

"""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__ :	Tuple						= logging.getLogger(__name__)


class 			_snake_case     (						UpperCamelCase_       ):







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None							)			->						Tuple:


											if current_layer == 0:
																__lowercase												=			self.embedding_hidden_mapping_in(__UpperCamelCase							)
											else:
																__lowercase												=			hidden_states[0]

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

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

											__lowercase												=			self.albert_layer_groups[group_idx](
											    __UpperCamelCase     ,							__UpperCamelCase     ,							head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]     ,							)
											__lowercase												=			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 			_snake_case     (						UpperCamelCase_       ):







						def __init__(self     ,							UpperCAmelCase_							)			->						Tuple:
											super().__init__(__UpperCamelCase							)

											__lowercase												=			AlbertTransformerWithPabee(__UpperCamelCase							)

											self.init_weights()
											__lowercase												=			0
											__lowercase												=			0
											__lowercase												=			0

											__lowercase												=			0







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Union[str, Any]:
											__lowercase												=			threshold







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Optional[int]:
											__lowercase												=			patience







						def        _UpperCamelCase    (self							)			->						Dict:
											__lowercase												=			0
											__lowercase												=			0







						def        _UpperCamelCase    (self							)			->						str:
											__lowercase												=			self.inference_layers_num / self.inference_instances_num
											__lowercase												=			(
											    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(__UpperCamelCase							)







						@add_start_docstrings_to_model_forward(__UpperCamelCase							)
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=False     ,							)			->						Optional[int]:


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

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

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

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

											__lowercase												=			self.embeddings(
											    __UpperCamelCase     ,							position_ids=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							inputs_embeds=__UpperCamelCase							)
											__lowercase												=			embedding_output

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

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

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

																					__lowercase												=			logits
																					if patient_counter == self.patience:
																										break
																__lowercase												=			[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\n    the pooled output) e.g. for GLUE tasks. '						,			UpperCamelCase_						,			)
class 			_snake_case     (						UpperCamelCase_       ):







						def __init__(self     ,							UpperCAmelCase_							)			->						List[str]:
											super().__init__(__UpperCamelCase							)
											__lowercase												=			config.num_labels

											__lowercase												=			AlbertModelWithPabee(__UpperCamelCase							)
											__lowercase												=			nn.Dropout(config.classifier_dropout_prob							)
											__lowercase												=			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(__UpperCamelCase							)
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							)			->						int:
											__lowercase												=			self.albert(
											    input_ids=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							position_ids=__UpperCamelCase     ,							head_mask=__UpperCamelCase     ,							inputs_embeds=__UpperCamelCase     ,							output_dropout=self.dropout     ,							output_layers=self.classifiers     ,							regression=self.num_labels == 1     ,							)

											__lowercase												=			(logits[-1],)

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

											return outputs


"""simple docstring"""

def 				__UpperCamelCase   (    a__: list ,	a__: list ,	a__: list ,	a__: int ,	a__: int			)				->			None:




					"""simple docstring"""



					if target == 0:
										answer.append(path.copy()			)
					else:
										for index in range(a__ ,	len(a__			)			):
															if target >= candidates[index]:
																				path.append(candidates[index]			)
																				backtrack(a__ ,	a__ ,	a__ ,	target - candidates[index] ,	a__			)
																				path.pop(len(a__			) - 1			)





def 				__UpperCamelCase   (    a__: list ,	a__: int			)				->			list:




					"""simple docstring"""



					__lowercase												=			[]  # type: list[int]
					__lowercase												=			[]  # type: list[int]
					backtrack(a__ ,	a__ ,	a__ ,	a__ ,	0			)
					return answer





def 				__UpperCamelCase   (    )				->			None:




					"""simple docstring"""



					print(combination_sum([-8, 2.3, 0] ,	1			)			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()


