'''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,
)


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

lowerCAmelCase_			:			Tuple    	=   "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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 __a			(						__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    Optional[Any]			,		__lowerCamelCase      :    Any=8					)						->				List[str]:
      '''simple docstring'''


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


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Union[str, Any]=512			,		__lowerCamelCase      :    Dict=512					)						->				Any:
      '''simple docstring'''


      lowercase_     			=   pil_image.resize((w, h)			,		resample=Image.BICUBIC			,		reducing_gap=1					)
      lowercase_     			=   np.array(pil_image.convert("RGB"					)					)
      lowercase_     			=   arr.astype(np.floataa					) / 1_2_7.5 - 1
      lowercase_     			=   np.transpose(__lowerCamelCase			,		[2, 0, 1]					)
      lowercase_     			=   torch.from_numpy(__lowerCamelCase					).unsqueeze(0					)
      return image






class 				lowercase    (    __lowerCamelCase			):


   def __init__( self					:						Any ,		__lowerCAmelCase					:						UNetaDConditionModel ,		__lowerCAmelCase					:						DDPMScheduler ,		__lowerCAmelCase					:						VQModel ,		)   ->       int:
         super().__init__()

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


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						List[str])   ->       Tuple:
         # get the original timestep using init_timestep
         lowercase_     			=   min(int(num_inference_steps * strength) ,		__lowerCAmelCase)

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

         return timesteps, num_inference_steps - t_start


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						List[Any]=None)   ->       Optional[int]:
         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)}')

         lowercase_     			=   image.to(device=__lowerCAmelCase ,		dtype=__lowerCAmelCase)

         lowercase_     			=   batch_size * num_images_per_prompt

         if image.shape[1] == 4:
               lowercase_     			=   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):
                     lowercase_     			=   [
                         self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(__lowerCAmelCase)
                     ]
                     lowercase_     			=   torch.cat(__lowerCAmelCase ,		dim=0)
               else:
                     lowercase_     			=   self.movq.encode(__lowerCAmelCase).latent_dist.sample(__lowerCAmelCase)

               lowercase_     			=   self.movq.config.scaling_factor * init_latents

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

         lowercase_     			=   init_latents.shape
         lowercase_     			=   randn_tensor(__lowerCAmelCase ,		generator=__lowerCAmelCase ,		device=__lowerCAmelCase ,		dtype=__lowerCAmelCase)

         # get latents
         lowercase_     			=   self.scheduler.add_noise(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase)

         lowercase_     			=   init_latents

         return latents


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						int=0)   ->       Any:
         if is_accelerate_available():
               from accelerate import cpu_offload
         else:
               raise ImportError("Please install accelerate via `pip install accelerate`")

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

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


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						int=0)   ->       str:
         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.")

         lowercase_     			=   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)

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

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


   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def __UpperCAmelCase							( self					:						str)   ->       int:
         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					:						List[Any] ,		__lowerCAmelCase					:						Union[torch.FloatTensor, List[torch.FloatTensor]] ,		__lowerCAmelCase					:						Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,		__lowerCAmelCase					:						Union[torch.FloatTensor, List[torch.FloatTensor]] ,		__lowerCAmelCase					:						torch.FloatTensor ,		__lowerCAmelCase					:						int = 512 ,		__lowerCAmelCase					:						int = 512 ,		__lowerCAmelCase					:						int = 100 ,		__lowerCAmelCase					:						float = 4.0 ,		__lowerCAmelCase					:						float = 0.3 ,		__lowerCAmelCase					:						int = 1 ,		__lowerCAmelCase					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None ,		__lowerCAmelCase					:						Optional[str] = "pil" ,		__lowerCAmelCase					:						bool = True ,		)   ->       List[Any]:
         lowercase_     			=   self._execution_device

         lowercase_     			=   guidance_scale > 1.0

         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):
               lowercase_     			=   torch.cat(__lowerCAmelCase ,		dim=0)
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):
               lowercase_     			=   torch.cat(__lowerCAmelCase ,		dim=0)
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):
               lowercase_     			=   torch.cat(__lowerCAmelCase ,		dim=0)

         lowercase_     			=   image_embeds.shape[0]

         if do_classifier_free_guidance:
               lowercase_     			=   image_embeds.repeat_interleave(__lowerCAmelCase ,		dim=0)
               lowercase_     			=   negative_image_embeds.repeat_interleave(__lowerCAmelCase ,		dim=0)
               lowercase_     			=   hint.repeat_interleave(__lowerCAmelCase ,		dim=0)

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

         if not isinstance(__lowerCAmelCase ,		__lowerCAmelCase):
               lowercase_     			=   [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')

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

         lowercase_     			=   self.movq.encode(__lowerCAmelCase)["latents"]
         lowercase_     			=   latents.repeat_interleave(__lowerCAmelCase ,		dim=0)
         self.scheduler.set_timesteps(__lowerCAmelCase ,		device=__lowerCAmelCase)
         lowercase_			,				lowercase_     			=   self.get_timesteps(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase)
         lowercase_     			=   timesteps[:1].repeat(batch_size * num_images_per_prompt)
         lowercase_			,				lowercase_     			=   downscale_height_and_width(__lowerCAmelCase ,		__lowerCAmelCase ,		self.movq_scale_factor)
         lowercase_     			=   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
               lowercase_     			=   torch.cat([latents] * 2) if do_classifier_free_guidance else latents

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

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

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

               lowercase_     			=   self.scheduler.step(
                   __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		generator=__lowerCAmelCase ,		)[0]

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

         if output_type == "pil":
               lowercase_     			=   self.numpy_to_pil(__lowerCAmelCase)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=__lowerCAmelCase)







'''simple docstring'''

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 ConvNextImageProcessor





class 				lowercase    (    unittest.TestCase			):


   def __init__( self					:						str ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[int]=7 ,		__lowerCAmelCase					:						Optional[Any]=3 ,		__lowerCAmelCase					:						Optional[Any]=18 ,		__lowerCAmelCase					:						List[str]=30 ,		__lowerCAmelCase					:						List[str]=400 ,		__lowerCAmelCase					:						Tuple=True ,		__lowerCAmelCase					:						Optional[int]=None ,		__lowerCAmelCase					:						List[str]=0.875 ,		__lowerCAmelCase					:						int=True ,		__lowerCAmelCase					:						Optional[int]=[0.5, 0.5, 0.5] ,		__lowerCAmelCase					:						List[str]=[0.5, 0.5, 0.5] ,		)   ->       Optional[Any]:
         lowercase_     			=   size if size is not None else {"shortest_edge": 20}
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   num_channels
         lowercase_     			=   image_size
         lowercase_     			=   min_resolution
         lowercase_     			=   max_resolution
         lowercase_     			=   do_resize
         lowercase_     			=   size
         lowercase_     			=   crop_pct
         lowercase_     			=   do_normalize
         lowercase_     			=   image_mean
         lowercase_     			=   image_std


   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }





@require_torch
@require_vision
class 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =ConvNextImageProcessor if is_vision_available() else None


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Tuple:
         lowercase_     			=   ConvNextImageProcessingTester(self)


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       int:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Any:
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"size"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"crop_pct"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_normalize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_mean"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_std"))


   def __UpperCAmelCase							( self					:						List[str])   ->       Any:
         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 20})

         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict ,		size=42)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 42})


   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         pass


   def __UpperCAmelCase							( self					:						List[Any])   ->       Any:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		Image.Image)

         # Test not batched input
         lowercase_     			=   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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       str:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		numpify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		np.ndarray)

         # Test not batched input
         lowercase_     			=   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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		torchify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		torch.Tensor)

         # Test not batched input
         lowercase_     			=   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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)






