





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,
)


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

SCREAMING_SNAKE_CASE_						       =	"""\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"""



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=8     )			->       Any:


      '''simple docstring'''


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



class 		UpperCamelCase__   (				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   Dict						,lowerCamelCase__						:   UNetaDConditionModel						,lowerCamelCase__						:   DDPMScheduler						,lowerCamelCase__						:   VQModel						,)					->		Optional[int]:




           '''simple docstring'''


           super().__init__()

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   str	)					->		str:




           '''simple docstring'''


           if latents is None:
                 SCREAMING_SNAKE_CASE		=						randn_tensor(__UpperCAmelCase						,generator=__UpperCAmelCase						,device=__UpperCAmelCase						,dtype=__UpperCAmelCase	)
           else:
                 if latents.shape != shape:
                       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
                 SCREAMING_SNAKE_CASE		=						latents.to(__UpperCAmelCase	)

           SCREAMING_SNAKE_CASE		=						latents * scheduler.init_noise_sigma
           return latents






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   List[str]=0	)					->		Dict:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						torch.device(F"""cuda:{gpu_id}"""	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Union[str, Any]=0	)					->		Dict:




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

           if self.device.type != "cpu":
                 self.to("""cpu"""						,silence_dtype_warnings=__UpperCAmelCase	)
                 torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

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






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Optional[Any]:




           '''simple docstring'''


           if not hasattr(self.unet						,"""_hf_hook"""	):
                 return self.device
           for module in self.unet.modules():
                 if (
                     hasattr(__UpperCAmelCase						,"""_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(__UpperCAmelCase	)
     def __call__(							self						:   List[str]						,lowerCamelCase__						:   Union[torch.FloatTensor, List[torch.FloatTensor]]						,lowerCamelCase__						:   Union[torch.FloatTensor, List[torch.FloatTensor]]						,lowerCamelCase__						:   torch.FloatTensor						,lowerCamelCase__						:   int = 512						,lowerCamelCase__						:   int = 512						,lowerCamelCase__						:   int = 100						,lowerCamelCase__						:   float = 4.0						,lowerCamelCase__						:   int = 1						,lowerCamelCase__						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None						,lowerCamelCase__						:   Optional[torch.FloatTensor] = None						,lowerCamelCase__						:   Optional[str] = "pil"						,lowerCamelCase__						:   bool = True						,)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._execution_device

           SCREAMING_SNAKE_CASE		=						guidance_scale > 1.0

           if isinstance(__UpperCAmelCase						,__UpperCAmelCase	):
                 SCREAMING_SNAKE_CASE		=						torch.cat(__UpperCAmelCase						,dim=0	)
           if isinstance(__UpperCAmelCase						,__UpperCAmelCase	):
                 SCREAMING_SNAKE_CASE		=						torch.cat(__UpperCAmelCase						,dim=0	)
           if isinstance(__UpperCAmelCase						,__UpperCAmelCase	):
                 SCREAMING_SNAKE_CASE		=						torch.cat(__UpperCAmelCase						,dim=0	)

           SCREAMING_SNAKE_CASE		=						image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
                 SCREAMING_SNAKE_CASE		=						image_embeds.repeat_interleave(__UpperCAmelCase						,dim=0	)
                 SCREAMING_SNAKE_CASE		=						negative_image_embeds.repeat_interleave(__UpperCAmelCase						,dim=0	)
                 SCREAMING_SNAKE_CASE		=						hint.repeat_interleave(__UpperCAmelCase						,dim=0	)

           SCREAMING_SNAKE_CASE		=						torch.cat([negative_image_embeds, image_embeds]						,dim=0	).to(dtype=self.unet.dtype						,device=__UpperCAmelCase	)
           SCREAMING_SNAKE_CASE		=						torch.cat([hint, hint]						,dim=0	).to(dtype=self.unet.dtype						,device=__UpperCAmelCase	)
           self.scheduler.set_timesteps(__UpperCAmelCase						,device=__UpperCAmelCase	)
           SCREAMING_SNAKE_CASE		=						self.scheduler.timesteps

           SCREAMING_SNAKE_CASE		=						self.movq.config.latent_channels

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						downscale_height_and_width(__UpperCAmelCase						,__UpperCAmelCase						,self.movq_scale_factor	)

           # create initial latent
           SCREAMING_SNAKE_CASE		=						self.prepare_latents(
               (batch_size, num_channels_latents, height, width)						,image_embeds.dtype						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,self.scheduler						,)

           for i, t in enumerate(self.progress_bar(__UpperCAmelCase	)	):
                 # expand the latents if we are doing classifier free guidance
                 SCREAMING_SNAKE_CASE		=						torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

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

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

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

           if output_type == "pil":
                 SCREAMING_SNAKE_CASE		=						self.numpy_to_pil(__UpperCAmelCase	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__UpperCAmelCase	)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""MvpTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MvpForCausalLM""",
          """MvpForConditionalGeneration""",
          """MvpForQuestionAnswering""",
          """MvpForSequenceClassification""",
          """MvpModel""",
          """MvpPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
      from .tokenization_mvp import MvpTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mvp_fast import MvpTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mvp import (
                MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                MvpForCausalLM,
                MvpForConditionalGeneration,
                MvpForQuestionAnswering,
                MvpForSequenceClassification,
                MvpModel,
                MvpPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


