
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 :			Union[str, Any]              =	logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase :			Optional[int]              =	"\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 				A	( lowercase   ,	lowercase   ,	lowercase=8			)       ->  int:
		'''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     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				)  -> List[str]:
				"""simple docstring"""
				super().__init__()

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				if latents is None:
						UpperCamelCase          =					randn_tensor(A_    ,				generator=A_    ,				device=A_    ,				dtype=A_	)
				else:
						if latents.shape != shape:
								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
						UpperCamelCase          =					latents.to(A_	)

				UpperCamelCase          =					latents * scheduler.init_noise_sigma
				return latents



		def __UpperCamelCase			(			self    ,				A_=0	)  -> str:
				"""simple docstring"""
				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(A_    ,				A_	)



		def __UpperCamelCase			(			self    ,				A_=0	)  -> List[str]:
				"""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          =					torch.device(F'''cuda:{gpu_id}'''	)

				if self.device.type != "cpu":
						self.to('cpu'    ,				silence_dtype_warnings=A_	)
						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     ,  UpperCamelCase          =					cpu_offload_with_hook(A_    ,				A_    ,				prev_module_hook=A_	)

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



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				if not hasattr(self.unet    ,				'_hf_hook'	):
						return self.device
				for module in self.unet.modules():
						if (
						    hasattr(A_    ,				'_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(A_	)
		def __call__(			self    ,				A_    ,				A_    ,				A_ = 512    ,				A_ = 512    ,				A_ = 100    ,				A_ = 4.0    ,				A_ = 1    ,				A_ = None    ,				A_ = None    ,				A_ = "pil"    ,				A_ = True    ,				)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self._execution_device

				UpperCamelCase          =					guidance_scale > 1.0

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

				if do_classifier_free_guidance:
						UpperCamelCase          =					image_embeds.repeat_interleave(A_    ,				dim=0	)
						UpperCamelCase          =					negative_image_embeds.repeat_interleave(A_    ,				dim=0	)

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

				self.scheduler.set_timesteps(A_    ,				device=A_	)
				UpperCamelCase          =					self.scheduler.timesteps

				UpperCamelCase          =					self.unet.config.in_channels

				UpperCamelCase     ,  UpperCamelCase          =					downscale_height_and_width(A_    ,				A_    ,				self.movq_scale_factor	)

				# create initial latent
				UpperCamelCase          =					self.prepare_latents(
				    (batch_size, num_channels_latents, height, width)    ,				image_embeds.dtype    ,				A_    ,				A_    ,				A_    ,				self.scheduler    ,				)

				for i, t in enumerate(self.progress_bar(A_	)	):
						# 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=A_    ,				timestep=A_    ,				encoder_hidden_states=A_    ,				added_cond_kwargs=A_    ,				return_dict=A_    ,				)[0]

						if do_classifier_free_guidance:
								UpperCamelCase     ,  UpperCamelCase          =					noise_pred.split(latents.shape[1]    ,				dim=1	)
								UpperCamelCase     ,  UpperCamelCase          =					noise_pred.chunk(2	)
								UpperCamelCase     ,  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     ,  UpperCamelCase          =					noise_pred.split(latents.shape[1]    ,				dim=1	)

						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase          =					self.scheduler.step(
						    A_    ,				A_    ,				A_    ,				generator=A_    ,				)[0]
				# post-processing
				UpperCamelCase          =					self.movq.decode(A_    ,				force_not_quantize=A_	)['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(A_	)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_	)


import logging
import os

from .state import PartialState


class     lowercase (		logging.LoggerAdapter       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					PartialState()
				return not main_process_only or (main_process_only and state.is_main_process)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				*A_    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if PartialState._shared_state == {}:
						raise RuntimeError(
						    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'	)
				UpperCamelCase          =					kwargs.pop('main_process_only'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('in_order'    ,				A_	)

				if self.isEnabledFor(A_	):
						if self._should_log(A_	):
								UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
								self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)

						elif in_order:
								UpperCamelCase          =					PartialState()
								for i in range(state.num_processes	):
										if i == state.process_index:
												UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
												self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)
										state.wait_for_everyone()






def 				A	( lowercase   ,	lowercase = None			)       ->  Dict:
		'''simple docstring'''

		if log_level is None:
				UpperCamelCase          =					os.environ.get('ACCELERATE_LOG_LEVEL'   ,	lowercase			)
		UpperCamelCase          =					logging.getLogger(lowercase			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(lowercase   ,	{}			)
