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       A__   (						snake_case_			:		Any   ):
 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[str]=   image.size
 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: int=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
 SCREAMING_SNAKE_CASE__: Tuple=   image.resize((w, h)							,					resample=PIL_INTERPOLATION['''lanczos''']   )
 SCREAMING_SNAKE_CASE__: List[Any]=   np.array(snake_case_   ).astype(np.floataa   ) / 2_55.0
 SCREAMING_SNAKE_CASE__: Optional[int]=   image[None].transpose(0							,					3							,					1							,					2   )
 SCREAMING_SNAKE_CASE__: Dict=   torch.from_numpy(snake_case_   )
 return 2.0 * image - 1.0






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			)							->       List[Any]:
     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			,			)							->       Union[Tuple, ImagePipelineOutput]:
     if isinstance(lowerCAmelCase			,			PIL.Image.Image ):
      SCREAMING_SNAKE_CASE__: str=   1
     elif isinstance(lowerCAmelCase			,			torch.Tensor ):
      SCREAMING_SNAKE_CASE__: int=   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 ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   preprocess(lowerCAmelCase )

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

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

     SCREAMING_SNAKE_CASE__: Optional[int]=   randn_tensor(lowerCAmelCase			,			generator=lowerCAmelCase			,			device=self.device			,			dtype=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   image.to(device=self.device			,			dtype=lowerCAmelCase )

     # set timesteps and move to the correct device
     self.scheduler.set_timesteps(lowerCAmelCase			,			device=self.device )
     SCREAMING_SNAKE_CASE__: Any=   self.scheduler.timesteps

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

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

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

     if output_type == "pil":
      SCREAMING_SNAKE_CASE__: Tuple=   self.numpy_to_pil(lowerCAmelCase )

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase )





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

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





