






"""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  (A_				:List[str]				):







  '''simple docstring'''

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






class 						snake_case  (  UpperCAmelCase	):





    def __init__(							self     : int    ,		A     : VQModel    ,		A     : UNetaDModel    ,		A     : Union[
            DDIMScheduler,
            PNDMScheduler,
            LMSDiscreteScheduler,
            EulerDiscreteScheduler,
            EulerAncestralDiscreteScheduler,
            DPMSolverMultistepScheduler,
        ]    ,		):


      '''simple docstring'''




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





    @torch.no_grad()
    def __call__(							self     : Any    ,		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    ,		):


      '''simple docstring'''




      if isinstance(A    ,		PIL.Image.Image      ):
        a					:      Optional[int]    =       1
      elif isinstance(A    ,		torch.Tensor      ):
        a					:      Optional[int]    =       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      ):
        a					:      List[Any]    =       preprocess(A      )

      a,					a					:      Union[str, Any]    =       image.shape[-2:]

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

      a					:      Tuple    =       randn_tensor(A    ,		generator=A    ,		device=self.device    ,		dtype=A      )

      a					:      Optional[Any]    =       image.to(device=self.device    ,		dtype=A      )

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

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

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

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

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

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=A      )












"""simple docstring"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_UpperCamelCase    :   Dict   			=						logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					field(default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use SortishSampler or not.'''}	)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}	)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': (
                '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                '''to the `max_length` value of the model configuration.'''
            )
        }       ,			)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': (
                '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                '''to the `num_beams` value of the model configuration.'''
            )
        }       ,			)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
        }       ,			)





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      List[Any]    =       super().to_dict()
      for k, v in d.items():
        if isinstance(A    ,		A      ):
          a					:      str    =       v.to_dict()
      return d




