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

__lowercase							:				Any					=       "\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 lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase=8			)       ->							Union[str, Any]:
     '''simple docstring'''

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




def lowercase_		(				_lowercase     ,    _lowercase=512     ,    _lowercase=512			)       ->							List[str]:
     '''simple docstring'''

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






class 		__lowercase			(					lowerCAmelCase_      ):

   def __init__(self     ,				A     ,				A     ,				A     ,				):
        super().__init__()

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

   def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
        # get the original timestep using init_timestep
        lowerCamelCase_   : List[Any]       =						min(int(num_inference_steps * strength )     ,				_snake_case )

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

        return timesteps, num_inference_steps - t_start

   def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A=None ):
        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 )}""" )

        lowerCamelCase_   : Any       =						image.to(device=_snake_case     ,				dtype=_snake_case )

        lowerCamelCase_   : Dict       =						batch_size * num_images_per_prompt

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

             lowerCamelCase_   : List[Any]       =						self.movq.config.scaling_factor * init_latents

        lowerCamelCase_   : List[str]       =						torch.cat([init_latents]     ,				dim=0 )

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

        # get latents
        lowerCamelCase_   : Dict       =						self.scheduler.add_noise(_snake_case     ,				_snake_case     ,				_snake_case )

        lowerCamelCase_   : Union[str, Any]       =						init_latents

        return latents

   def     UpperCAmelCase__	(self     ,				A=0 ):
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('''Please install accelerate via `pip install accelerate`''' )

        lowerCamelCase_   : Any       =						torch.device(F"""cuda:{gpu_id}""" )

        lowerCamelCase_   : Union[str, Any]       =						[
            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     ,				A=0 ):
        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.''' )

        lowerCamelCase_   : List[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)

        lowerCamelCase_   : Optional[Any]       =						None
        for cpu_offloaded_model in [self.unet, self.movq]:
             lowerCamelCase_   : Union[str, Any]       =						cpu_offload_with_hook(_snake_case     ,				_snake_case     ,				prev_module_hook=_snake_case )

        # We'll offload the last model manually.
        lowerCamelCase_   : Tuple       =						hook

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def     UpperCAmelCase__	(self ):
        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     ,				A     ,				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     ,				):
        lowerCamelCase_   : Dict       =						self._execution_device

        lowerCamelCase_   : Dict       =						guidance_scale > 1.0

        if isinstance(_snake_case     ,				_snake_case ):
             lowerCamelCase_   : List[Any]       =						torch.cat(_snake_case     ,				dim=0 )
        if isinstance(_snake_case     ,				_snake_case ):
             lowerCamelCase_   : Dict       =						torch.cat(_snake_case     ,				dim=0 )
        if isinstance(_snake_case     ,				_snake_case ):
             lowerCamelCase_   : str       =						torch.cat(_snake_case     ,				dim=0 )

        lowerCamelCase_   : List[Any]       =						image_embeds.shape[0]

        if do_classifier_free_guidance:
             lowerCamelCase_   : Dict       =						image_embeds.repeat_interleave(_snake_case     ,				dim=0 )
             lowerCamelCase_   : Union[str, Any]       =						negative_image_embeds.repeat_interleave(_snake_case     ,				dim=0 )
             lowerCamelCase_   : Optional[int]       =						hint.repeat_interleave(_snake_case     ,				dim=0 )

        lowerCamelCase_   : str       =						torch.cat([negative_image_embeds, image_embeds]     ,				dim=0 ).to(dtype=self.unet.dtype     ,				device=_snake_case )
        lowerCamelCase_   : int       =						torch.cat([hint, hint]     ,				dim=0 ).to(dtype=self.unet.dtype     ,				device=_snake_case )

        if not isinstance(_snake_case     ,				_snake_case ):
             lowerCamelCase_   : int       =						[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""" )

        lowerCamelCase_   : str       =						torch.cat([prepare_image(_snake_case     ,				_snake_case     ,				_snake_case ) for i in image]     ,				dim=0 )
        lowerCamelCase_   : Dict       =						image.to(dtype=image_embeds.dtype     ,				device=_snake_case )

        lowerCamelCase_   : Dict       =						self.movq.encode(_snake_case )['latents']
        lowerCamelCase_   : str       =						latents.repeat_interleave(_snake_case     ,				dim=0 )
        self.scheduler.set_timesteps(_snake_case     ,				device=_snake_case )
        lowerCamelCase_   : int       =						self.get_timesteps(_snake_case     ,				_snake_case     ,				_snake_case )
        lowerCamelCase_   : Union[str, Any]       =						timesteps[:1].repeat(batch_size * num_images_per_prompt )
        lowerCamelCase_   : Optional[Any]       =						downscale_height_and_width(_snake_case     ,				_snake_case     ,				self.movq_scale_factor )
        lowerCamelCase_   : str       =						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
             lowerCamelCase_   : Dict       =						torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

             lowerCamelCase_   : Optional[int]       =						{'image_embeds': image_embeds, 'hint': hint}
             lowerCamelCase_   : Optional[int]       =						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:
                  lowerCamelCase_   : Tuple       =						noise_pred.split(latents.shape[1]     ,				dim=1 )
                  lowerCamelCase_   : List[Any]       =						noise_pred.chunk(2 )
                  lowerCamelCase_   : int       =						variance_pred.chunk(2 )
                  lowerCamelCase_   : List[Any]       =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  lowerCamelCase_   : Optional[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"]
             ):
                  lowerCamelCase_   : List[Any]       =						noise_pred.split(latents.shape[1]     ,				dim=1 )

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

             lowerCamelCase_   : int       =						self.scheduler.step(
                 _snake_case     ,				_snake_case     ,				_snake_case     ,				generator=_snake_case     ,				)[0]

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

        if output_type == "pil":
             lowerCamelCase_   : Tuple       =						self.numpy_to_pil(_snake_case )

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case )




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowercase							:				List[str]					=       {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				int					=       [
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

					__lowercase							:				List[str]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


