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

lowercase			:				str    		=			'\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_				(   snake_case__		,				snake_case__		,				snake_case__=8						):




    '''simple docstring'''



    A       :   int					=						height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    A       :   Union[str, Any]					=						width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=512		,				snake_case__=512						):




    '''simple docstring'''



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







class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				Any:






         """simple docstring"""






         super().__init__()

         self.register_modules(
             unet=SCREAMING_SNAKE_CASE						,			scheduler=SCREAMING_SNAKE_CASE						,			movq=SCREAMING_SNAKE_CASE						,			)
         A       :   Dict					=						2 ** (len(self.movq.config.block_out_channels	) - 1)





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






         """simple docstring"""






         A       :   Any					=						min(int(num_inference_steps * strength	)						,			SCREAMING_SNAKE_CASE	)

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

         return timesteps, num_inference_steps - t_start





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None	) ->				List[Any]:






         """simple docstring"""






         if not isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE	)}'	)

         A       :   str					=						image.to(device=SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)

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

         if image.shape[1] == 4:
             A       :   Optional[int]					=						image

         else:
             if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) and len(SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(
                     F'You have passed a list of generators of length {len(SCREAMING_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(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
                 A       :   Union[str, Any]					=						[
                     self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(SCREAMING_SNAKE_CASE	)
                 ]
                 A       :   Optional[int]					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)
             else:
                 A       :   Optional[Any]					=						self.movq.encode(SCREAMING_SNAKE_CASE	).latent_dist.sample(SCREAMING_SNAKE_CASE	)

             A       :   str					=						self.movq.config.scaling_factor * init_latents

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

         A       :   Tuple					=						init_latents.shape
         A       :   str					=						randn_tensor(SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			device=SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)

         # get latents
         A       :   str					=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						init_latents

         return latents





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=0	) ->				List[Any]:






         """simple docstring"""






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

         A       :   List[Any]					=						torch.device(F'cuda:{gpu_id}'	)

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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=0	) ->				Tuple:






         """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.'''	)

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

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

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

         # We'll offload the last model manually.
         A       :   List[str]					=						hook





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






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






         """simple docstring"""






         A       :   Tuple					=						self._execution_device

         A       :   Any					=						guidance_scale > 1.0

         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   str					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)
         A       :   str					=						image_embeds.shape[0]
         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   Dict					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)

         if do_classifier_free_guidance:
             A       :   Union[str, Any]					=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE						,			dim=0	)
             A       :   List[str]					=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE						,			dim=0	)

         A       :   Any					=						torch.cat([negative_image_embeds, image_embeds]						,			dim=0	).to(dtype=self.unet.dtype						,			device=SCREAMING_SNAKE_CASE	)

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

         A       :   Tuple					=						torch.cat([prepare_image(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for i in image]						,			dim=0	)
         A       :   Tuple					=						image.to(dtype=image_embeds.dtype						,			device=SCREAMING_SNAKE_CASE	)

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

             A       :   Dict					=						{'''image_embeds''': image_embeds}
             A       :   Tuple					=						self.unet(
                 sample=SCREAMING_SNAKE_CASE						,			timestep=SCREAMING_SNAKE_CASE						,			encoder_hidden_states=SCREAMING_SNAKE_CASE						,			added_cond_kwargs=SCREAMING_SNAKE_CASE						,			return_dict=SCREAMING_SNAKE_CASE						,			)[0]

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

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

         # post-processing
         A       :   Tuple					=						self.movq.decode(SCREAMING_SNAKE_CASE						,			force_not_quantize=SCREAMING_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"]:
             A       :   Union[str, Any]					=						image * 0.5 + 0.5
             A       :   Dict					=						image.clamp(0						,			1	)
             A       :   Any					=						image.cpu().permute(0						,			2						,			3						,			1	).float().numpy()

         if output_type == "pil":
             A       :   int					=						self.numpy_to_pil(SCREAMING_SNAKE_CASE	)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE	)












import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


a		:       Optional[Any]			     = {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

a		:       Optional[Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	'maskformer'
      a  : Dict							       =	{'hidden_size': 'mask_feature_size'}
      a  : Optional[Any]							       =	['resnet', 'swin']
      a  : List[Any]							       =	['detr']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int = 256		,      __lowercase					:			int = 256		,      __lowercase					:			float = 0.1		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			float = 0.02		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 20.0		,      __lowercase					:			Optional[bool] = None		,      **__lowercase					:			Tuple		,      )							->      str:
        if backbone_config is None:
          # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
          __UpperCAmelCase							:	List[str]           =  SwinConfig(
              image_size=384		,      in_channels=3		,      patch_size=4		,      embed_dim=128		,      depths=[2, 2, 18, 2]		,      num_heads=[4, 8, 16, 32]		,      window_size=12		,      drop_path_rate=0.3		,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		,      )

        if isinstance(__lowercase		,      __lowercase					):
          __UpperCAmelCase							:	Dict           =  backbone_config.pop("""model_type"""					)
          __UpperCAmelCase							:	Tuple           =  CONFIG_MAPPING[backbone_model_type]
          __UpperCAmelCase							:	List[str]           =  config_class.from_dict(__lowercase					)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
          logger.warning_once(
              f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
              f"""Supported model types: {",".join(self.backbones_supported					)}"""					)

        if decoder_config is None:
          # fall back to https://huggingface.co/facebook/detr-resnet-50
          __UpperCAmelCase							:	List[Any]           =  DetrConfig()
        else:
          # verify that the decoder is supported
          __UpperCAmelCase							:	List[Any]           =  (
              decoder_config.pop("""model_type"""					) if isinstance(__lowercase		,      __lowercase					) else decoder_config.model_type
          )
          if decoder_type not in self.decoders_supported:
            raise ValueError(
                f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                f""" {",".join(self.decoders_supported					)}"""					)
          if isinstance(__lowercase		,      __lowercase					):
            __UpperCAmelCase							:	Dict           =  CONFIG_MAPPING[decoder_type]
            __UpperCAmelCase							:	Union[str, Any]           =  config_class.from_dict(__lowercase					)

        __UpperCAmelCase							:	Optional[Any]           =  backbone_config
        __UpperCAmelCase							:	List[str]           =  decoder_config
        # main feature dimension for the model
        __UpperCAmelCase							:	Union[str, Any]           =  fpn_feature_size
        __UpperCAmelCase							:	Optional[Any]           =  mask_feature_size
        # initializer
        __UpperCAmelCase							:	int           =  init_std
        __UpperCAmelCase							:	Any           =  init_xavier_std
        # Hungarian matcher && loss
        __UpperCAmelCase							:	Any           =  cross_entropy_weight
        __UpperCAmelCase							:	Optional[Any]           =  dice_weight
        __UpperCAmelCase							:	List[str]           =  mask_weight
        __UpperCAmelCase							:	Union[str, Any]           =  use_auxiliary_loss
        __UpperCAmelCase							:	int           =  no_object_weight
        __UpperCAmelCase							:	int           =  output_auxiliary_logits

        __UpperCAmelCase							:	Optional[Any]           =  self.decoder_config.encoder_attention_heads
        __UpperCAmelCase							:	Dict           =  self.decoder_config.num_hidden_layers
        super().__init__(**__lowercase					)
      @classmethod
      def 	UpperCAmelCase      (      cls					:			int		,      __lowercase					:			PretrainedConfig		,      __lowercase					:			PretrainedConfig		,      **__lowercase					:			str					)							->      Tuple:
        return cls(
            backbone_config=__lowercase		,      decoder_config=__lowercase		,      **__lowercase		,      )




      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Dict[str, any]:
        __UpperCAmelCase							:	Optional[Any]           =  copy.deepcopy(self.__dict__					)
        __UpperCAmelCase							:	Optional[int]           =  self.backbone_config.to_dict()
        __UpperCAmelCase							:	Any           =  self.decoder_config.to_dict()
        __UpperCAmelCase							:	Tuple           =  self.__class__.model_type
        return output


