





"""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,
)


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

__magic_name__       :					List[Any]						=			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=8     ):
						UpperCamelCase				:		Union[str, Any]				   =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCamelCase				:		Optional[int]				   =			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=512							,     SCREAMING_SNAKE_CASE=512     ):
						UpperCamelCase				:		Optional[Any]				   =			pil_image.resize((w, h)							,     resample=Image.BICUBIC							,     reducing_gap=1     )
						UpperCamelCase				:		Any				   =			np.array(pil_image.convert("""RGB"""     )     )
						UpperCamelCase				:		List[Any]				   =			arr.astype(np.floataa     ) / 1_27.5 - 1
						UpperCamelCase				:		Union[str, Any]				   =			np.transpose(SCREAMING_SNAKE_CASE							,     [2, 0, 1]     )
						UpperCamelCase				:		List[Any]				   =			torch.from_numpy(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )
						return image






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   ):






									'''simple docstring'''







									super().__init__()

									self.register_modules(
									    unet=_A						,   scheduler=_A						,   movq=_A						,   )
									UpperCamelCase				:		List[str]				   =			2 ** (len(self.movq.config.block_out_channels    ) - 1)







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			min(int(num_inference_steps * strength    )						,   _A    )

									UpperCamelCase				:		List[str]				   =			max(num_inference_steps - init_timestep						,   0    )
									UpperCamelCase				:		Optional[Any]				   =			self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A=None    ):






									'''simple docstring'''







									if not isinstance(_A						,   (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(_A    )}"""    )

									UpperCamelCase				:		Optional[int]				   =			image.to(device=_A						,   dtype=_A    )

									UpperCamelCase				:		int				   =			batch_size * num_images_per_prompt

									if image.shape[1] == 4:
															UpperCamelCase				:		List[str]				   =			image

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

															elif isinstance(_A						,   _A    ):
																					UpperCamelCase				:		str				   =			[
																					    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_A    )
																					]
																					UpperCamelCase				:		Union[str, Any]				   =			torch.cat(_A						,   dim=0    )
															else:
																					UpperCamelCase				:		List[str]				   =			self.movq.encode(_A    ).latent_dist.sample(_A    )

															UpperCamelCase				:		Dict				   =			self.movq.config.scaling_factor * init_latents

									UpperCamelCase				:		Tuple				   =			torch.cat([init_latents]						,   dim=0    )

									UpperCamelCase				:		List[str]				   =			init_latents.shape
									UpperCamelCase				:		int				   =			randn_tensor(_A						,   generator=_A						,   device=_A						,   dtype=_A    )

									# get latents
									UpperCamelCase				:		int				   =			self.scheduler.add_noise(_A						,   _A						,   _A    )

									UpperCamelCase				:		Optional[Any]				   =			init_latents

									return latents







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







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

									UpperCamelCase				:		List[str]				   =			torch.device(f"""cuda:{gpu_id}"""    )

									UpperCamelCase				:		Optional[int]				   =			[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(_A						,   _A    )







			def   _a      (      self						,   _A=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."""    )

									UpperCamelCase				:		List[str]				   =			torch.device(f"""cuda:{gpu_id}"""    )

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

									UpperCamelCase				:		Dict				   =			None
									for cpu_offloaded_model in [self.unet, self.movq]:
															UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			cpu_offload_with_hook(_A						,   _A						,   prev_module_hook=_A    )

									# We'll offload the last model manually.
									UpperCamelCase				:		Optional[Any]				   =			hook







			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def   _a      (      self    ):






									'''simple docstring'''







									if not hasattr(self.unet						,   """_hf_hook"""    ):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(_A						,   """_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(_A    )
			def __call__(      self						,   _A						,   _A						,   _A						,   _A = 5_1_2						,   _A = 5_1_2						,   _A = 1_0_0						,   _A = 4.0						,   _A = 0.3						,   _A = 1						,   _A = None						,   _A = "pil"						,   _A = True						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self._execution_device

									UpperCamelCase				:		List[Any]				   =			guidance_scale > 1.0

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Dict				   =			torch.cat(_A						,   dim=0    )
									UpperCamelCase				:		Tuple				   =			image_embeds.shape[0]
									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		int				   =			torch.cat(_A						,   dim=0    )

									if do_classifier_free_guidance:
															UpperCamelCase				:		str				   =			image_embeds.repeat_interleave(_A						,   dim=0    )
															UpperCamelCase				:		Optional[Any]				   =			negative_image_embeds.repeat_interleave(_A						,   dim=0    )

									UpperCamelCase				:		Dict				   =			torch.cat([negative_image_embeds, image_embeds]						,   dim=0    ).to(dtype=self.unet.dtype						,   device=_A    )

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

									UpperCamelCase				:		Any				   =			torch.cat([prepare_image(_A						,   _A						,   _A    ) for i in image]						,   dim=0    )
									UpperCamelCase				:		Union[str, Any]				   =			image.to(dtype=image_embeds.dtype						,   device=_A    )

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

															UpperCamelCase				:		Dict				   =			{"""image_embeds""": image_embeds}
															UpperCamelCase				:		int				   =			self.unet(
															    sample=_A						,   timestep=_A						,   encoder_hidden_states=_A						,   added_cond_kwargs=_A						,   return_dict=_A						,   )[0]

															if do_classifier_free_guidance:
																					UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			noise_pred.split(latents.shape[1]						,   dim=1    )
																					UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			noise_pred.chunk(2    )
																					UpperCamelCase   ,						UpperCamelCase				:		Optional[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   ,						UpperCamelCase				:		int				   =			noise_pred.split(latents.shape[1]						,   dim=1    )

															# compute the previous noisy sample x_t -> x_t-1
															UpperCamelCase				:		List[Any]				   =			self.scheduler.step(
															    _A						,   _A						,   _A						,   generator=_A						,   )[0]

									# post-processing
									UpperCamelCase				:		Union[str, Any]				   =			self.movq.decode(_A						,   force_not_quantize=_A    )["""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				:		Optional[int]				   =			image * 0.5 + 0.5
															UpperCamelCase				:		Optional[Any]				   =			image.clamp(0						,   1    )
															UpperCamelCase				:		str				   =			image.cpu().permute(0						,   2						,   3						,   1    ).float().numpy()

									if output_type == "pil":
															UpperCamelCase				:		Optional[Any]				   =			self.numpy_to_pil(_A    )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_A    )








'''simple docstring'''





import unittest

import numpy as np

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

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 DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    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['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    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['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    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['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  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['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



