



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







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







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




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

          if isinstance(snake_case_ ,     PIL.Image.Image						):
                snake_case__  :    Union[str, Any]    						=      preprocess(snake_case_						)

          snake_case__						,				snake_case__  :    Tuple    						=      image.shape[-2:]

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

          snake_case__  :    int    						=      randn_tensor(snake_case_ ,     generator=snake_case_ ,     device=self.device ,     dtype=snake_case_						)

          snake_case__  :    Optional[Any]    						=      image.to(device=self.device ,     dtype=snake_case_						)

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

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

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

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

          if output_type == "pil":
                snake_case__  :    Tuple    						=      self.numpy_to_pil(snake_case_						)

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=snake_case_						)





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase	:       List[Any]      	=	{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       int      	=	[
       "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
       "M2M100ForConditionalGeneration",
       "M2M100Model",
       "M2M100PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
   from .tokenization_mam_aaa import MaMaaaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mam_aaa import (
          M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaMaaaForConditionalGeneration,
          MaMaaaModel,
          MaMaaaPreTrainedModel,
      )


else:
   import sys

   _lowerCamelCase	:       Optional[int]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

