

"""simple docstring"""






import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Union[str, Any]  =    int(_lowerCamelCase   )
    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    t // 3600, (t // 60) % 60, t % 60
    return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=300   ):

    '''simple docstring'''



    return f"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    '<table border="1" class="dataframe">\n'
    html_code += """  <thead>\n <tr style="text-align: left;">\n"""
    for i in items[0]:
        html_code += f"""      <th>{i}</th>\n"""
    html_code += "    </tr>\n  </thead>\n  <tbody>\n"
    for line in items[1:]:
        html_code += "    <tr>\n"
        for elt in line:
            _lowerCAmelCase       :   str  =    f"""{elt:.6f}""" if isinstance(_lowerCamelCase ,   _lowerCamelCase   ) else str(_lowerCamelCase   )
            html_code += f"""      <td>{elt}</td>\n"""
        html_code += "    </tr>\n"
    html_code += "  </tbody>\n</table><p>"
    return html_code







class 	__UpperCamelCase    :
  _UpperCAmelCase          =		5
  _UpperCAmelCase          =		0.2

  def __init__(		self    ,_A    ,_A = None    ,_A = True    ,_A = None    ,_A = 300    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    total
      _lowerCAmelCase       :   str  =    '' if prefix is None else prefix
      _lowerCAmelCase       :   Optional[int]  =    leave
      _lowerCAmelCase       :   int  =    parent
      _lowerCAmelCase       :   List[Any]  =    width
      _lowerCAmelCase       :   List[Any]  =    None
      _lowerCAmelCase       :   Dict  =    None
      _lowerCAmelCase       :   List[Any]  =    None

  def __lowerCamelCase		(		self    ,_A    ,_A = False    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    value
      if comment is not None:
          _lowerCAmelCase       :   Optional[Any]  =    comment
      if self.last_value is None:
          _lowerCAmelCase       :   Optional[Any]  =    time.time()
          _lowerCAmelCase       :   Dict  =    value
          _lowerCAmelCase       :   Tuple  =    None
          _lowerCAmelCase       :   Tuple  =    self.warmup
          _lowerCAmelCase       :   Union[str, Any]  =    1
          self.update_bar(_A      )
      elif value <= self.last_value and not force_update:
          return
      elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for    ,self.total      ):
          if self.first_calls > 0:
              self.first_calls -= 1
          _lowerCAmelCase       :   str  =    time.time()
          _lowerCAmelCase       :   int  =    current_time - self.start_time
          # We could have value = self.start_value if the update is called twixe with the same start value.
          if value > self.start_value:
              _lowerCAmelCase       :   int  =    self.elapsed_time / (value - self.start_value)
          else:
              _lowerCAmelCase       :   List[str]  =    None
          if value >= self.total:
              _lowerCAmelCase       :   Tuple  =    self.total
              _lowerCAmelCase       :   Optional[int]  =    None
              if not self.leave:
                  self.close()
          elif self.average_time_per_item is not None:
              _lowerCAmelCase       :   int  =    self.average_time_per_item * (self.total - value)
          self.update_bar(_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    value
          _lowerCAmelCase       :   Optional[Any]  =    current_time
          if self.average_time_per_item is None:
              _lowerCAmelCase       :   Any  =    1
          else:
              _lowerCAmelCase       :   Optional[int]  =    max(int(self.update_every / self.average_time_per_item      )    ,1      )

  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    ' ' * (len(str(self.total      )      ) - len(str(_A      )      )) + str(_A      )
      if self.elapsed_time is None:
          _lowerCAmelCase       :   Any  =    F"""[{spaced_value}/{self.total} : < :"""
      elif self.predicted_remaining is None:
          _lowerCAmelCase       :   List[Any]  =    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time      )}"""
      else:
          _lowerCAmelCase       :   List[Any]  =    (
              F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time      )} <"""
              F""" {format_time(self.predicted_remaining      )}"""
          )
          self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
      self.label += "]" if self.comment is None or len(self.comment      ) == 0 else F""", {self.comment}]"""
      self.display()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    html_progress_bar(self.value    ,self.total    ,self.prefix    ,self.label    ,self.width      )
      if self.parent is not None:
          # If this is a child bar, the parent will take care of the display.
          self.parent.display()
          return
      if self.output is None:
          _lowerCAmelCase       :   Optional[int]  =    disp.display(disp.HTML(self.html_code      )    ,display_id=_A      )
      else:
          self.output.update(disp.HTML(self.html_code      )      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      if self.parent is None and self.output is not None:
          self.output.update(disp.HTML(''      )      )







class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      super().__init__(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    None if column_names is None else [column_names]
      _lowerCAmelCase       :   Optional[Any]  =    None

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    html_progress_bar(self.value    ,self.total    ,self.prefix    ,self.label    ,self.width      )
      if self.inner_table is not None:
          self.html_code += text_to_html_table(self.inner_table      )
      if self.child_bar is not None:
          self.html_code += self.child_bar.html_code
      if self.output is None:
          _lowerCAmelCase       :   Union[str, Any]  =    disp.display(disp.HTML(self.html_code      )    ,display_id=_A      )
      else:
          self.output.update(disp.HTML(self.html_code      )      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if self.inner_table is None:
          _lowerCAmelCase       :   Dict  =    [list(values.keys()      ), list(values.values()      )]
      else:
          _lowerCAmelCase       :   Union[str, Any]  =    self.inner_table[0]
          if len(self.inner_table      ) == 1:
              # We give a chance to update the column names at the first iteration
              for key in values.keys():
                  if key not in columns:
                      columns.append(_A      )
              _lowerCAmelCase       :   List[str]  =    columns
          self.inner_table.append([values[c] for c in columns]      )

  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=300      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    NotebookProgressBar(_A    ,prefix=_A    ,parent=self    ,width=_A      )
      return self.child_bar







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    None
      self.display()







class 	__UpperCamelCase    (    a__						):

  def __init__(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    None
      _lowerCAmelCase       :   Union[str, Any]  =    None
      _lowerCAmelCase       :   List[Any]  =    False

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
      _lowerCAmelCase       :   Any  =    0
      _lowerCAmelCase       :   Tuple  =    0
      _lowerCAmelCase       :   int  =    [self.first_column] + ['Training Loss']
      if args.evaluation_strategy != IntervalStrategy.NO:
          column_names.append('Validation Loss'      )
      _lowerCAmelCase       :   List[str]  =    NotebookTrainingTracker(state.max_steps    ,_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    int(state.epoch      ) if int(state.epoch      ) == state.epoch else F"""{state.epoch:.2f}"""
      self.training_tracker.update(
          state.global_step + 1    ,comment=F"""Epoch {epoch}/{state.num_train_epochs}"""    ,force_update=self._force_next_update    ,)
      _lowerCAmelCase       :   int  =    False

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if not has_length(_A      ):
          return
      if self.prediction_bar is None:
          if self.training_tracker is not None:
              _lowerCAmelCase       :   str  =    self.training_tracker.add_child(len(_A      )      )
          else:
              _lowerCAmelCase       :   str  =    NotebookProgressBar(len(_A      )      )
          self.prediction_bar.update(1      )
      else:
          self.prediction_bar.update(self.prediction_bar.value + 1      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,**_A      ):
      '''simple docstring'''

      if self.prediction_bar is not None:
          self.prediction_bar.close()
      _lowerCAmelCase       :   Tuple  =    None

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
          _lowerCAmelCase       :   str  =    {'Training Loss': logs['loss']}
          # First column is necessarily Step sine we're not in epoch eval strategy
          _lowerCAmelCase       :   Dict  =    state.global_step
          self.training_tracker.write_line(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if self.training_tracker is not None:
          _lowerCAmelCase       :   Any  =    {'Training Loss': 'No log', 'Validation Loss': 'No log'}
          for log in reversed(state.log_history      ):
              if "loss" in log:
                  _lowerCAmelCase       :   str  =    log['loss']
                  break

          if self.first_column == "Epoch":
              _lowerCAmelCase       :   Dict  =    int(state.epoch      )
          else:
              _lowerCAmelCase       :   Optional[Any]  =    state.global_step
          _lowerCAmelCase       :   Optional[Any]  =    'eval'
          for k in metrics:
              if k.endswith('_loss'      ):
                  _lowerCAmelCase       :   List[Any]  =    re.sub(r'\_loss$'    ,''    ,_A      )
          _lowerCAmelCase       :   Dict  =    metrics.pop('total_flos'    ,_A      )
          _lowerCAmelCase       :   int  =    metrics.pop('epoch'    ,_A      )
          _lowerCAmelCase       :   str  =    metrics.pop(F"""{metric_key_prefix}_runtime"""    ,_A      )
          _lowerCAmelCase       :   Dict  =    metrics.pop(F"""{metric_key_prefix}_samples_per_second"""    ,_A      )
          _lowerCAmelCase       :   Tuple  =    metrics.pop(F"""{metric_key_prefix}_steps_per_second"""    ,_A      )
          _lowerCAmelCase       :   int  =    metrics.pop(F"""{metric_key_prefix}_jit_compilation_time"""    ,_A      )
          for k, v in metrics.items():
              if k == F"""{metric_key_prefix}_loss""":
                  _lowerCAmelCase       :   Optional[int]  =    v
              else:
                  _lowerCAmelCase       :   Tuple  =    k.split('_'      )
                  _lowerCAmelCase       :   Optional[int]  =    ' '.join([part.capitalize() for part in splits[1:]]      )
                  _lowerCAmelCase       :   str  =    v
          self.training_tracker.write_line(_A      )
          self.training_tracker.remove_child()
          _lowerCAmelCase       :   Tuple  =    None
          # Evaluation takes a long time so we should force the next update.
          _lowerCAmelCase       :   Union[str, Any]  =    True







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,**_A      ):
      '''simple docstring'''

      self.training_tracker.update(
          state.global_step    ,comment=F"""Epoch {int(state.epoch      )}/{state.num_train_epochs}"""    ,force_update=_A      )
      _lowerCAmelCase       :   List[Any]  =    None








"""simple docstring"""






from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		42







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 32    ,_A = 64    ,_A = 20    ,_A = 768    ,_A=77    ,_A=4    ,_A = 0.0    ,_A = "silu"    ,_A = None    ,_A = None    ,_A = "linear"    ,_A = "prd"    ,_A = None    ,_A = None    ,_A = None    ,):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Optional[int]  =    attention_head_dim
      _lowerCAmelCase       :   Tuple  =    num_attention_heads * attention_head_dim
      _lowerCAmelCase       :   Optional[Any]  =    additional_embeddings

      _lowerCAmelCase       :   Union[str, Any]  =    time_embed_dim or inner_dim
      _lowerCAmelCase       :   Union[str, Any]  =    embedding_proj_dim or embedding_dim
      _lowerCAmelCase       :   Optional[int]  =    clip_embed_dim or embedding_dim

      _lowerCAmelCase       :   int  =    Timesteps(_A    ,_A    ,0      )
      _lowerCAmelCase       :   int  =    TimestepEmbedding(_A    ,_A    ,out_dim=_A    ,act_fn=_A      )

      _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )

      if embedding_proj_norm_type is None:
          _lowerCAmelCase       :   Optional[Any]  =    None
      elif embedding_proj_norm_type == "layer":
          _lowerCAmelCase       :   List[Any]  =    nn.LayerNorm(_A      )
      else:
          raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""      )

      _lowerCAmelCase       :   Tuple  =    nn.Linear(_A    ,_A      )

      if encoder_hid_proj_type is None:
          _lowerCAmelCase       :   int  =    None
      elif encoder_hid_proj_type == "linear":
          _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )
      else:
          raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""      )

      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,num_embeddings + additional_embeddings    ,_A      )      )

      if added_emb_type == "prd":
          _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,1    ,_A      )      )
      elif added_emb_type is None:
          _lowerCAmelCase       :   List[Any]  =    None
      else:
          raise ValueError(
              F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""      )

      _lowerCAmelCase       :   List[Any]  =    nn.ModuleList(
          [
              BasicTransformerBlock(
                  _A    ,_A    ,_A    ,dropout=_A    ,activation_fn='gelu'    ,attention_bias=_A    ,)
              for d in range(_A      )
          ]      )

      if norm_in_type == "layer":
          _lowerCAmelCase       :   Any  =    nn.LayerNorm(_A      )
      elif norm_in_type is None:
          _lowerCAmelCase       :   Any  =    None
      else:
          raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""      )

      _lowerCAmelCase       :   Union[str, Any]  =    nn.LayerNorm(_A      )

      _lowerCAmelCase       :   int  =    nn.Linear(_A    ,_A      )

      _lowerCAmelCase       :   Any  =    torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,-1_0_0_0_0.0      )
      causal_attention_mask.triu_(1      )
      _lowerCAmelCase       :   Tuple  =    causal_attention_mask[None, ...]
      self.register_buffer('causal_attention_mask'    ,_A    ,persistent=_A      )

      _lowerCAmelCase       :   Tuple  =    nn.Parameter(torch.zeros(1    ,_A      )      )
      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,_A      )      )

  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    {}

      def fn_recursive_add_processors(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              _lowerCAmelCase       :   str  =    module.processor

          for sub_name, child in module.named_children():
              fn_recursive_add_processors(F"""{name}.{sub_name}"""    ,_A    ,_A      )

          return processors

      for name, module in self.named_children():
          fn_recursive_add_processors(_A    ,_A    ,_A      )

      return processors

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    len(self.attn_processors.keys()      )

      if isinstance(_A    ,_A      ) and len(_A      ) != count:
          raise ValueError(
              F"""A dict of processors was passed, but the number of processors {len(_A      )} does not match the"""
              F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""      )

      def fn_recursive_attn_processor(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              if not isinstance(_A    ,_A      ):
                  module.set_processor(_A      )
              else:
                  module.set_processor(processor.pop(F"""{name}.processor"""      )      )

          for sub_name, child in module.named_children():
              fn_recursive_attn_processor(F"""{name}.{sub_name}"""    ,_A    ,_A      )

      for name, module in self.named_children():
          fn_recursive_attn_processor(_A    ,_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.set_attn_processor(AttnProcessor()      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A = None    ,_A = None    ,_A = True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    hidden_states.shape[0]

      _lowerCAmelCase       :   int  =    timestep
      if not torch.is_tensor(_A      ):
          _lowerCAmelCase       :   str  =    torch.tensor([timesteps]    ,dtype=torch.long    ,device=hidden_states.device      )
      elif torch.is_tensor(_A      ) and len(timesteps.shape      ) == 0:
          _lowerCAmelCase       :   Dict  =    timesteps[None].to(hidden_states.device      )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _lowerCAmelCase       :   Optional[int]  =    timesteps * torch.ones(_A    ,dtype=timesteps.dtype    ,device=timesteps.device      )

      _lowerCAmelCase       :   Dict  =    self.time_proj(_A      )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might be fp16, so we need to cast here.
      _lowerCAmelCase       :   Any  =    timesteps_projected.to(dtype=self.dtype      )
      _lowerCAmelCase       :   Optional[Any]  =    self.time_embedding(_A      )

      if self.embedding_proj_norm is not None:
          _lowerCAmelCase       :   int  =    self.embedding_proj_norm(_A      )

      _lowerCAmelCase       :   str  =    self.embedding_proj(_A      )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          _lowerCAmelCase       :   str  =    self.encoder_hidden_states_proj(_A      )
      elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'      )

      _lowerCAmelCase       :   Any  =    self.proj_in(_A      )

      _lowerCAmelCase       :   Dict  =    self.positional_embedding.to(hidden_states.dtype      )

      _lowerCAmelCase       :   List[Any]  =    []
      _lowerCAmelCase       :   Optional[Any]  =    0

      if encoder_hidden_states is not None:
          additional_embeds.append(_A      )
          additional_embeddings_len += encoder_hidden_states.shape[1]

      if len(proj_embeddings.shape      ) == 2:
          _lowerCAmelCase       :   int  =    proj_embeddings[:, None, :]

      if len(hidden_states.shape      ) == 2:
          _lowerCAmelCase       :   Any  =    hidden_states[:, None, :]

      _lowerCAmelCase       :   int  =    additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   List[str]  =    self.prd_embedding.to(hidden_states.dtype      ).expand(_A    ,-1    ,-1      )
          additional_embeds.append(_A      )

      _lowerCAmelCase       :   List[str]  =    torch.cat(
          _A    ,dim=1    ,)

      # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
      _lowerCAmelCase       :   Tuple  =    additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
          _lowerCAmelCase       :   Any  =    F.pad(
              _A    ,(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )    ,value=0.0    ,)

      _lowerCAmelCase       :   int  =    hidden_states + positional_embeddings

      if attention_mask is not None:
          _lowerCAmelCase       :   Optional[Any]  =    (1 - attention_mask.to(hidden_states.dtype      )) * -1_0_0_0_0.0
          _lowerCAmelCase       :   Union[str, Any]  =    F.pad(_A    ,(0, self.additional_embeddings)    ,value=0.0      )
          _lowerCAmelCase       :   Tuple  =    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
          _lowerCAmelCase       :   Optional[Any]  =    attention_mask.repeat_interleave(self.config.num_attention_heads    ,dim=0      )

      if self.norm_in is not None:
          _lowerCAmelCase       :   Any  =    self.norm_in(_A      )

      for block in self.transformer_blocks:
          _lowerCAmelCase       :   int  =    block(_A    ,attention_mask=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.norm_out(_A      )

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states[:, -1]
      else:
          _lowerCAmelCase       :   Any  =    hidden_states[:, additional_embeddings_len:]

      _lowerCAmelCase       :   Optional[int]  =    self.proj_to_clip_embeddings(_A      )

      if not return_dict:
          return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    (prior_latents * self.clip_std) + self.clip_mean
      return prior_latents





