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

UpperCamelCase__											=					'\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    lowerCAmelCase_ (     __A,  __A,  __A=8							)     ->				int:







      '''simple docstring'''


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







class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						int       ,  __UpperCAmelCase     :						UNetaDConditionModel       ,  __UpperCAmelCase     :						DDPMScheduler       ,  __UpperCAmelCase     :						VQModel       ,  )						->				List[str]:
         """simple docstring"""



         super().__init__()

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


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				List[Any]:
         """simple docstring"""



         if latents is None:
               UpperCAmelCase__								=  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}"""		)
               UpperCAmelCase__								=  latents.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  latents * scheduler.init_noise_sigma
         return latents


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						int=0		)						->				Tuple:
         """simple docstring"""



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

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

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


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int=0		)						->				List[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."		)

         UpperCAmelCase__								=  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)

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

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


   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 			lowercase_      (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(__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     :						int       ,  __UpperCAmelCase     :						Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  __UpperCAmelCase     :						Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						int = 5_1_2       ,  __UpperCAmelCase     :						int = 5_1_2       ,  __UpperCAmelCase     :						int = 1_0_0       ,  __UpperCAmelCase     :						float = 4.0       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  __UpperCAmelCase     :						Optional[torch.FloatTensor] = None       ,  __UpperCAmelCase     :						Optional[str] = "pil"       ,  __UpperCAmelCase     :						bool = True       ,  )						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self._execution_device

         UpperCAmelCase__								=  guidance_scale > 1.0

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

         UpperCAmelCase__								=  image_embeds.shape[0] * num_images_per_prompt

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

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

         UpperCAmelCase__								=  self.movq.config.latent_channels

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

         # create initial latent
         UpperCAmelCase__								=  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
               UpperCAmelCase__								=  torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

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

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

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

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

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=__UpperCAmelCase		)





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor

class __snake_case		(      unittest.TestCase     ):
    def __init__(  self		,snake_case		,snake_case=7		,snake_case=3		,snake_case=18		,snake_case=30		,snake_case=400		,snake_case=True		,snake_case=None		,snake_case=True		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:     Dict       	=      size if size is not None else {"""shortest_edge""": 20}
           lowercase							:     Union[str, Any]       	=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           lowercase							:     str       	=      parent
           lowercase							:     int       	=      batch_size
           lowercase							:     str       	=      num_channels
           lowercase							:     int       	=      image_size
           lowercase							:     List[str]       	=      min_resolution
           lowercase							:     str       	=      max_resolution
           lowercase							:     Dict       	=      do_resize
           lowercase							:     Dict       	=      size
           lowercase							:     Dict       	=      do_center_crop
           lowercase							:     str       	=      crop_size
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Any=				MobileNetVaImageProcessor if is_vision_available() else None
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      MobileNetVaImageProcessingTester(self      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(snake_case		,"""do_resize"""      )      )
           self.assertTrue(hasattr(snake_case		,"""size"""      )      )
           self.assertTrue(hasattr(snake_case		,"""do_center_crop"""      )      )
           self.assertTrue(hasattr(snake_case		,"""crop_size"""      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 20}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 18, """width""": 18}      )

           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict		,size=42		,crop_size=84      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 42}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 84, """width""": 84}      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random PIL images
           lowercase							:     str       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,Image.Image      )

           # Test not batched input
           lowercase							:     Dict       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     Tuple       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           lowercase							:     Union[str, Any]       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,numpify=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,np.ndarray      )

           # Test not batched input
           lowercase							:     Optional[Any]       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     List[str]       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowercase							:     Tuple       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,torchify=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,torch.Tensor      )

           # Test not batched input
           lowercase							:     Optional[int]       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     List[str]       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
