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





class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




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







     @torch.no_grad()
     def __call__(			self			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 100			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = None			,  __UpperCAmelCase = "pil"			,  __UpperCAmelCase = True			,  ):

      '''simple docstring'''




      if isinstance(__UpperCAmelCase			,  PIL.Image.Image   ):
       __UpperCamelCase       						= 1
      elif isinstance(__UpperCAmelCase			,  torch.Tensor   ):
       __UpperCamelCase       						= image.shape[0]
      else:
       raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__UpperCAmelCase   )}'   )

      if isinstance(__UpperCAmelCase			,  PIL.Image.Image   ):
       __UpperCamelCase       						= preprocess(__UpperCAmelCase   )

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

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

      __UpperCamelCase       						= randn_tensor(__UpperCAmelCase			,  generator=__UpperCAmelCase			,  device=self.device			,  dtype=__UpperCAmelCase   )

      __UpperCamelCase       						= image.to(device=self.device			,  dtype=__UpperCAmelCase   )

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

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

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

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

      if output_type == "pil":
       __UpperCamelCase       						= self.numpy_to_pil(__UpperCAmelCase   )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase   )





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase			:       int									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"bit"
     lowercase		     =		["preactivation", "bottleneck"]
     lowercase		     =		["SAME", "VALID"]





     def __init__(			self			,  __UpperCAmelCase=3			,  __UpperCAmelCase=64			,  __UpperCAmelCase=[256, 512, 1024, 2048]			,  __UpperCAmelCase=[3, 4, 6, 3]			,  __UpperCAmelCase="preactivation"			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=None			,  __UpperCAmelCase=32			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=1			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      if layer_type not in self.layer_types:
       raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
      if global_padding is not None:
       if global_padding.upper() in self.supported_padding:
        __UpperCamelCase       						= global_padding.upper()
       else:
        raise ValueError(F'Padding strategy {global_padding} not supported'   )
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embedding_size
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= layer_type
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= global_padding
      __UpperCamelCase       						= num_groups
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= embedding_dynamic_padding
      __UpperCamelCase       						= output_stride
      __UpperCamelCase       						= width_factor

      __UpperCamelCase       						= ['stem'] + [F'stage{idx}' for idx in range(1			,  len(__UpperCAmelCase   ) + 1   )]
      __UpperCamelCase						,			__UpperCamelCase       						= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase			,  out_indices=__UpperCAmelCase			,  stage_names=self.stage_names   )



