



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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Any				)->Optional[Any]:
						_lowerCAmelCase			,   _lowerCAmelCase 			=  image.size
						_lowerCAmelCase			,   _lowerCAmelCase 			=  (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
						_lowerCAmelCase 			=  image.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']				)
						_lowerCAmelCase 			=  np.array(_SCREAMING_SNAKE_CASE				).astype(np.floataa				) / 255.0
						_lowerCAmelCase 			=  image[None].transpose(0     ,				3     ,				1     ,				2				)
						_lowerCAmelCase 			=  torch.from_numpy(_SCREAMING_SNAKE_CASE				)
						return 2.0 * image - 1.0





class 		UpperCAmelCase						(						snake_case_				):





			def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
									super().__init__()
									self.register_modules(vqvae=_lowerCAmelCase       ,     unet=_lowerCAmelCase       ,     scheduler=_lowerCAmelCase			)





			@torch.no_grad()
			def __call__(	self       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = 1       ,     _lowerCAmelCase = 100       ,     _lowerCAmelCase = 0.0       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = "pil"       ,     _lowerCAmelCase = True       ,     ):
									if isinstance(_lowerCAmelCase       ,     PIL.Image.Image			):
															_lowerCAmelCase 			=  1
									elif isinstance(_lowerCAmelCase       ,     torch.Tensor			):
															_lowerCAmelCase 			=  image.shape[0]
									else:
															raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_lowerCAmelCase			)}'''			)

									if isinstance(_lowerCAmelCase       ,     PIL.Image.Image			):
															_lowerCAmelCase 			=  preprocess(_lowerCAmelCase			)

									_lowerCAmelCase			,   _lowerCAmelCase 			=  image.shape[-2:]

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

									_lowerCAmelCase 			=  randn_tensor(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     device=self.device       ,     dtype=_lowerCAmelCase			)

									_lowerCAmelCase 			=  image.to(device=self.device       ,     dtype=_lowerCAmelCase			)

									# set timesteps and move to the correct device
									self.scheduler.set_timesteps(_lowerCAmelCase       ,     device=self.device			)
									_lowerCAmelCase 			=  self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									_lowerCAmelCase 			=  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 			=  '''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
									_lowerCAmelCase 			=  {}
									if accepts_eta:
															_lowerCAmelCase 			=  eta

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

									# decode the image latents with the VQVAE
									_lowerCAmelCase 			=  self.vqvae.decode(_lowerCAmelCase			).sample
									_lowerCAmelCase 			=  torch.clamp(_lowerCAmelCase       ,     -1.0       ,     1.0			)
									_lowerCAmelCase 			=  image / 2 + 0.5
									_lowerCAmelCase 			=  image.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()

									if output_type == "pil":
															_lowerCAmelCase 			=  self.numpy_to_pil(_lowerCAmelCase			)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_lowerCAmelCase			)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"processing_layoutxlm": ["LayoutXLMProcessor"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["LayoutXLMTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["LayoutXLMTokenizerFast"]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)