isinstance(_lowerCamelCase							,						_lowerCamelCase   ):
                  __magic_name__										=						torch.cat(_lowerCamelCase							,						dim=0   )
           if isinstance(_lowerCamelCase							,						_lowerCamelCase   ):
                  __magic_name__										=						torch.cat(_lowerCamelCase							,						dim=0   )
           if isinstance(_lowerCamelCase							,						_lowerCamelCase   ):
                  __magic_name__										=						torch.cat(_lowerCamelCase							,						dim=0   )

           __magic_name__										=						image_embeds.shape[0]

           if do_classifier_free_guidance:
                  __magic_name__										=						image_embeds.repeat_interleave(_lowerCamelCase							,						dim=0   )
                  __magic_name__										=						negative_image_embeds.repeat_interleave(_lowerCamelCase							,						dim=0   )
                  __magic_name__										=						hint.repeat_interleave(_lowerCamelCase							,						dim=0   )

           __magic_name__										=						torch.cat([negative_image_embeds, image_embeds]							,						dim=0   ).to(dtype=self.unet.dtype							,						device=_lowerCamelCase   )
           __magic_name__										=						torch.cat([hint, hint]							,						dim=0   ).to(dtype=self.unet.dtype							,						device=_lowerCamelCase   )

           if not isinstance(_lowerCamelCase							,						_lowerCamelCase   ):
                  __magic_name__										=						[image]
           if not all(isinstance(_lowerCamelCase							,						(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
                  raise ValueError(
                      f"""Input is in incorrect format: {[type(_lowerCamelCase   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""   )

           __magic_name__										=						torch.cat([prepare_image(_lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase   ) for i in image]							,						dim=0   )
           __magic_name__										=						image.to(dtype=image_embeds.dtype							,						device=_lowerCamelCase   )

           __magic_name__										=						self.movq.encode(_lowerCamelCase   )["""latents"""]
           __magic_name__										=						latents.repeat_interleave(_lowerCamelCase							,						dim=0   )
           self.scheduler.set_timesteps(_lowerCamelCase							,						device=_lowerCamelCase   )
           __magic_name__										=						self.get_timesteps(_lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase   )
           __magic_name__										=						timesteps[:1].repeat(batch_size * num_images_per_prompt   )
           __magic_name__										=						downscale_height_and_width(_lowerCamelCase							,						_lowerCamelCase							,						self.movq_scale_factor   )
           __magic_name__										=						self.prepare_latents(
               _lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase							,						image_embeds.dtype							,						_lowerCamelCase							,						_lowerCamelCase   )
           for i, t in enumerate(self.progress_bar(_lowerCamelCase   )   ):
                  # expand the latents if we are doing classifier free guidance
                  __magic_name__										=						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

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

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

                  # compute the previous noisy sample x_t -> x_t-1

                  __magic_name__										=						self.scheduler.step(
                      _lowerCamelCase							,						_lowerCamelCase							,						_lowerCamelCase							,						generator=_lowerCamelCase							,						)[0]

           # post-processing
           __magic_name__										=						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"]:
                  __magic_name__										=						image * 0.5 + 0.5
                  __magic_name__										=						image.clamp(0							,						1   )
                  __magic_name__										=						image.cpu().permute(0							,						2							,						3							,						1   ).float().numpy()

           if output_type == "pil":
                  __magic_name__										=						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_tokenizers_available, is_torch_available


snake_case   :   Optional[int]    		=		{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		[
     """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SplinterForQuestionAnswering""",
     """SplinterForPreTraining""",
     """SplinterLayer""",
     """SplinterModel""",
     """SplinterPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
 from .tokenization_splinter import SplinterTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_splinter_fast import SplinterTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_splinter import (
      SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SplinterForPreTraining,
      SplinterForQuestionAnswering,
      SplinterLayer,
      SplinterModel,
      SplinterPreTrainedModel,
  )


else:
 import sys

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