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

__snake_case         =     '''
    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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=8    )->	List[Any]:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=5_12		,       __lowerCAmelCase=5_12    )->	int:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	=pil_image.resize((w, h)		,       resample=Image.BICUBIC		,       reducing_gap=1    )
      UpperCAmelCase     :			Any     	=np.array(pil_image.convert('''RGB'''    )    )
      UpperCAmelCase     :			Any     	=arr.astype(np.floataa    ) / 127.5 - 1
      UpperCAmelCase     :			List[str]     	=np.transpose(__lowerCAmelCase		,       [2, 0, 1]    )
      UpperCAmelCase     :			List[str]     	=torch.from_numpy(__lowerCAmelCase    ).unsqueeze(0    )
      return image







class       __snake_case (  lowerCamelCase__ ):
 def __init__(	self		, snake_case__		, snake_case__		, snake_case__		, )						->				List[str]:
       '''simple docstring'''





       super().__init__()

       self.register_modules(
           unet=snake_case__		, scheduler=snake_case__		, movq=snake_case__		, )
       UpperCAmelCase     :			List[Any]     	=2 ** (len(self.movq.config.block_out_channels    ) - 1)
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=min(int(num_inference_steps * strength    )		, snake_case__    )

       UpperCAmelCase     :			Union[str, Any]     	=max(num_inference_steps - init_timestep		, 0    )
       UpperCAmelCase     :			str     	=self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__=None    )						->				Tuple:
       '''simple docstring'''





       if not isinstance(snake_case__		, (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(snake_case__    )}'''    )

       UpperCAmelCase     :			Tuple     	=image.to(device=snake_case__		, dtype=snake_case__    )

       UpperCAmelCase     :			str     	=batch_size * num_images_per_prompt

       if image.shape[1] == 4:
             UpperCAmelCase     :			Union[str, Any]     	=image

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

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

             UpperCAmelCase     :			Optional[Any]     	=self.movq.config.scaling_factor * init_latents

       UpperCAmelCase     :			Optional[Any]     	=torch.cat([init_latents]		, dim=0    )

       UpperCAmelCase     :			List[Any]     	=init_latents.shape
       UpperCAmelCase     :			str     	=randn_tensor(snake_case__		, generator=snake_case__		, device=snake_case__		, dtype=snake_case__    )

       # get latents
       UpperCAmelCase     :			Optional[int]     	=self.scheduler.add_noise(snake_case__		, snake_case__		, snake_case__    )

       UpperCAmelCase     :			Tuple     	=init_latents

       return latents
 def 						UpperCAmelCase__							(	self		, snake_case__=0    )						->				Any:
       '''simple docstring'''





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

       UpperCAmelCase     :			Optional[Any]     	=torch.device(f'''cuda:{gpu_id}'''    )

       UpperCAmelCase     :			str     	=[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                   cpu_offload(snake_case__		, snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__=0    )						->				Optional[Any]:
       '''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     :			Any     	=torch.device(f'''cuda:{gpu_id}'''    )

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

       UpperCAmelCase     :			Optional[Any]     	=None
       for cpu_offloaded_model in [self.unet, self.movq]:
             UpperCAmelCase	,    UpperCAmelCase     :			Optional[Any]     	=cpu_offload_with_hook(snake_case__		, snake_case__		, prev_module_hook=snake_case__    )

       # We'll offload the last model manually.
       UpperCAmelCase     :			Optional[int]     	=hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       if not hasattr(self.unet		, '''_hf_hook'''    ):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(snake_case__		, '''_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(snake_case__    )
 def __call__(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = 512		, snake_case__ = 512		, snake_case__ = 100		, snake_case__ = 4.0		, snake_case__ = 0.3		, snake_case__ = 1		, snake_case__ = None		, snake_case__ = "pil"		, snake_case__ = True		, )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self._execution_device

       UpperCAmelCase     :			Optional[int]     	=guidance_scale > 1.0

       if isinstance(snake_case__		, snake_case__    ):
             UpperCAmelCase     :			Tuple     	=torch.cat(snake_case__		, dim=0    )
       if isinstance(snake_case__		, snake_case__    ):
             UpperCAmelCase     :			Union[str, Any]     	=torch.cat(snake_case__		, dim=0    )
       if isinstance(snake_case__		, snake_case__    ):
             UpperCAmelCase     :			Tuple     	=torch.cat(snake_case__		, dim=0    )

       UpperCAmelCase     :			List[str]     	=image_embeds.shape[0]

       if do_classifier_free_guidance:
             UpperCAmelCase     :			str     	=image_embeds.repeat_interleave(snake_case__		, dim=0    )
             UpperCAmelCase     :			str     	=negative_image_embeds.repeat_interleave(snake_case__		, dim=0    )
             UpperCAmelCase     :			Union[str, Any]     	=hint.repeat_interleave(snake_case__		, dim=0    )

       UpperCAmelCase     :			Optional[Any]     	=torch.cat([negative_image_embeds, image_embeds]		, dim=0    ).to(dtype=self.unet.dtype		, device=snake_case__    )
       UpperCAmelCase     :			Tuple     	=torch.cat([hint, hint]		, dim=0    ).to(dtype=self.unet.dtype		, device=snake_case__    )

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

       UpperCAmelCase     :			List[str]     	=torch.cat([prepare_image(snake_case__		, snake_case__		, snake_case__    ) for i in image]		, dim=0    )
       UpperCAmelCase     :			Optional[int]     	=image.to(dtype=image_embeds.dtype		, device=snake_case__    )

       UpperCAmelCase     :			Union[str, Any]     	=self.movq.encode(snake_case__    )['''latents''']
       UpperCAmelCase     :			List[str]     	=latents.repeat_interleave(snake_case__		, dim=0    )
       self.scheduler.set_timesteps(snake_case__		, device=snake_case__    )
       UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=self.get_timesteps(snake_case__		, snake_case__		, snake_case__    )
       UpperCAmelCase     :			Dict     	=timesteps[:1].repeat(batch_size * num_images_per_prompt    )
       UpperCAmelCase	,    UpperCAmelCase     :			Dict     	=downscale_height_and_width(snake_case__		, snake_case__		, self.movq_scale_factor    )
       UpperCAmelCase     :			Union[str, Any]     	=self.prepare_latents(
           snake_case__		, snake_case__		, snake_case__		, snake_case__		, image_embeds.dtype		, snake_case__		, snake_case__    )
       for i, t in enumerate(self.progress_bar(snake_case__    )    ):
             # expand the latents if we are doing classifier free guidance
             UpperCAmelCase     :			Optional[int]     	=torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

             UpperCAmelCase     :			Optional[Any]     	={'''image_embeds''': image_embeds, '''hint''': hint}
             UpperCAmelCase     :			Any     	=self.unet(
                 sample=snake_case__		, timestep=snake_case__		, encoder_hidden_states=snake_case__		, added_cond_kwargs=snake_case__		, return_dict=snake_case__		, )[0]

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

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

             UpperCAmelCase     :			Any     	=self.scheduler.step(
                 snake_case__		, snake_case__		, snake_case__		, generator=snake_case__		, )[0]

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

       if output_type == "pil":
             UpperCAmelCase     :			Dict     	=self.numpy_to_pil(snake_case__    )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=snake_case__    )

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 EfficientNetImageProcessor







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=3		, snake_case__=18		, snake_case__=30		, snake_case__=400		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=[0.5, 0.5, 0.5]		, )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=size if size is not None else {'''height''': 18, '''width''': 18}
       UpperCAmelCase     :			Tuple     	=parent
       UpperCAmelCase     :			List[str]     	=batch_size
       UpperCAmelCase     :			int     	=num_channels
       UpperCAmelCase     :			List[str]     	=image_size
       UpperCAmelCase     :			str     	=min_resolution
       UpperCAmelCase     :			Optional[int]     	=max_resolution
       UpperCAmelCase     :			Optional[int]     	=do_resize
       UpperCAmelCase     :			int     	=size
       UpperCAmelCase     :			Union[str, Any]     	=do_normalize
       UpperCAmelCase     :			Dict     	=image_mean
       UpperCAmelCase     :			List[Any]     	=image_std






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''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       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   EfficientNetImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=EfficientNetImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(snake_case__		, '''image_mean'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''image_std'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_normalize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_resize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''size'''    )    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





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

       UpperCAmelCase     :			List[str]     	=self.image_processing_class.from_dict(self.image_processor_dict		, size=42    )
       self.assertEqual(image_processor.size		, {'''height''': 42, '''width''': 42}    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       UpperCAmelCase     :			Optional[Any]     	=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
       UpperCAmelCase     :			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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			int     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			List[str]     	=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
       UpperCAmelCase     :			str     	=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
       UpperCAmelCase     :			Union[str, Any]     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       UpperCAmelCase     :			int     	=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
       UpperCAmelCase     :			List[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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
