





import logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						logging.getLogger(__name__)







class   __lowerCAmelCase			(					_UpperCamelCase  ):





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





      a__	:						str      		=  self.layer[current_layer](snake_case					,			snake_case					,			head_mask[current_layer]      )

      a__	:						Tuple      		=  layer_outputs[0]

      return hidden_states







@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."""		,_UpperCamelCase		,)
class   __lowerCAmelCase			(					_UpperCamelCase  ):





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





      super().__init__(snake_case      )

      a__	:						Union[str, Any]      		=  BertEncoderWithPabee(snake_case      )

      self.init_weights()
      a__	:						Optional[int]      		=  0
      a__	:						Optional[Any]      		=  0
      a__	:						Optional[int]      		=  0

      a__	:						List[Any]      		=  0





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





      a__	:						Any      		=  threshold





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





      a__	:						Optional[int]      		=  patience





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





      a__	:						str      		=  0
      a__	:						Tuple      		=  0





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





      a__	:						Optional[int]      		=  self.inference_layers_num / self.inference_instances_num
      a__	:						Optional[Any]      		=  (
          F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
          F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
      )
      print(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					,			snake_case=None					,			snake_case=None					,			snake_case=False					,			)				->  str:
      """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__	:						Any      		=  input_ids.size()
      elif inputs_embeds is not None:
         a__	:						List[str]      		=  inputs_embeds.size()[:-1]
      else:
         raise ValueError("You have to specify either input_ids or inputs_embeds"      )

      a__	:						List[Any]      		=  input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
         a__	:						List[str]      		=  torch.ones(snake_case					,			device=snake_case      )
      if token_type_ids is None:
         a__	:						str      		=  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 self.config.is_decoder and encoder_hidden_states is not None:
         a__		,     a__		,     a__	:						Optional[int]      		=  encoder_hidden_states.size()
         a__	:						str      		=  (encoder_batch_size, encoder_sequence_length)
         if encoder_attention_mask is None:
            a__	:						Union[str, Any]      		=  torch.ones(snake_case					,			device=snake_case      )
         a__	:						Dict      		=  self.invert_attention_mask(snake_case      )
      else:
         a__	:						List[Any]      		=  None

      # 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__	:						str      		=  self.get_head_mask(snake_case					,			self.config.num_hidden_layers      )

      a__	:						List[str]      		=  self.embeddings(
          input_ids=snake_case					,			position_ids=snake_case					,			token_type_ids=snake_case					,			inputs_embeds=snake_case      )
      a__	:						str      		=  embedding_output

      if self.training:
         a__	:						Optional[Any]      		=  []
         for i in range(self.config.num_hidden_layers      ):
            a__	:						Any      		=  self.encoder.adaptive_forward(
                snake_case					,			current_layer=snake_case					,			attention_mask=snake_case					,			head_mask=snake_case      )

            a__	:						List[Any]      		=  self.pooler(snake_case      )
            a__	:						Optional[Any]      		=  output_layers[i](output_dropout(snake_case      )      )
            res.append(snake_case      )
      elif self.patience == 0:  # Use all layers for inference
         a__	:						Optional[int]      		=  self.encoder(
             snake_case					,			attention_mask=snake_case					,			head_mask=snake_case					,			encoder_hidden_states=snake_case					,			encoder_attention_mask=snake_case					,			)
         a__	:						Optional[Any]      		=  self.pooler(encoder_outputs[0]      )
         a__	:						List[Any]      		=  [output_layers[self.config.num_hidden_layers - 1](snake_case      )]
      else:
         a__	:						str      		=  0
         a__	:						Dict      		=  None
         a__	:						str      		=  0
         for i in range(self.config.num_hidden_layers      ):
            calculated_layer_num += 1
            a__	:						str      		=  self.encoder.adaptive_forward(
                snake_case					,			current_layer=snake_case					,			attention_mask=snake_case					,			head_mask=snake_case      )

            a__	:						Tuple      		=  self.pooler(snake_case      )
            a__	:						Dict      		=  output_layers[i](snake_case      )
            if regression:
               a__	:						Union[str, Any]      		=  logits.detach()
               if patient_result is not None:
                  a__	:						Optional[int]      		=  patient_result.detach()
               if (patient_result is not None) and torch.abs(patient_result - labels      ) < self.regression_threshold:
                  patient_counter += 1
               else:
                  a__	:						List[str]      		=  0
            else:
               a__	:						Tuple      		=  logits.detach().argmax(dim=1      )
               if patient_result is not None:
                  a__	:						int      		=  patient_result.detach().argmax(dim=1      )
               if (patient_result is not None) and torch.all(labels.eq(snake_case      )      ):
                  patient_counter += 1
               else:
                  a__	:						Tuple      		=  0

            a__	:						Union[str, Any]      		=  logits
            if patient_counter == self.patience:
               break
         a__	:						Optional[int]      		=  [patient_result]
         self.inference_layers_num += calculated_layer_num
         self.inference_instances_num += 1

      return res







@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """		,_UpperCamelCase		,)
class   __lowerCAmelCase			(					_UpperCamelCase  ):





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





      super().__init__(snake_case      )
      a__	:						Union[str, Any]      		=  config.num_labels

      a__	:						str      		=  BertModelWithPabee(snake_case      )
      a__	:						Optional[int]      		=  nn.Dropout(config.hidden_dropout_prob      )
      a__	:						Optional[Any]      		=  nn.ModuleList(
          [nn.Linear(config.hidden_size					,			self.config.num_labels      ) for _ in range(config.num_hidden_layers      )]      )

      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					,			)				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Optional[int]      		=  self.bert(
          input_ids=snake_case					,			attention_mask=snake_case					,			token_type_ids=snake_case					,			position_ids=snake_case					,			head_mask=snake_case					,			inputs_embeds=snake_case					,			output_dropout=self.dropout					,			output_layers=self.classifiers					,			regression=self.num_labels == 1					,			)

      a__	:						List[Any]      		=  (logits[-1],)

      if labels is not None:
         a__	:						Tuple      		=  None
         a__	:						Union[str, Any]      		=  0
         for ix, logits_item in enumerate(snake_case      ):
            if self.num_labels == 1:
               #  We are doing regression
               a__	:						Dict      		=  MSELoss()
               a__	:						Any      		=  loss_fct(logits_item.view(-1      )					,			labels.view(-1      )      )
            else:
               a__	:						Tuple      		=  CrossEntropyLoss()
               a__	:						Optional[int]      		=  loss_fct(logits_item.view(-1					,			self.num_labels      )					,			labels.view(-1      )      )
            if total_loss is None:
               a__	:						Any      		=  loss
            else:
               total_loss += loss * (ix + 1)
            total_weights += ix + 1
         a__	:						Optional[Any]      		=  (total_loss / total_weights,) + outputs

      return outputs








from PIL import Image






def       _A  (      lowerCamelCase							, lowerCamelCase   ):

   def brightness(lowerCamelCase   ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError("level must be between -255.0 (black) and 255.0 (white)"   )
   return img.point(lowerCamelCase   )


if __name__ == "__main__":
  # Load image
  with Image.open("""image_data/lena.jpg""") as img:
    # Change brightness to 100
    SCREAMING_SNAKE_CASE__       :			List[str]			      =						change_brightness(img, 1_0_0)
    brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

