





'''simple docstring'''






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__lowerCamelCase						: Tuple		=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if isinstance(__SCREAMING_SNAKE_CASE  ,      torch.Tensor   ):
    return image
  elif isinstance(__SCREAMING_SNAKE_CASE  ,      PIL.Image.Image   ):
    _UpperCamelCase       			=[image]

  _UpperCamelCase       			=[trans(img.convert('''RGB'''   )   ) for img in image]
  _UpperCamelCase       			=torch.stack(__SCREAMING_SNAKE_CASE   )
  return image







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def __init__(				self    :					Any							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					Any  )     ->						List[str]:
    super().__init__()

    # make sure scheduler can always be converted to DDIM
    _UpperCamelCase       			=DDIMScheduler.from_config(scheduler.config  )

    self.register_modules(unet=UpperCamelCase__							,	scheduler=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					Optional[int]  )     ->						int:
    if strength < 0 or strength > 1:
      raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''  )


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					List[str]  )     ->						Any:
    # get the original timestep using init_timestep
    _UpperCamelCase       			=min(int(num_inference_steps * strength  )							,	UpperCamelCase__  )

    _UpperCamelCase       			=max(num_inference_steps - init_timestep							,	0  )
    _UpperCamelCase       			=self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start


  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Optional[int]=None  )     ->						Optional[int]:
    if not isinstance(UpperCamelCase__							,	(torch.Tensor, PIL.Image.Image, list)  ):
      raise ValueError(
          F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCamelCase__  )}'''  )

    _UpperCamelCase       			=image.to(device=UpperCamelCase__							,	dtype=UpperCamelCase__  )

    if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) and len(UpperCamelCase__  ) != batch_size:
      raise ValueError(
          F'''You have passed a list of generators of length {len(UpperCamelCase__  )}, but requested an effective batch'''
          F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

    _UpperCamelCase       			=init_latents.shape
    _UpperCamelCase       			=randn_tensor(UpperCamelCase__							,	generator=UpperCamelCase__							,	device=UpperCamelCase__							,	dtype=UpperCamelCase__  )

    # get latents
    print('''add noise to latents at timestep'''							,	UpperCamelCase__  )
    _UpperCamelCase       			=self.scheduler.add_noise(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=init_latents

    return latents




  @torch.no_grad()
  def __call__(				self    :					Any							,	UpperCamelCase__    :					Union[torch.FloatTensor, PIL.Image.Image] = None							,	UpperCamelCase__    :					float = 0.8							,	UpperCamelCase__    :					int = 1							,	UpperCamelCase__    :					Optional[Union[torch.Generator, List[torch.Generator]]] = None							,	UpperCamelCase__    :					float = 0.0							,	UpperCamelCase__    :					int = 50							,	UpperCamelCase__    :					Optional[bool] = None							,	UpperCamelCase__    :					Optional[str] = "pil"							,	UpperCamelCase__    :					bool = True							,	)     ->						Union[ImagePipelineOutput, Tuple]:
    self.check_inputs(UpperCamelCase__  )

    # 2. Preprocess image
    _UpperCamelCase       			=preprocess(UpperCamelCase__  )

    # 3. set timesteps
    self.scheduler.set_timesteps(UpperCamelCase__							,	device=self.device  )
    _UpperCamelCase			,     _UpperCamelCase       			=self.get_timesteps(UpperCamelCase__							,	UpperCamelCase__							,	self.device  )
    _UpperCamelCase       			=timesteps[:1].repeat(UpperCamelCase__  )

    # 4. Prepare latent variables
    _UpperCamelCase       			=self.prepare_latents(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	self.unet.dtype							,	self.device							,	UpperCamelCase__  )
    _UpperCamelCase       			=latents

    # 5. Denoising loop
    for t in self.progress_bar(UpperCamelCase__  ):
      # 1. predict noise model_output
      _UpperCamelCase       			=self.unet(UpperCamelCase__							,	UpperCamelCase__  ).sample

      # 2. predict previous mean of image x_t-1 and add variance depending on eta
      # eta corresponds to η in paper and should be between [0, 1]
      # do x_t -> x_t-1
      _UpperCamelCase       			=self.scheduler.step(
          UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	eta=UpperCamelCase__							,	use_clipped_model_output=UpperCamelCase__							,	generator=UpperCamelCase__							,	).prev_sample

    _UpperCamelCase       			=(image / 2 + 0.5).clamp(0							,	1  )
    _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, latent_timestep.item())

    return ImagePipelineOutput(images=UpperCamelCase__  )













'''simple docstring'''






from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy







@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """   ,     lowercase_   ,     )
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Dict							,	UpperCamelCase__    :					List[str]  )     ->						List[Any]:
    super().__init__(UpperCamelCase__  )

    _UpperCamelCase       			=RobertaEmbeddings(UpperCamelCase__  )
    self.init_weights()







@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """   ,     lowercase_   ,     )
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Any  )     ->						Dict:
    super().__init__(UpperCamelCase__  )
    _UpperCamelCase       			=config.num_labels
    _UpperCamelCase       			=config.num_hidden_layers

    _UpperCamelCase       			=DeeRobertaModel(UpperCamelCase__  )
    _UpperCamelCase       			=nn.Dropout(config.hidden_dropout_prob  )
    _UpperCamelCase       			=nn.Linear(config.hidden_size							,	self.config.num_labels  )




  @add_start_docstrings_to_model_forward(UpperCamelCase__  )
  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Optional[Any]=None							,	UpperCamelCase__    :					int=None							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					Union[str, Any]=-1							,	UpperCamelCase__    :					Dict=False							,	)     ->						Union[str, Any]:
    _UpperCamelCase       			=self.num_layers
    try:
      _UpperCamelCase       			=self.roberta(
          UpperCamelCase__							,	attention_mask=UpperCamelCase__							,	token_type_ids=UpperCamelCase__							,	position_ids=UpperCamelCase__							,	head_mask=UpperCamelCase__							,	inputs_embeds=UpperCamelCase__							,	)

      _UpperCamelCase       			=outputs[1]

      _UpperCamelCase       			=self.dropout(UpperCamelCase__  )
      _UpperCamelCase       			=self.classifier(UpperCamelCase__  )
      _UpperCamelCase       			=(logits,) + outputs[2:]  # add hidden states and attention if they are here
    except HighwayException as e:
      _UpperCamelCase       			=e.message
      _UpperCamelCase       			=e.exit_layer
      _UpperCamelCase       			=outputs[0]

    if not self.training:
      _UpperCamelCase       			=entropy(UpperCamelCase__  )
      _UpperCamelCase       			=[]
      _UpperCamelCase       			=[]
    if labels is not None:
      if self.num_labels == 1:
        #  We are doing regression
        _UpperCamelCase       			=MSELoss()
        _UpperCamelCase       			=loss_fct(logits.view(-1  )							,	labels.view(-1  )  )
      else:
        _UpperCamelCase       			=CrossEntropyLoss()
        _UpperCamelCase       			=loss_fct(logits.view(-1							,	self.num_labels  )							,	labels.view(-1  )  )

      # work with highway exits
      _UpperCamelCase       			=[]
      for highway_exit in outputs[-1]:
        _UpperCamelCase       			=highway_exit[0]
        if not self.training:
          highway_logits_all.append(UpperCamelCase__  )
          highway_entropy.append(highway_exit[2]  )
        if self.num_labels == 1:
          #  We are doing regression
          _UpperCamelCase       			=MSELoss()
          _UpperCamelCase       			=loss_fct(highway_logits.view(-1  )							,	labels.view(-1  )  )
        else:
          _UpperCamelCase       			=CrossEntropyLoss()
          _UpperCamelCase       			=loss_fct(highway_logits.view(-1							,	self.num_labels  )							,	labels.view(-1  )  )
        highway_losses.append(UpperCamelCase__  )

      if train_highway:
        _UpperCamelCase       			=(sum(highway_losses[:-1]  ),) + outputs
        # exclude the final highway, of course
      else:
        _UpperCamelCase       			=(loss,) + outputs
    if not self.training:
      _UpperCamelCase       			=outputs + ((original_entropy, highway_entropy), exit_layer)
      if output_layer >= 0:
        _UpperCamelCase       			=(
            (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
        )  # use the highway of the last layer

    return outputs  # (loss), logits, (hidden_states), (attentions), entropy






