"""simple docstring"""


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,
)


a							=logging.getLogger(__name__)






class        __UpperCAmelCase						(     __lowerCAmelCase     ):

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None				):
										if current_layer == 0:
																lowerCamelCase__ =self.embedding_hidden_mapping_in(_lowerCamelCase				)
										else:
																lowerCamelCase__ =hidden_states[0]

										lowerCamelCase__ =int(self.config.num_hidden_layers / self.config.num_hidden_groups				)

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

										lowerCamelCase__ =self.albert_layer_groups[group_idx](
										    _lowerCamelCase     ,			_lowerCamelCase     ,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]     ,			)
										lowerCamelCase__ =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.'''				,				__lowerCAmelCase				,				)
class        __UpperCAmelCase						(     __lowerCAmelCase     ):

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

										lowerCamelCase__ =AlbertTransformerWithPabee(_lowerCamelCase				)

										self.init_weights()
										lowerCamelCase__ =0
										lowerCamelCase__ =0
										lowerCamelCase__ =0

										lowerCamelCase__ =0

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =threshold

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =patience

				def 		_a     (				self				):
										lowerCamelCase__ =0
										lowerCamelCase__ =0

				def 		_a     (				self				):
										lowerCamelCase__ =self.inference_layers_num / self.inference_instances_num
										lowerCamelCase__ =(
										    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(_lowerCamelCase				)






				@add_start_docstrings_to_model_forward(_lowerCamelCase				)
				def 		_a     (				self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=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:
																lowerCamelCase__ =input_ids.size()
										elif inputs_embeds is not None:
																lowerCamelCase__ =inputs_embeds.size()[:-1]
										else:
																raise ValueError("You have to specify either input_ids or inputs_embeds"				)

										lowerCamelCase__ =input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																lowerCamelCase__ =torch.ones(_lowerCamelCase     ,			device=_lowerCamelCase				)
										if token_type_ids is None:
																lowerCamelCase__ =torch.zeros(_lowerCamelCase     ,			dtype=torch.long     ,			device=_lowerCamelCase				)

										lowerCamelCase__ =attention_mask.unsqueeze(1				).unsqueeze(2				)
										lowerCamelCase__ =extended_attention_mask.to(dtype=self.dtype				)  # fp16 compatibility
										lowerCamelCase__ =(1.0 - extended_attention_mask) * -1_0_0_0_0.0
										lowerCamelCase__ =self.get_head_mask(_lowerCamelCase     ,			self.config.num_hidden_layers				)

										lowerCamelCase__ =self.embeddings(
										    _lowerCamelCase     ,			position_ids=_lowerCamelCase     ,			token_type_ids=_lowerCamelCase     ,			inputs_embeds=_lowerCamelCase				)
										lowerCamelCase__ =embedding_output

										if self.training:
																lowerCamelCase__ =[]
																for i in range(self.config.num_hidden_layers				):
																						lowerCamelCase__ =self.encoder.adaptive_forward(
																						    _lowerCamelCase     ,			current_layer=_lowerCamelCase     ,			attention_mask=_lowerCamelCase     ,			head_mask=_lowerCamelCase     ,			)

																						lowerCamelCase__ =self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]				)				)
																						lowerCamelCase__ =output_layers[i](output_dropout(_lowerCamelCase				)				)
																						res.append(_lowerCamelCase				)
										elif self.patience == 0:  # Use all layers for inference
																lowerCamelCase__ =self.encoder(_lowerCamelCase     ,			_lowerCamelCase     ,			head_mask=_lowerCamelCase				)
																lowerCamelCase__ =self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]				)				)
																lowerCamelCase__ =[output_layers[self.config.num_hidden_layers - 1](_lowerCamelCase				)]
										else:
																lowerCamelCase__ =0
																lowerCamelCase__ =None
																lowerCamelCase__ =0
																for i in range(self.config.num_hidden_layers				):
																						calculated_layer_num += 1
																						lowerCamelCase__ =self.encoder.adaptive_forward(
																						    _lowerCamelCase     ,			current_layer=_lowerCamelCase     ,			attention_mask=_lowerCamelCase     ,			head_mask=_lowerCamelCase     ,			)

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

																						lowerCamelCase__ =logits
																						if patient_counter == self.patience:
																												break
																lowerCamelCase__ =[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. '''				,				__lowerCAmelCase				,				)
class        __UpperCAmelCase						(     __lowerCAmelCase     ):

				def __init__(				self     ,			_lowerCamelCase				):
										super().__init__(_lowerCamelCase				)
										lowerCamelCase__ =config.num_labels

										lowerCamelCase__ =AlbertModelWithPabee(_lowerCamelCase				)
										lowerCamelCase__ =nn.Dropout(config.classifier_dropout_prob				)
										lowerCamelCase__ =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(_lowerCamelCase				)
				def 		_a     (				self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			):
										lowerCamelCase__ =self.albert(
										    input_ids=_lowerCamelCase     ,			attention_mask=_lowerCamelCase     ,			token_type_ids=_lowerCamelCase     ,			position_ids=_lowerCamelCase     ,			head_mask=_lowerCamelCase     ,			inputs_embeds=_lowerCamelCase     ,			output_dropout=self.dropout     ,			output_layers=self.classifiers     ,			regression=self.num_labels == 1     ,			)

										lowerCamelCase__ =(logits[-1],)

										if labels is not None:
																lowerCamelCase__ =None
																lowerCamelCase__ =0
																for ix, logits_item in enumerate(_lowerCamelCase				):
																						if self.num_labels == 1:
																												#  We are doing regression
																												lowerCamelCase__ =MSELoss()
																												lowerCamelCase__ =loss_fct(logits_item.view(-1				)     ,			labels.view(-1				)				)
																						else:
																												lowerCamelCase__ =CrossEntropyLoss()
																												lowerCamelCase__ =loss_fct(logits_item.view(-1     ,			self.num_labels				)     ,			labels.view(-1				)				)
																						if total_loss is None:
																												lowerCamelCase__ =loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																lowerCamelCase__ =(total_loss / total_weights,) + outputs

										return outputs



"""simple docstring"""


class        __UpperCAmelCase						:

				def __init__(				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =data
										lowerCamelCase__ =None
										lowerCamelCase__ =None





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> BinaryTreeNode | None:




						'''simple docstring'''



						if node is None:
												lowerCamelCase__ =BinaryTreeNode(__lowerCAmelCase    )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												lowerCamelCase__ =insert(node.left_child       ,   __lowerCAmelCase    )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowerCamelCase__ =insert(node.right_child       ,   __lowerCAmelCase    )
						return node





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:  # if node is None,return




						'''simple docstring'''



						if node:
												lowerCamelCase__ =inorder(node.left_child    )
												lowerCamelCase__ =[*inorder_array, node.data]
												lowerCamelCase__ =inorder_array + inorder(node.right_child    )
						else:
												lowerCamelCase__ =[]
						return inorder_array





def       lowerCamelCase_			( )							-> BinaryTreeNode | None:




						'''simple docstring'''



						lowerCamelCase__ =insert(__lowerCAmelCase       ,   15    )
						insert(__lowerCAmelCase       ,   10    )
						insert(__lowerCAmelCase       ,   25    )
						insert(__lowerCAmelCase       ,   6    )
						insert(__lowerCAmelCase       ,   14    )
						insert(__lowerCAmelCase       ,   20    )
						insert(__lowerCAmelCase       ,   60    )
						return root





def       lowerCamelCase_			( )							-> None:




						'''simple docstring'''



						lowerCamelCase__ =make_tree()
						print("Printing values of binary search tree in Inorder Traversal."    )
						inorder(__lowerCAmelCase    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


