'''simple docstring'''





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

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.exp(a_		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       torch.sum(a_       ,      dim=1		)  # sum of exp(x_i)
     SCREAMING_SNAKE_CASE					:			str    	=       torch.sum(x * exp_x       ,      dim=1		)  # sum of x_i * exp(x_i)
     return torch.log(a_		) - B / A






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Optional[Any]:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			int    	=       config.output_attentions
         SCREAMING_SNAKE_CASE					:			List[str]    	=       config.output_hidden_states
         SCREAMING_SNAKE_CASE					:			List[str]    	=       nn.ModuleList([BertLayer(lowercase__ ) for _ in range(config.num_hidden_layers )] )
         SCREAMING_SNAKE_CASE					:			Dict    	=       nn.ModuleList([BertHighway(lowercase__ ) for _ in range(config.num_hidden_layers )] )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [-1 for _ in range(config.num_hidden_layers )]






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tuple:
         if (type(lowercase__ ) is float) or (type(lowercase__ ) is int):
              for i in range(len(self.early_exit_entropy ) ):
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       x
         else:
              SCREAMING_SNAKE_CASE					:			Dict    	=       x






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pooler.state_dict()
         for highway in self.highway:
              for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name] )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							)							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       ()
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ()
         for i, layer_module in enumerate(self.layer ):
              if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       all_hidden_states + (hidden_states,)

              SCREAMING_SNAKE_CASE					:			int    	=       layer_module(
                  lowercase__    ,							lowercase__    ,							head_mask[i]    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       layer_outputs[0]

              if self.output_attentions:
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       all_attentions + (layer_outputs[1],)

              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (hidden_states,)
              if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE					:			Any    	=       current_outputs + (all_hidden_states,)
              if self.output_attentions:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       current_outputs + (all_attentions,)

              SCREAMING_SNAKE_CASE					:			List[str]    	=       self.highway[i](lowercase__ )
              # logits, pooled_output

              if not self.training:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       highway_exit[0]
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       entropy(lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       all_highway_exits + (highway_exit,)

                   if highway_entropy < self.early_exit_entropy[i]:
                        SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                        raise HighwayException(lowercase__    ,							i + 1 )
              else:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       all_highway_exits + (highway_exit,)

        # Add last layer
         if self.output_hidden_states:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       all_hidden_states + (hidden_states,)

         SCREAMING_SNAKE_CASE					:			List[str]    	=       (hidden_states,)
         if self.output_hidden_states:
              SCREAMING_SNAKE_CASE					:			str    	=       outputs + (all_hidden_states,)
         if self.output_attentions:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       outputs + (all_attentions,)

         SCREAMING_SNAKE_CASE					:			int    	=       outputs + (all_highway_exits,)
         return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "   ,    _SCREAMING_SNAKE_CASE   ,    )
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Optional[int]:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       config

         SCREAMING_SNAKE_CASE					:			str    	=       BertEmbeddings(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       DeeBertEncoder(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       BertPooler(lowercase__ )

         self.init_weights()






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         self.encoder.init_highway_pooler(self.pooler )






    def       _UpperCamelCase    (       self )							-> int:
         return self.embeddings.word_embeddings






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       value






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Dict:
         for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(lowercase__ )






    @add_start_docstrings_to_model_forward(lowercase__ )
    def       _UpperCamelCase    (       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							)							-> Union[str, Any]:
         if input_ids is not None and inputs_embeds is not None:
              raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
         elif input_ids is not None:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       input_ids.size()
         elif inputs_embeds is not None:
              SCREAMING_SNAKE_CASE					:			Any    	=       inputs_embeds.size()[:-1]
         else:
              raise ValueError('You have to specify either input_ids or inputs_embeds' )

         SCREAMING_SNAKE_CASE					:			int    	=       input_ids.device if input_ids is not None else inputs_embeds.device

         if attention_mask is None:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.ones(lowercase__    ,							device=lowercase__ )
         if encoder_attention_mask is None:
              SCREAMING_SNAKE_CASE					:			Any    	=       torch.ones(lowercase__    ,							device=lowercase__ )
         if token_type_ids is None:
              SCREAMING_SNAKE_CASE					:			Any    	=       torch.zeros(lowercase__    ,							dtype=torch.long    ,							device=lowercase__ )

         # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
         # ourselves in which case we just need to make it broadcastable to all heads.
         SCREAMING_SNAKE_CASE					:			torch.Tensor    	=       self.get_extended_attention_mask(lowercase__    ,							lowercase__    ,							lowercase__ )

         # If a 2D ou 3D attention mask is provided for the cross-attention
         # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
         if encoder_attention_mask.dim() == 3:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       encoder_attention_mask[:, None, :, :]
         if encoder_attention_mask.dim() == 2:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       encoder_attention_mask[:, None, None, :]

         SCREAMING_SNAKE_CASE					:			str    	=       encoder_extended_attention_mask.to(
             dtype=next(self.parameters() ).dtype )  # fp16 compatibility
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (1.0 - encoder_extended_attention_mask) * -10_000.0

         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
         # attention_probs has shape bsz x n_heads x N x N
         # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
         # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_head_mask(lowercase__    ,							self.config.num_hidden_layers )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.embeddings(
             input_ids=lowercase__    ,							position_ids=lowercase__    ,							token_type_ids=lowercase__    ,							inputs_embeds=lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.encoder(
             lowercase__    ,							attention_mask=lowercase__    ,							head_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			str    	=       encoder_outputs[0]
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.pooler(lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       (
             sequence_output,
             pooled_output,
         ) + encoder_outputs[
             1:
         ]  # add hidden_states and attentions if they are here
         return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       message
         SCREAMING_SNAKE_CASE					:			int    	=       exit_layer  # start from 1!






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Dict:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BertPooler(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       nn.Dropout(config.hidden_dropout_prob )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       nn.Linear(config.hidden_size    ,							config.num_labels )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[int]:
         # Pooler
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoder_outputs[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.pooler(lowercase__ )
         # "return" pooler_output

         # BertModel
         SCREAMING_SNAKE_CASE					:			int    	=       (pooler_input, pooler_output) + encoder_outputs[1:]
         # "return" bmodel_output

         # Dropout and classification
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       bmodel_output[1]

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dropout(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.classifier(lowercase__ )

         return logits, pooled_output






@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "   ,    _SCREAMING_SNAKE_CASE   ,    )
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Optional[int]:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       config.num_labels
         SCREAMING_SNAKE_CASE					:			Tuple    	=       config.num_hidden_layers

         SCREAMING_SNAKE_CASE					:			Dict    	=       DeeBertModel(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       nn.Dropout(config.hidden_dropout_prob )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       nn.Linear(config.hidden_size    ,							self.config.num_labels )

         self.init_weights()






    @add_start_docstrings_to_model_forward(lowercase__ )
    def       _UpperCamelCase    (       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=-1    ,							lowercase__=False    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.num_layers
         try:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.bert(
                  lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							position_ids=lowercase__    ,							head_mask=lowercase__    ,							inputs_embeds=lowercase__    ,							)
              # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

              SCREAMING_SNAKE_CASE					:			Tuple    	=       outputs[1]

              SCREAMING_SNAKE_CASE					:			str    	=       self.dropout(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       self.classifier(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       (logits,) + outputs[2:]  # add hidden states and attention if they are here
         except HighwayException as e:
              SCREAMING_SNAKE_CASE					:			int    	=       e.message
              SCREAMING_SNAKE_CASE					:			List[str]    	=       e.exit_layer
              SCREAMING_SNAKE_CASE					:			str    	=       outputs[0]

         if not self.training:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       entropy(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       []
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
         if labels is not None:
              if self.num_labels == 1:
                   #  We are doing regression
                   SCREAMING_SNAKE_CASE					:			Dict    	=       MSELoss()
                   SCREAMING_SNAKE_CASE					:			int    	=       loss_fct(logits.view(-1 )    ,							labels.view(-1 ) )
              else:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       CrossEntropyLoss()
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       loss_fct(logits.view(-1    ,							self.num_labels )    ,							labels.view(-1 ) )

              # work with highway exits
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
              for highway_exit in outputs[-1]:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       highway_exit[0]
                   if not self.training:
                        highway_logits_all.append(lowercase__ )
                        highway_entropy.append(highway_exit[2] )
                   if self.num_labels == 1:
                        #  We are doing regression
                        SCREAMING_SNAKE_CASE					:			str    	=       MSELoss()
                        SCREAMING_SNAKE_CASE					:			Any    	=       loss_fct(highway_logits.view(-1 )    ,							labels.view(-1 ) )
                   else:
                        SCREAMING_SNAKE_CASE					:			List[str]    	=       CrossEntropyLoss()
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       loss_fct(highway_logits.view(-1    ,							self.num_labels )    ,							labels.view(-1 ) )
                   highway_losses.append(lowercase__ )

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

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




'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     warnings.warn(
         'The preprocess method is deprecated and will be removed in a future version. Please'
         ' use VaeImageProcessor.preprocess instead'       ,      a_       ,      )
     if isinstance(a_       ,      torch.Tensor		):
          return image
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			str    	=       [image]

     if isinstance(image[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       image[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          SCREAMING_SNAKE_CASE					:			Any    	=       [np.array(i.resize((w, h)       ,      resample=PIL_INTERPOLATION['lanczos']		)		)[None, :] for i in image]
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       np.array(a_		).astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Tuple    	=       image.transpose(0       ,      3       ,      1       ,      2		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       2.0 * image - 1.0
          SCREAMING_SNAKE_CASE					:			int    	=       torch.from_numpy(a_		)
     elif isinstance(image[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cat(a_       ,      dim=0		)
     return image


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     if isinstance(a_       ,      torch.Tensor		):
          return mask
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [mask]

     if isinstance(mask[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          SCREAMING_SNAKE_CASE					:			Tuple    	=       [np.array(m.convert('L'		).resize((w, h)       ,      resample=PIL_INTERPOLATION['nearest']		)		)[None, :] for m in mask]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask.astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1
          SCREAMING_SNAKE_CASE					:			str    	=       torch.from_numpy(a_		)
     elif isinstance(mask[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.cat(a_       ,      dim=0		)
     return mask






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   UNetaDModel
    snake_case__							:   RePaintScheduler






    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__()
         self.register_modules(unet=lowercase__    ,							scheduler=lowercase__ )






    @torch.no_grad()
    def __call__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 250    ,							lowercase__ = 0.0    ,							lowercase__ = 10    ,							lowercase__ = 10    ,							lowercase__ = None    ,							lowercase__ = "pil"    ,							lowercase__ = True    ,							)							-> Union[ImagePipelineOutput, Tuple]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image

         SCREAMING_SNAKE_CASE					:			List[str]    	=       _preprocess_image(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       original_image.to(device=self.device    ,							dtype=self.unet.dtype )
         SCREAMING_SNAKE_CASE					:			Any    	=       _preprocess_mask(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       mask_image.to(device=self.device    ,							dtype=self.unet.dtype )

         SCREAMING_SNAKE_CASE					:			int    	=       original_image.shape[0]

         # sample gaussian noise to begin the loop
         if isinstance(lowercase__    ,							lowercase__ ) and len(lowercase__ ) != batch_size:
              raise ValueError(
                  F"""You have passed a list of generators of length {len(lowercase__ )}, but requested an effective batch"""
                  F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         SCREAMING_SNAKE_CASE					:			Dict    	=       original_image.shape
         SCREAMING_SNAKE_CASE					:			Any    	=       randn_tensor(lowercase__    ,							generator=lowercase__    ,							device=self.device    ,							dtype=self.unet.dtype )

         # set step values
         self.scheduler.set_timesteps(lowercase__    ,							lowercase__    ,							lowercase__    ,							self.device )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       eta

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler.timesteps[0] + 1
         SCREAMING_SNAKE_CASE					:			Tuple    	=       generator[0] if isinstance(lowercase__    ,							lowercase__ ) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
              if t < t_last:
                   # predict the noise residual
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.unet(lowercase__    ,							lowercase__ ).sample
                   # compute previous image: x_t -> x_t-1
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ ).prev_sample

              else:
                   # compute the reverse: x_t-1 -> x_t
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler.undo_step(lowercase__    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       t

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (image / 2 + 0.5).clamp(0    ,							1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image.cpu().permute(0    ,							2    ,							3    ,							1 ).numpy()
         if output_type == "pil":
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.numpy_to_pil(lowercase__ )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=lowercase__ )



