

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.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


snake_case_    						=				logging.getLogger(__name__)


class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a=None		,     a=None):
         if current_layer == 0:
              lowercase__					:     Optional[Any]   = self.embedding_hidden_mapping_in(a)
         else:
              lowercase__					:     Union[str, Any]   = hidden_states[0]

         lowercase__					:     Dict   = int(self.config.num_hidden_layers / self.config.num_hidden_groups)

         # Index of the hidden group
         lowercase__					:     Dict   = int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups))

         lowercase__					:     List[Any]   = self.albert_layer_groups[group_idx](
             a		,     a		,     head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]		,     )
         lowercase__					:     str   = layer_group_output[0]

         return (hidden_states,)



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





    def __init__(   self		,     a):
         super().__init__(a)

         lowercase__					:     Dict   = AlbertTransformerWithPabee(a)

         self.init_weights()
         lowercase__					:     str   = 0
         lowercase__					:     Union[str, Any]   = 0
         lowercase__					:     Dict   = 0

         lowercase__					:     str   = 0





    def    snake_case_       (   self		,     a):
         lowercase__					:     List[Any]   = threshold





    def    snake_case_       (   self		,     a):
         lowercase__					:     int   = patience





    def    snake_case_       (   self):
         lowercase__					:     Dict   = 0
         lowercase__					:     List[str]   = 0





    def    snake_case_       (   self):
         lowercase__					:     Union[str, Any]   = self.inference_layers_num / self.inference_instances_num
         lowercase__					:     str   = (
             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(a)





    @add_start_docstrings_to_model_forward(a)
    def    snake_case_       (   self		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=False		,     ):

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

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

         if attention_mask is None:
              lowercase__					:     Union[str, Any]   = torch.ones(a		,     device=a)
         if token_type_ids is None:
              lowercase__					:     Tuple   = torch.zeros(a		,     dtype=torch.long		,     device=a)

         lowercase__					:     Optional[Any]   = attention_mask.unsqueeze(1).unsqueeze(2)
         lowercase__					:     Union[str, Any]   = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
         lowercase__					:     Dict   = (1.0 - extended_attention_mask) * -1_0000.0
         lowercase__					:     Any   = self.get_head_mask(a		,     self.config.num_hidden_layers)

         lowercase__					:     int   = self.embeddings(
             a		,     position_ids=a		,     token_type_ids=a		,     inputs_embeds=a)
         lowercase__					:     Any   = embedding_output

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

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

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

                   lowercase__					:     Any   = logits
                   if patient_counter == self.patience:
                        break
              lowercase__					:     List[str]   = [patient_result]
              self.inference_layers_num += calculated_layer_num
              self.inference_instances_num += 1

         return res



@add_start_docstrings(
    """Albert 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. """	, __snake_case	, )
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def __init__(   self		,     a):
         super().__init__(a)
         lowercase__					:     List[Any]   = config.num_labels

         lowercase__					:     Optional[int]   = AlbertModelWithPabee(a)
         lowercase__					:     List[str]   = nn.Dropout(config.classifier_dropout_prob)
         lowercase__					:     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(a)
    def    snake_case_       (   self		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     ):
         lowercase__					:     Optional[Any]   = self.albert(
             input_ids=a		,     attention_mask=a		,     token_type_ids=a		,     position_ids=a		,     head_mask=a		,     inputs_embeds=a		,     output_dropout=self.dropout		,     output_layers=self.classifiers		,     regression=self.num_labels == 1		,     )

         lowercase__					:     int   = (logits[-1],)

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

         return outputs




from __future__ import annotations


class    SCREAMING_SNAKE_CASE__      :





    def __init__(   self		,     a = 0):
         lowercase__					:     int   = value
         lowercase__					:     Node | None   = None
         lowercase__					:     Node | None   = None



def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None			,  SCREAMING_SNAKE_CASE_	:					Node | None    ):



     '''simple docstring'''





     if treea is None:
          return treea
     if treea is None:
          return treea

     lowercase__					:     Optional[Any]   = treea.value + treea.value
     lowercase__					:     Union[str, Any]   = merge_two_binary_trees(treea.left			,  treea.left    )
     lowercase__					:     Optional[Any]   = merge_two_binary_trees(treea.right			,  treea.right    )
     return treea

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None    ):



     '''simple docstring'''





     if root:
          print(root.value    )
          print_preorder(root.left    )
          print_preorder(root.right    )


if __name__ == "__main__":
       snake_case_    						=				Node(1)
       snake_case_    						=				Node(2)
       snake_case_    						=				Node(3)
       snake_case_    						=				Node(4)

       snake_case_    						=				Node(2)
       snake_case_    						=				Node(4)
       snake_case_    						=				Node(6)
       snake_case_    						=				Node(9)
       snake_case_    						=				Node(5)

       print('''Tree1 is: ''')
       print_preorder(treea)
       print('''Tree2 is: ''')
       print_preorder(treea)
       snake_case_    						=				merge_two_binary_trees(treea, treea)
       print('''Merged Tree is: ''')
       print_preorder(merged_tree)

