






from typing import List, Optional, Union

import torch

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,
)


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

__UpperCAmelCase      				=		"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""




def 	lowercase__ (	__snake_case       :				Union[str, Any]	,			__snake_case       :				Optional[int]	,			__snake_case       :				Optional[int]=8    ):


					'''simple docstring'''


					UpperCAmelCase_       : List[str]				=		height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCAmelCase_       : Dict				=		width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor






class     lowerCamelCase (lowercase_     ):







		'''simple docstring'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						)      ->				List[Any]:
							super().__init__()

							self.register_modules(
							    unet=_UpperCamelCase							,						scheduler=_UpperCamelCase							,						movq=_UpperCamelCase							,						)
							UpperCAmelCase_       : List[Any]				=		2 ** (len(self.movq.config.block_out_channels  ) - 1)



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Optional[int]:
							if latents is None:
												UpperCAmelCase_       : List[str]				=		randn_tensor(_UpperCamelCase							,						generator=_UpperCamelCase							,						device=_UpperCamelCase							,						dtype=_UpperCamelCase  )
							else:
												if latents.shape != shape:
																	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
												UpperCAmelCase_       : List[str]				=		latents.to(_UpperCamelCase  )

							UpperCAmelCase_       : Any				=		latents * scheduler.init_noise_sigma
							return latents



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase=0  )      ->				Optional[int]:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'  )

							UpperCAmelCase_       : Union[str, Any]				=		torch.device(f"cuda:{gpu_id}"  )

							UpperCAmelCase_       : Union[str, Any]				=		[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(_UpperCamelCase							,						_UpperCamelCase  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase=0  )      ->				int:
							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_       : Dict				=		torch.device(f"cuda:{gpu_id}"  )

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

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

							# We'll offload the last model manually.
							UpperCAmelCase_       : int				=		hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							if not hasattr(self.unet							,						'_hf_hook'  ):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(_UpperCamelCase							,						'_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(_UpperCamelCase  )
		def __call__(				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase = 5_1_2							,						_UpperCamelCase = 5_1_2							,						_UpperCamelCase = 1_0_0							,						_UpperCamelCase = 4.0							,						_UpperCamelCase = 1							,						_UpperCamelCase = None							,						_UpperCamelCase = None							,						_UpperCamelCase = "pil"							,						_UpperCamelCase = True							,						)      ->				int:
							UpperCAmelCase_       : List[Any]				=		self._execution_device

							UpperCAmelCase_       : Union[str, Any]				=		guidance_scale > 1.0

							if isinstance(_UpperCamelCase							,						_UpperCamelCase  ):
												UpperCAmelCase_       : List[Any]				=		torch.cat(_UpperCamelCase							,						dim=0  )
							if isinstance(_UpperCamelCase							,						_UpperCamelCase  ):
												UpperCAmelCase_       : List[Any]				=		torch.cat(_UpperCamelCase							,						dim=0  )
							if isinstance(_UpperCamelCase							,						_UpperCamelCase  ):
												UpperCAmelCase_       : Optional[Any]				=		torch.cat(_UpperCamelCase							,						dim=0  )

							UpperCAmelCase_       : str				=		image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
												UpperCAmelCase_       : int				=		image_embeds.repeat_interleave(_UpperCamelCase							,						dim=0  )
												UpperCAmelCase_       : Optional[int]				=		negative_image_embeds.repeat_interleave(_UpperCamelCase							,						dim=0  )
												UpperCAmelCase_       : Any				=		hint.repeat_interleave(_UpperCamelCase							,						dim=0  )

							UpperCAmelCase_       : int				=		torch.cat([negative_image_embeds, image_embeds]							,						dim=0  ).to(dtype=self.unet.dtype							,						device=_UpperCamelCase  )
							UpperCAmelCase_       : List[str]				=		torch.cat([hint, hint]							,						dim=0  ).to(dtype=self.unet.dtype							,						device=_UpperCamelCase  )
							self.scheduler.set_timesteps(_UpperCamelCase							,						device=_UpperCamelCase  )
							UpperCAmelCase_       : Optional[int]				=		self.scheduler.timesteps

							UpperCAmelCase_       : Optional[Any]				=		self.movq.config.latent_channels

							UpperCAmelCase_			,    UpperCAmelCase_       : int				=		downscale_height_and_width(_UpperCamelCase							,						_UpperCamelCase							,						self.movq_scale_factor  )

							# create initial latent
							UpperCAmelCase_       : Optional[Any]				=		self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)							,						image_embeds.dtype							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						self.scheduler							,						)

							for i, t in enumerate(self.progress_bar(_UpperCamelCase  )  ):
												# 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_       : List[Any]				=		{'image_embeds': image_embeds, 'hint': hint}
												UpperCAmelCase_       : Optional[int]				=		self.unet(
												    sample=_UpperCamelCase							,						timestep=_UpperCamelCase							,						encoder_hidden_states=_UpperCamelCase							,						added_cond_kwargs=_UpperCamelCase							,						return_dict=_UpperCamelCase							,						)[0]

												if do_classifier_free_guidance:
																	UpperCAmelCase_			,    UpperCAmelCase_       : List[str]				=		noise_pred.split(latents.shape[1]							,						dim=1  )
																	UpperCAmelCase_			,    UpperCAmelCase_       : int				=		noise_pred.chunk(2  )
																	UpperCAmelCase_			,    UpperCAmelCase_       : Any				=		variance_pred.chunk(2  )
																	UpperCAmelCase_       : Dict				=		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_       : List[Any]				=		noise_pred.split(latents.shape[1]							,						dim=1  )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_       : int				=		self.scheduler.step(
												    _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						generator=_UpperCamelCase							,						)[0]
							# post-processing
							UpperCAmelCase_       : Union[str, Any]				=		self.movq.decode(_UpperCamelCase							,						force_not_quantize=_UpperCamelCase  )['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_       : Dict				=		image * 0.5 + 0.5
												UpperCAmelCase_       : List[Any]				=		image.clamp(0							,						1  )
												UpperCAmelCase_       : List[str]				=		image.cpu().permute(0							,						2							,						3							,						1  ).float().numpy()

							if output_type == "pil":
												UpperCAmelCase_       : Dict				=		self.numpy_to_pil(_UpperCamelCase  )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase  )









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




