






'''simple docstring'''







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_ (UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			Any 						= image.size
							lowerCamelCase__      :			List[str] 						= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
							lowerCamelCase__      :			List[Any] 						= image.resize((w, h)       ,    resample=PIL_INTERPOLATION["""lanczos"""]   )
							lowerCamelCase__      :			Any 						= np.array(UpperCamelCase   ).astype(np.floataa   ) / 255.0
							lowerCamelCase__      :			int 						= image[None].transpose(0       ,    3       ,    1       ,    2   )
							lowerCamelCase__      :			Optional[int] 						= torch.from_numpy(UpperCamelCase   )
							return 2.0 * image - 1.0



class _lowercase			(       _lowercase	):




							def __init__(							self:       int    ,							UpperCamelCase__:       VQModel    ,							UpperCamelCase__:       UNetaDModel    ,							UpperCamelCase__:       Union[
							        DDIMScheduler,
							        PNDMScheduler,
							        LMSDiscreteScheduler,
							        EulerDiscreteScheduler,
							        EulerAncestralDiscreteScheduler,
							        DPMSolverMultistepScheduler,
							    ]    ,							):
														super().__init__()
														self.register_modules(vqvae=UpperCamelCase__    ,							unet=UpperCamelCase__    ,							scheduler=UpperCamelCase__  )




							@torch.no_grad()
							def __call__(							self:       Union[str, Any]    ,							UpperCamelCase__:       Union[torch.Tensor, PIL.Image.Image] = None    ,							UpperCamelCase__:       Optional[int] = 1    ,							UpperCamelCase__:       Optional[int] = 100    ,							UpperCamelCase__:       Optional[float] = 0.0    ,							UpperCamelCase__:       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							UpperCamelCase__:       Optional[str] = "pil"    ,							UpperCamelCase__:       bool = True    ,							):
														if isinstance(UpperCamelCase__    ,							PIL.Image.Image  ):
																					lowerCamelCase__      :			Any 						= 1
														elif isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																					lowerCamelCase__      :			Tuple 						= image.shape[0]
														else:
																					raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__  )}'''  )

														if isinstance(UpperCamelCase__    ,							PIL.Image.Image  ):
																					lowerCamelCase__      :			List[str] 						= preprocess(UpperCamelCase__  )

														lowerCamelCase__      :			str 						= image.shape[-2:]

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

														lowerCamelCase__      :			Union[str, Any] 						= randn_tensor(UpperCamelCase__    ,							generator=UpperCamelCase__    ,							device=self.device    ,							dtype=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= image.to(device=self.device    ,							dtype=UpperCamelCase__  )

														# set timesteps and move to the correct device
														self.scheduler.set_timesteps(UpperCamelCase__    ,							device=self.device  )
														lowerCamelCase__      :			Optional[Any] 						= self.scheduler.timesteps

														# scale the initial noise by the standard deviation required by the scheduler
														lowerCamelCase__      :			str 						= 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]
														lowerCamelCase__      :			Optional[Any] 						= """eta""" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
														lowerCamelCase__      :			Tuple 						= {}
														if accepts_eta:
																					lowerCamelCase__      :			Dict 						= eta

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

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

														if output_type == "pil":
																					lowerCamelCase__      :			Dict 						= self.numpy_to_pil(UpperCamelCase__  )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCamelCase__  )










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A			:    Dict					  ={
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    Union[str, Any]					  =[
			    '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GitForCausalLM''',
			    '''GitModel''',
			    '''GitPreTrainedModel''',
			    '''GitVisionModel''',
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

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

