



'''simple docstring'''
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,
)


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

snake_case_			:     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__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=8							):
						_UpperCamelCase :		Optional[int]			    =       height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_UpperCamelCase :		List[Any]			    =       width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    List[str]			,lowerCamelCase__ :    UNetaDConditionModel			,lowerCamelCase__ :    DDPMScheduler			,lowerCamelCase__ :    VQModel			,):







							'''simple docstring'''
							super().__init__()

							self.register_modules(
							    unet=lowerCamelCase__			,scheduler=lowerCamelCase__			,movq=lowerCamelCase__			,)
							_UpperCamelCase :		str			    =       2 ** (len(self.movq.config.block_out_channels			) - 1)





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							if latents is None:
													_UpperCamelCase :		Any			    =       randn_tensor(lowerCamelCase__			,generator=lowerCamelCase__			,device=lowerCamelCase__			,dtype=lowerCamelCase__			)
							else:
													if latents.shape != shape:
																			raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'			)
													_UpperCamelCase :		Tuple			    =       latents.to(lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       latents * scheduler.init_noise_sigma
							return latents





	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    str=0			):







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

							_UpperCamelCase :		str			    =       torch.device(F'cuda:{gpu_id}'			)

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





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    int=0			):







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

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

							_UpperCamelCase :		Optional[int]			    =       None
							for cpu_offloaded_model in [self.unet, self.movq]:
													_UpperCamelCase    ,      _UpperCamelCase :		int			    =       cpu_offload_with_hook(lowerCamelCase__			,lowerCamelCase__			,prev_module_hook=lowerCamelCase__			)

							# We'll offload the last model manually.
							_UpperCamelCase :		Dict			    =       hook





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							if not hasattr(self.unet			,'_hf_hook'			):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(lowerCamelCase__			,'_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(lowerCamelCase__			)
	def __call__(			self :    int			,lowerCamelCase__ :    Union[torch.FloatTensor, List[torch.FloatTensor]]			,lowerCamelCase__ :    Union[torch.FloatTensor, List[torch.FloatTensor]]			,lowerCamelCase__ :    int = 512			,lowerCamelCase__ :    int = 512			,lowerCamelCase__ :    int = 100			,lowerCamelCase__ :    float = 4.0			,lowerCamelCase__ :    int = 1			,lowerCamelCase__ :    Optional[Union[torch.Generator, List[torch.Generator]]] = None			,lowerCamelCase__ :    Optional[torch.FloatTensor] = None			,lowerCamelCase__ :    Optional[str] = "pil"			,lowerCamelCase__ :    bool = True			,):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       self._execution_device

							_UpperCamelCase :		List[str]			    =       guidance_scale > 1.0

							if isinstance(lowerCamelCase__			,lowerCamelCase__			):
													_UpperCamelCase :		Dict			    =       torch.cat(lowerCamelCase__			,dim=0			)
							_UpperCamelCase :		List[Any]			    =       image_embeds.shape[0] * num_images_per_prompt
							if isinstance(lowerCamelCase__			,lowerCamelCase__			):
													_UpperCamelCase :		str			    =       torch.cat(lowerCamelCase__			,dim=0			)

							if do_classifier_free_guidance:
													_UpperCamelCase :		Union[str, Any]			    =       image_embeds.repeat_interleave(lowerCamelCase__			,dim=0			)
													_UpperCamelCase :		int			    =       negative_image_embeds.repeat_interleave(lowerCamelCase__			,dim=0			)

							_UpperCamelCase :		Dict			    =       torch.cat([negative_image_embeds, image_embeds]			,dim=0			).to(dtype=self.unet.dtype			,device=lowerCamelCase__			)

							self.scheduler.set_timesteps(lowerCamelCase__			,device=lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       self.scheduler.timesteps

							_UpperCamelCase :		Optional[Any]			    =       self.unet.config.in_channels

							_UpperCamelCase    ,      _UpperCamelCase :		List[Any]			    =       downscale_height_and_width(lowerCamelCase__			,lowerCamelCase__			,self.movq_scale_factor			)

							# create initial latent
							_UpperCamelCase :		str			    =       self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)			,image_embeds.dtype			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,self.scheduler			,)

							for i, t in enumerate(self.progress_bar(lowerCamelCase__			)			):
													# expand the latents if we are doing classifier free guidance
													_UpperCamelCase :		Dict			    =       torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

													_UpperCamelCase :		List[str]			    =       {'image_embeds': image_embeds}
													_UpperCamelCase :		Optional[Any]			    =       self.unet(
													    sample=lowerCamelCase__			,timestep=lowerCamelCase__			,encoder_hidden_states=lowerCamelCase__			,added_cond_kwargs=lowerCamelCase__			,return_dict=lowerCamelCase__			,)[0]

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

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

							if output_type == "pil":
													_UpperCamelCase :		List[str]			    =       self.numpy_to_pil(lowerCamelCase__			)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowerCamelCase__			)







'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch






@require_torch
@require_optimum
@slow
class 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		str			    =       AutoTokenizer.from_pretrained(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       tokenizer('This is me'			,return_tensors='pt'			)

							_UpperCamelCase :		str			    =       model.to_bettertransformer()

							self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							_UpperCamelCase :		Optional[Any]			    =       model.generate(**lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       model.reverse_bettertransformer()

							self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

													self.assertFalse(
													    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

													_UpperCamelCase :		Optional[Any]			    =       model_reloaded.generate(**lowerCamelCase__			)
													self.assertTrue(torch.allclose(lowerCamelCase__			,lowerCamelCase__			)			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
													with self.assertRaises(lowerCamelCase__			):
																			model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		str			    =       model.reverse_bettertransformer()
													model.save_pretrained(lowerCamelCase__			)


