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,
)


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

__lowerCAmelCase	:       Dict						       =	'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Tuple=8       ):


	"""simple docstring"""
	__UpperCAmelCase							=			height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	__UpperCAmelCase							=			width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






class 						A   (   UpperCAmelCase   ):





							def __init__(      self				:							Optional[Any]     ,		__a				:							UNetaDConditionModel     ,		__a				:							DDPMScheduler     ,		__a				:							VQModel     ,		)							->	Tuple:
								super().__init__()

								self.register_modules(
								    unet=UpperCAmelCase__     ,		scheduler=UpperCAmelCase__     ,		movq=UpperCAmelCase__     ,		)
								__UpperCAmelCase							=			2 ** (len(self.movq.config.block_out_channels	) - 1)





							def       snake_case__						(      self				:							int     ,		__a				:							List[Any]     ,		__a				:							Dict     ,		__a				:							Optional[Any]     ,		__a				:							Tuple     ,		__a				:							Tuple     ,		__a				:							List[Any]	)							->	int:
								if latents is None:
									__UpperCAmelCase							=			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							=			latents.to(UpperCAmelCase__	)

								__UpperCAmelCase							=			latents * scheduler.init_noise_sigma
								return latents





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

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

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





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							int=0	)							->	str:
								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							=			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							=			None
								for cpu_offloaded_model in [self.unet, self.movq]:
									__UpperCAmelCase							=			cpu_offload_with_hook(UpperCAmelCase__     ,		UpperCAmelCase__     ,		prev_module_hook=UpperCAmelCase__	)

								# We'll offload the last model manually.
								__UpperCAmelCase							=			hook





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def       snake_case__						(      self				:							List[Any]	)							->	int:
								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				:							Any     ,		__a				:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__a				:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__a				:							int = 5_1_2     ,		__a				:							int = 5_1_2     ,		__a				:							int = 1_0_0     ,		__a				:							float = 4.0     ,		__a				:							int = 1     ,		__a				:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__a				:							Optional[torch.FloatTensor] = None     ,		__a				:							Optional[str] = "pil"     ,		__a				:							bool = True     ,		)							->	Optional[Any]:
								__UpperCAmelCase							=			self._execution_device

								__UpperCAmelCase							=			guidance_scale > 1.0

								if isinstance(UpperCAmelCase__     ,		UpperCAmelCase__	):
									__UpperCAmelCase							=			torch.cat(UpperCAmelCase__     ,		dim=0	)
								__UpperCAmelCase							=			image_embeds.shape[0] * num_images_per_prompt
								if isinstance(UpperCAmelCase__     ,		UpperCAmelCase__	):
									__UpperCAmelCase							=			torch.cat(UpperCAmelCase__     ,		dim=0	)

								if do_classifier_free_guidance:
									__UpperCAmelCase							=			image_embeds.repeat_interleave(UpperCAmelCase__     ,		dim=0	)
									__UpperCAmelCase							=			negative_image_embeds.repeat_interleave(UpperCAmelCase__     ,		dim=0	)

								__UpperCAmelCase							=			torch.cat([negative_image_embeds, image_embeds]     ,		dim=0	).to(dtype=self.unet.dtype     ,		device=UpperCAmelCase__	)

								self.scheduler.set_timesteps(UpperCAmelCase__     ,		device=UpperCAmelCase__	)
								__UpperCAmelCase							=			self.scheduler.timesteps

								__UpperCAmelCase							=			self.unet.config.in_channels

								__UpperCAmelCase							=			downscale_height_and_width(UpperCAmelCase__     ,		UpperCAmelCase__     ,		self.movq_scale_factor	)

								# create initial latent
								__UpperCAmelCase							=			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							=			torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

									__UpperCAmelCase							=			{'''image_embeds''': image_embeds}
									__UpperCAmelCase							=			self.unet(
									    sample=UpperCAmelCase__     ,		timestep=UpperCAmelCase__     ,		encoder_hidden_states=UpperCAmelCase__     ,		added_cond_kwargs=UpperCAmelCase__     ,		return_dict=UpperCAmelCase__     ,		)[0]

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

									# compute the previous noisy sample x_t -> x_t-1
									__UpperCAmelCase							=			self.scheduler.step(
									    UpperCAmelCase__     ,		UpperCAmelCase__     ,		UpperCAmelCase__     ,		generator=UpperCAmelCase__     ,		)[0]
								# post-processing
								__UpperCAmelCase							=			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							=			image * 0.5 + 0.5
									__UpperCAmelCase							=			image.clamp(0     ,		1	)
									__UpperCAmelCase							=			image.cpu().permute(0     ,		2     ,		3     ,		1	).float().numpy()

								if output_type == "pil":
									__UpperCAmelCase							=			self.numpy_to_pil(UpperCAmelCase__	)

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=UpperCAmelCase__	)

'''simple docstring'''


from ...configuration_utils import PretrainedConfig






class 						A   (   UpperCAmelCase   ):
							a_        =      '''bert-generation'''





							def __init__(      self				:							str     ,		__a				:							str=5_0_3_5_8     ,		__a				:							int=1_0_2_4     ,		__a				:							Optional[Any]=2_4     ,		__a				:							Any=1_6     ,		__a				:							int=4_0_9_6     ,		__a				:							Any="gelu"     ,		__a				:							Union[str, Any]=0.1     ,		__a				:							Any=0.1     ,		__a				:							Union[str, Any]=5_1_2     ,		__a				:							int=0.0_2     ,		__a				:							str=1e-12     ,		__a				:							List[str]=0     ,		__a				:							Optional[int]=2     ,		__a				:							Tuple=1     ,		__a				:							str="absolute"     ,		__a				:							Optional[Any]=True     ,		**__a				:							Tuple     ,		)							->	Any:
								super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			position_embedding_type
								__UpperCAmelCase							=			use_cache
