




'''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 		snake_case_   (UpperCamelCase	:  Optional[Any]				):





							'''simple docstring'''


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

class       A   (		_a   ):







							def __init__(  self	:  Any							,      lowerCAmelCase_	:  VQModel							,      lowerCAmelCase_	:  UNetaDModel							,      lowerCAmelCase_	:  Union[
							        DDIMScheduler,
							        PNDMScheduler,
							        LMSDiscreteScheduler,
							        EulerDiscreteScheduler,
							        EulerAncestralDiscreteScheduler,
							        DPMSolverMultistepScheduler,
							    ]							,      )      ->							int:


														"""simple docstring"""


														super().__init__()
														self.register_modules(vqvae=lowerCAmelCase_							,      unet=lowerCAmelCase_							,      scheduler=lowerCAmelCase_ )







							@torch.no_grad()
							def __call__(  self	:  Tuple							,      lowerCAmelCase_	:  Union[torch.Tensor, PIL.Image.Image] = None							,      lowerCAmelCase_	:  Optional[int] = 1							,      lowerCAmelCase_	:  Optional[int] = 1_00							,      lowerCAmelCase_	:  Optional[float] = 0.0							,      lowerCAmelCase_	:  Optional[Union[torch.Generator, List[torch.Generator]]] = None							,      lowerCAmelCase_	:  Optional[str] = "pil"							,      lowerCAmelCase_	:  bool = True							,      )      ->							Union[Tuple, ImagePipelineOutput]:


														"""simple docstring"""


														if isinstance(lowerCAmelCase_							,      PIL.Image.Image ):
																					_a     					=    1
														elif isinstance(lowerCAmelCase_							,      torch.Tensor ):
																					_a     					=    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 ):
																					_a     					=    preprocess(lowerCAmelCase_ )

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

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

														_a     					=    randn_tensor(lowerCAmelCase_							,      generator=lowerCAmelCase_							,      device=self.device							,      dtype=lowerCAmelCase_ )

														_a     					=    image.to(device=self.device							,      dtype=lowerCAmelCase_ )

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

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

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

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

														if output_type == "pil":
																					_a     					=    self.numpy_to_pil(lowerCAmelCase_ )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowerCAmelCase_ )







'''simple docstring'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

