
import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      image.size
							__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
							__UpperCamelCase			:     Optional[int]         =      image.resize((w, h)		,						resample=PIL_INTERPOLATION["lanczos"])
							__UpperCamelCase			:     Optional[Any]         =      np.array(_lowerCamelCase).astype(np.floataa) / 2_5_5.0
							__UpperCamelCase			:     List[str]         =      image[None].transpose(0		,						3		,						1		,						2)
							__UpperCamelCase			:     str         =      torch.from_numpy(_lowerCamelCase)
							return 2.0 * image - 1.0




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Union[str, Any]  ,     a	:VQModel  ,     a	:UNetaDModel  ,     a	:Union[
	        DDIMScheduler,
	        PNDMScheduler,
	        LMSDiscreteScheduler,
	        EulerDiscreteScheduler,
	        EulerAncestralDiscreteScheduler,
	        DPMSolverMultistepScheduler,
	    ]  ,     )	->				List[str]:
								super().__init__()
								self.register_modules(vqvae=a  ,     unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:List[str]  ,     a	:Union[torch.Tensor, PIL.Image.Image] = None  ,     a	:Optional[int] = 1  ,     a	:Optional[int] = 1_0_0  ,     a	:Optional[float] = 0.0  ,     a	:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     )	->				Union[Tuple, ImagePipelineOutput]:
								if isinstance(a  ,     PIL.Image.Image		):
															__UpperCamelCase			:     str         =      1
								elif isinstance(a  ,     torch.Tensor		):
															__UpperCamelCase			:     Any         =      image.shape[0]
								else:
															raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(a		)}'		)

								if isinstance(a  ,     PIL.Image.Image		):
															__UpperCamelCase			:     List[str]         =      preprocess(a		)

								__UpperCamelCase     ,		__UpperCamelCase			:     str         =      image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								__UpperCamelCase			:     Any         =      (batch_size, self.unet.config.in_channels // 2, height, width)
								__UpperCamelCase			:     List[Any]         =      next(self.unet.parameters()		).dtype

								__UpperCamelCase			:     int         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=a		)

								__UpperCamelCase			:     Optional[int]         =      image.to(device=self.device  ,     dtype=a		)

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(a  ,     device=self.device		)
								__UpperCamelCase			:     Tuple         =      self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								__UpperCamelCase			:     Dict         =      latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								__UpperCamelCase			:     List[Any]         =      "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
								__UpperCamelCase			:     Any         =      {}
								if accepts_eta:
															__UpperCamelCase			:     Any         =      eta

								for t in self.progress_bar(a		):
															# concat latents and low resolution image in the channel dimension.
															__UpperCamelCase			:     Any         =      torch.cat([latents, image]  ,     dim=1		)
															__UpperCamelCase			:     Optional[int]         =      self.scheduler.scale_model_input(a  ,     a		)
															# predict the noise residual
															__UpperCamelCase			:     int         =      self.unet(a  ,     a		).sample
															# compute the previous noisy sample x_t -> x_t-1
															__UpperCamelCase			:     Optional[int]         =      self.scheduler.step(a  ,     a  ,     a  ,     **a		).prev_sample

								# decode the image latents with the VQVAE
								__UpperCamelCase			:     Dict         =      self.vqvae.decode(a		).sample
								__UpperCamelCase			:     Union[str, Any]         =      torch.clamp(a  ,     -1.0  ,     1.0		)
								__UpperCamelCase			:     str         =      image / 2 + 0.5
								__UpperCamelCase			:     str         =      image.cpu().permute(0  ,     2  ,     3  ,     1		).numpy()

								if output_type == "pil":
															__UpperCamelCase			:     Optional[int]         =      self.numpy_to_pil(a		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)