





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    _A	(   lowerCamelCase ):
   a__						:    Optional[int]   		=	torch.exp(lowerCamelCase )
   a__						:    Optional[Any]   		=	torch.sum(lowerCamelCase				,			dim=1 )  # sum of exp(x_i)
   a__						:    int   		=	torch.sum(x * exp_x				,			dim=1 )  # sum of x_i * exp(x_i)
   return torch.log(lowerCamelCase ) - B / A







class 	__lowerCAmelCase      (       nn.Module	):





   def __init__(			self       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      super().__init__()
      a__						:    Dict   		=	config.output_attentions
      a__						:    Tuple   		=	config.output_hidden_states
      a__						:    Optional[Any]   		=	nn.ModuleList([BertLayer(snake_case   ) for _ in range(config.num_hidden_layers   )]   )
      a__						:    Optional[int]   		=	nn.ModuleList([BertHighway(snake_case   ) for _ in range(config.num_hidden_layers   )]   )

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





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      if (type(snake_case   ) is float) or (type(snake_case   ) is int):
         for i in range(len(self.early_exit_entropy   )   ):
            a__						:    Optional[Any]   		=	x
      else:
         a__						:    Optional[int]   		=	x





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Any   		=	pooler.state_dict()
      for highway in self.highway:
         for name, param in highway.pooler.state_dict().items():
            param.copy_(loaded_model[name]   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      )					->	List[str]:
      """simple docstring"""





      a__						:    Optional[int]   		=	()
      a__						:    List[str]   		=	()
      a__						:    Any   		=	()
      for i, layer_module in enumerate(self.layer   ):
         if self.output_hidden_states:
            a__						:    List[str]   		=	all_hidden_states + (hidden_states,)

         a__						:    Dict   		=	layer_module(
             snake_case       ,      snake_case       ,      head_mask[i]       ,      snake_case       ,      snake_case   )
         a__						:    Optional[Any]   		=	layer_outputs[0]

         if self.output_attentions:
            a__						:    Optional[Any]   		=	all_attentions + (layer_outputs[1],)

         a__						:    Optional[int]   		=	(hidden_states,)
         if self.output_hidden_states:
            a__						:    Optional[Any]   		=	current_outputs + (all_hidden_states,)
         if self.output_attentions:
            a__						:    Union[str, Any]   		=	current_outputs + (all_attentions,)

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

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

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

        # Add last layer
      if self.output_hidden_states:
         a__						:    Optional[int]   		=	all_hidden_states + (hidden_states,)

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

      a__						:    Tuple   		=	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). """		,_UpperCamelCase		,)
class 	__lowerCAmelCase      (       _UpperCamelCase	):





   def __init__(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      super().__init__(snake_case   )
      a__						:    List[Any]   		=	config

      a__						:    str   		=	BertEmbeddings(snake_case   )
      a__						:    List[str]   		=	DeeBertEncoder(snake_case   )
      a__						:    int   		=	BertPooler(snake_case   )

      self.init_weights()





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      self.encoder.init_highway_pooler(self.pooler   )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      return self.embeddings.word_embeddings





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	value





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      for layer, heads in heads_to_prune.items():
         self.encoder.layer[layer].attention.prune_heads(snake_case   )





   @add_start_docstrings_to_model_forward(snake_case   )
   def 						_snake_case			(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      )					->	List[Any]:
      """simple docstring"""





      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:
         a__						:    Dict   		=	input_ids.size()
      elif inputs_embeds is not None:
         a__						:    Optional[Any]   		=	inputs_embeds.size()[:-1]
      else:
         raise ValueError("You have to specify either input_ids or inputs_embeds"   )

      a__						:    Tuple   		=	input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
         a__						:    Tuple   		=	torch.ones(snake_case       ,      device=snake_case   )
      if encoder_attention_mask is None:
         a__						:    Optional[int]   		=	torch.ones(snake_case       ,      device=snake_case   )
      if token_type_ids is None:
         a__						:    Tuple   		=	torch.zeros(snake_case       ,      dtype=torch.long       ,      device=snake_case   )

      # 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.
      a__						:    torch.Tensor   		=	self.get_extended_attention_mask(snake_case       ,      snake_case       ,      snake_case   )

      # 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:
         a__						:    List[Any]   		=	encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.dim() == 2:
         a__						:    List[str]   		=	encoder_attention_mask[:, None, None, :]

      a__						:    Optional[Any]   		=	encoder_extended_attention_mask.to(
          dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
      a__						:    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]
      a__						:    List[str]   		=	self.get_head_mask(snake_case       ,      self.config.num_hidden_layers   )

      a__						:    Optional[Any]   		=	self.embeddings(
          input_ids=snake_case       ,      position_ids=snake_case       ,      token_type_ids=snake_case       ,      inputs_embeds=snake_case   )
      a__						:    str   		=	self.encoder(
          snake_case       ,      attention_mask=snake_case       ,      head_mask=snake_case       ,      encoder_hidden_states=snake_case       ,      encoder_attention_mask=snake_case       ,      )
      a__						:    str   		=	encoder_outputs[0]
      a__						:    Any   		=	self.pooler(snake_case   )

      a__						:    Dict   		=	(
          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 	__lowerCAmelCase      (       _UpperCamelCase	):





   def __init__(			self       ,      snake_case       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





      a__						:    List[str]   		=	message
      a__						:    List[Any]   		=	exit_layer  # start from 1!







class 	__lowerCAmelCase      (       nn.Module	):





   def __init__(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      super().__init__()
      a__						:    str   		=	BertPooler(snake_case   )
      a__						:    Tuple   		=	nn.Dropout(config.hidden_dropout_prob   )
      a__						:    Tuple   		=	nn.Linear(config.hidden_size       ,      config.num_labels   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      a__						:    List[str]   		=	encoder_outputs[0]
      a__						:    Optional[int]   		=	self.pooler(snake_case   )
      # "return" pooler_output

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

      # Dropout and classification
      a__						:    Any   		=	bmodel_output[1]

      a__						:    List[str]   		=	self.dropout(snake_case   )
      a__						:    int   		=	self.classifier(snake_case   )

      return logits, pooled_output







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





   def __init__(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      super().__init__(snake_case   )
      a__						:    Optional[Any]   		=	config.num_labels
      a__						:    Any   		=	config.num_hidden_layers

      a__						:    Any   		=	DeeBertModel(snake_case   )
      a__						:    List[str]   		=	nn.Dropout(config.hidden_dropout_prob   )
      a__						:    Any   		=	nn.Linear(config.hidden_size       ,      self.config.num_labels   )

      self.init_weights()





   @add_start_docstrings_to_model_forward(snake_case   )
   def 						_snake_case			(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=-1       ,      snake_case=False       ,      )					->	int:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	self.num_layers
      try:
         a__						:    Dict   		=	self.bert(
             snake_case       ,      attention_mask=snake_case       ,      token_type_ids=snake_case       ,      position_ids=snake_case       ,      head_mask=snake_case       ,      inputs_embeds=snake_case       ,      )
         # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

         a__						:    Any   		=	outputs[1]

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

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

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

         if train_highway:
            a__						:    Union[str, Any]   		=	(sum(highway_losses[:-1]   ),) + outputs
            # exclude the final highway, of course
         else:
            a__						:    Any   		=	(loss,) + outputs
      if not self.training:
         a__						:    Optional[Any]   		=	outputs + ((original_entropy, highway_entropy), exit_layer)
         if output_layer >= 0:
            a__						:    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)








import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      List[str]         =    argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()


SCREAMING_SNAKE_CASE__							:      int         =    """cpu"""
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

SCREAMING_SNAKE_CASE__							:      List[Any]         =    """path-to-your-trained-model"""
SCREAMING_SNAKE_CASE__							:      Tuple         =    StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    pipe.to(device)

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE__							:      Any         =    torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE__							:      List[Any]         =    torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    (sample, timestep, encoder_hidden_status)
try:
  SCREAMING_SNAKE_CASE__							:      Any         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      str         =    ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    6_6_6
SCREAMING_SNAKE_CASE__							:      Any         =    torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE__							:      int         =    {"""generator""": generator}
if args.steps is not None:
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
  SCREAMING_SNAKE_CASE__							:      Dict         =    pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")

