


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,
)


__magic_name__       					=      logging.getLogger(__name__)





class    lowerCAmelCase__				(			__UpperCAmelCase       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_=None				,							a_=None     ):



							if current_layer == 0:
								lowerCamelCase_			:							List[Any]      =       self.embedding_hidden_mapping_in(lowerCamelCase__     )
							else:
								lowerCamelCase_			:							Any      =       hidden_states[0]

							lowerCamelCase_			:							Optional[int]      =       int(self.config.num_hidden_layers / self.config.num_hidden_groups     )

							# Index of the hidden group
							lowerCamelCase_			:							List[str]      =       int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)     )

							lowerCamelCase_			:							str      =       self.albert_layer_groups[group_idx](
							    lowerCamelCase__				,							lowerCamelCase__				,							head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]				,							)
							lowerCamelCase_			:							Union[str, Any]      =       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.''',    __UpperCAmelCase,    )
class    lowerCAmelCase__				(			__UpperCAmelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_     ):
							super().__init__(lowerCamelCase__     )

							lowerCamelCase_			:							List[str]      =       AlbertTransformerWithPabee(lowerCamelCase__     )

							self.init_weights()
							lowerCamelCase_			:							List[Any]      =       0
							lowerCamelCase_			:							Any      =       0
							lowerCamelCase_			:							Tuple      =       0

							lowerCamelCase_			:							Dict      =       0




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Optional[Any]      =       threshold




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Union[str, Any]      =       patience




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       0
							lowerCamelCase_			:							Any      =       0




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[str]      =       self.inference_layers_num / self.inference_instances_num
							lowerCamelCase_			:							List[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(lowerCamelCase__     )




						@add_start_docstrings_to_model_forward(lowerCamelCase__     )
						def 					_UpperCamelCase			(   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:
								lowerCamelCase_			:							Optional[int]      =       input_ids.size()
							elif inputs_embeds is not None:
								lowerCamelCase_			:							int      =       inputs_embeds.size()[:-1]
							else:
								raise ValueError("You have to specify either input_ids or inputs_embeds"     )

							lowerCamelCase_			:							List[str]      =       input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
								lowerCamelCase_			:							Optional[int]      =       torch.ones(lowerCamelCase__				,							device=lowerCamelCase__     )
							if token_type_ids is None:
								lowerCamelCase_			:							str      =       torch.zeros(lowerCamelCase__				,							dtype=torch.long				,							device=lowerCamelCase__     )

							lowerCamelCase_			:							Union[str, Any]      =       attention_mask.unsqueeze(1     ).unsqueeze(2     )
							lowerCamelCase_			:							str      =       extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
							lowerCamelCase_			:							List[Any]      =       (1.0 - extended_attention_mask) * -1_0000.0
							lowerCamelCase_			:							Tuple      =       self.get_head_mask(lowerCamelCase__				,							self.config.num_hidden_layers     )

							lowerCamelCase_			:							List[str]      =       self.embeddings(
							    lowerCamelCase__				,							position_ids=lowerCamelCase__				,							token_type_ids=lowerCamelCase__				,							inputs_embeds=lowerCamelCase__     )
							lowerCamelCase_			:							Any      =       embedding_output

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

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

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

									lowerCamelCase_			:							Any      =       logits
									if patient_counter == self.patience:
										break
								lowerCamelCase_			:							Optional[Any]      =       [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. ''',    __UpperCAmelCase,    )
class    lowerCAmelCase__				(			__UpperCAmelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_     ):
							super().__init__(lowerCamelCase__     )
							lowerCamelCase_			:							Union[str, Any]      =       config.num_labels

							lowerCamelCase_			:							str      =       AlbertModelWithPabee(lowerCamelCase__     )
							lowerCamelCase_			:							Tuple      =       nn.Dropout(config.classifier_dropout_prob     )
							lowerCamelCase_			:							List[str]      =       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 					_UpperCamelCase			(   self				,							a_=None				,							a_=None				,							a_=None				,							a_=None				,							a_=None				,							a_=None				,							a_=None				,							):
							lowerCamelCase_			:							Tuple      =       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_			:							Optional[Any]      =       (logits[-1],)

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

							return outputs





from __future__ import annotations





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self				,							a_     ):
							lowerCamelCase_			:							List[str]      =       value
							lowerCamelCase_			:							Node | None      =       None
							lowerCamelCase_			:							Node | None      =       None





class    lowerCAmelCase__				:






						"""simple docstring"""
						__UpperCAmelCase   :		int




						def __init__(   self     ):
							lowerCamelCase_			:							str      =       0




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if node is None:
								return

							if path_sum == self.target:
								self.paths += 1

							if node.left:
								self.depth_first_search(node.left				,							path_sum + node.left.value     )
							if node.right:
								self.depth_first_search(node.right				,							path_sum + node.right.value     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None     ):
							if node is None:
								return 0
							if target is not None:
								lowerCamelCase_			:							List[str]      =       target

							self.depth_first_search(a_				,							node.value     )
							self.path_sum(node.left     )
							self.path_sum(node.right     )

							return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()

