


'''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,
)


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

lowercase				='\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				Union[str, Any]			,       __lowerCamelCase			:				Optional[Any]=8				):







   '''simple docstring'''




   _UpperCAmelCase  :  int				       =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			:				Dict			,       __lowerCamelCase			:				List[Any]=5_1_2			,       __lowerCamelCase			:				List[str]=5_1_2				):







   '''simple docstring'''




   _UpperCAmelCase  :  Any				       =pil_image.resize((w, h)			,       resample=Image.BICUBIC			,       reducing_gap=1				)
   _UpperCAmelCase  :  str				       =np.array(pil_image.convert('RGB'				)				)
   _UpperCAmelCase  :  Tuple				       =arr.astype(np.floataa				) / 1_27.5 - 1
   _UpperCAmelCase  :  Dict				       =np.transpose(__lowerCamelCase			,       [2, 0, 1]				)
   _UpperCAmelCase  :  Dict				       =torch.from_numpy(__lowerCamelCase				).unsqueeze(0				)
   return image






class    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				)		->    Dict:




          '''simple docstring'''
          super().__init__()

          self.register_modules(
              unet=snake_case    ,				scheduler=snake_case    ,				movq=snake_case    ,				)
          _UpperCAmelCase  :  Any				       =2 ** (len(self.movq.config.block_out_channels) - 1)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    Tuple:




          '''simple docstring'''
          # get the original timestep using init_timestep
          _UpperCAmelCase  :  Dict				       =min(int(num_inference_steps * strength)    ,				snake_case)

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

          return timesteps, num_inference_steps - t_start






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case=None)		->    Dict:




          '''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  :  str				       =image.to(device=snake_case    ,				dtype=snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       =batch_size * num_images_per_prompt

          if image.shape[1] == 4:
             _UpperCAmelCase  :  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  :  Optional[int]				       =[
                    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(snake_case)
                ]
                _UpperCAmelCase  :  str				       =torch.cat(snake_case    ,				dim=0)
             else:
                _UpperCAmelCase  :  Union[str, Any]				       =self.movq.encode(snake_case).latent_dist.sample(snake_case)

             _UpperCAmelCase  :  Any				       =self.movq.config.scaling_factor * init_latents

          _UpperCAmelCase  :  Optional[int]				       =torch.cat([init_latents]    ,				dim=0)

          _UpperCAmelCase  :  Dict				       =init_latents.shape
          _UpperCAmelCase  :  Union[str, Any]				       =randn_tensor(snake_case    ,				generator=snake_case    ,				device=snake_case    ,				dtype=snake_case)

          # get latents
          _UpperCAmelCase  :  int				       =self.scheduler.add_noise(snake_case    ,				snake_case    ,				snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       =init_latents

          return latents






       def      lowerCAmelCase						(     self    ,				snake_case=0)		->    Tuple:




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

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

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






       def      lowerCAmelCase						(     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  :  str				       =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  :  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      lowerCAmelCase						(     self)		->    Tuple:




          '''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 = 5_1_2    ,				snake_case = 5_1_2    ,				snake_case = 1_0_0    ,				snake_case = 4.0    ,				snake_case = 0.3    ,				snake_case = 1    ,				snake_case = None    ,				snake_case = "pil"    ,				snake_case = True    ,				)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self._execution_device

          _UpperCAmelCase  :  int				       =guidance_scale > 1.0

          if isinstance(snake_case    ,				snake_case):
             _UpperCAmelCase  :  Dict				       =torch.cat(snake_case    ,				dim=0)
          _UpperCAmelCase  :  Optional[Any]				       =image_embeds.shape[0]
          if isinstance(snake_case    ,				snake_case):
             _UpperCAmelCase  :  List[Any]				       =torch.cat(snake_case    ,				dim=0)

          if do_classifier_free_guidance:
             _UpperCAmelCase  :  Optional[int]				       =image_embeds.repeat_interleave(snake_case    ,				dim=0)
             _UpperCAmelCase  :  Optional[int]				       =negative_image_embeds.repeat_interleave(snake_case    ,				dim=0)

          _UpperCAmelCase  :  Tuple				       =torch.cat([negative_image_embeds, image_embeds]    ,				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  :  Optional[int]				       =torch.cat([prepare_image(snake_case    ,				snake_case    ,				snake_case) for i in image]    ,				dim=0)
          _UpperCAmelCase  :  str				       =image.to(dtype=image_embeds.dtype    ,				device=snake_case)

          _UpperCAmelCase  :  List[Any]				       =self.movq.encode(snake_case)['latents']
          _UpperCAmelCase  :  int				       =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  :  List[Any]				       =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  :  Tuple				       =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  :  List[str]				       =torch.cat([latents] * 2) if do_classifier_free_guidance else latents

             _UpperCAmelCase  :  int				       ={'image_embeds': image_embeds}
             _UpperCAmelCase  :  Union[str, 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  :  Optional[Any]				       =noise_pred.split(latents.shape[1]    ,				dim=1)
                _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[int]				       =noise_pred.chunk(2)
                _UpperCAmelCase       ,			_UpperCAmelCase  :  Any				       =variance_pred.chunk(2)
                _UpperCAmelCase  :  Union[str, Any]				       =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                _UpperCAmelCase  :  int				       =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  :  Dict				       =noise_pred.split(latents.shape[1]    ,				dim=1)

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

          # post-processing
          _UpperCAmelCase  :  Tuple				       =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  :  List[str]				       =image * 0.5 + 0.5
             _UpperCAmelCase  :  Any				       =image.clamp(0    ,				1)
             _UpperCAmelCase  :  List[str]				       =image.cpu().permute(0    ,				2    ,				3    ,				1).float().numpy()

          if output_type == "pil":
             _UpperCAmelCase  :  Union[str, Any]				       =self.numpy_to_pil(snake_case)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=snake_case)





'''simple docstring'''






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
 from PIL import Image
else:






 class    __magic_name__				:






        @staticmethod
        def      lowerCAmelCase						(     *snake_case    ,				**snake_case)		->    str:




           '''simple docstring'''
           pass





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =hashlib.mda(image.tobytes()				)
   return m.hexdigest()[:1_0]





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =np.array(__lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =npimg.shape
   return {"hash": hashimage(__lowerCamelCase				), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class    __magic_name__				(						unittest.TestCase							):
       UpperCAmelCase		 =dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       UpperCAmelCase		 =dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =MaskGenerationPipeline(model=snake_case    ,				image_processor=snake_case)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          pass






       @require_tf
       @unittest.skip('Image segmentation not implemented in TF')
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          pass






       @slow
       @require_torch
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =pipeline('mask-generation'    ,				model='facebook/sam-vit-huge')

          _UpperCAmelCase  :  Union[str, Any]				       =image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  str				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_71}
              ]    ,				)
          # fmt: on






       @require_torch
       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       ='facebook/sam-vit-huge'
          _UpperCAmelCase  :  Optional[int]				       =pipeline('mask-generation'    ,				model=snake_case)

          _UpperCAmelCase  :  str				       =image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'    ,				pred_iou_thresh=1    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  Tuple				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
              ]    ,				)

