





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,
)


__lowercase       :List[str]       =  logging.getLogger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							def      A_  (      self				:     List[str]							,	a				:     Union[str, Any]							,	a				:     Tuple							,	a				:     Tuple=None							,	a				:     Any=None    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.layer[current_layer](a							,	a							,	head_mask[current_layer]    )

													SCREAMING_SNAKE_CASE__     :				Any   =					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."					,							lowercase__					,							)
class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     Dict							,	a				:     List[str]    )	->str:
													super().__init__(a    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					BertEncoderWithPabee(a    )

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

													SCREAMING_SNAKE_CASE__     :				List[str]   =					0

							def      A_  (      self				:     Any							,	a				:     Dict    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					threshold

							def      A_  (      self				:     Optional[Any]							,	a				:     List[Any]    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					patience

							def      A_  (      self				:     int    )	->int:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					0
													SCREAMING_SNAKE_CASE__     :				List[str]   =					0

							def      A_  (      self				:     Optional[int]    )	->int:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.inference_layers_num / self.inference_instances_num
													SCREAMING_SNAKE_CASE__     :				List[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      A_  (      self				:     Any							,	a				:     Optional[int]=None							,	a				:     Dict=None							,	a				:     Union[str, Any]=None							,	a				:     Optional[int]=None							,	a				:     Optional[int]=None							,	a				:     Dict=None							,	a				:     int=None							,	a				:     Union[str, Any]=None							,	a				:     Dict=None							,	a				:     List[Any]=None							,	a				:     Optional[Any]=False							,	)	->Any:

													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:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					input_ids.size()
													elif inputs_embeds is not None:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					inputs_embeds.size()[:-1]
													else:
																			raise ValueError("You have to specify either input_ids or inputs_embeds"    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					input_ids.device if input_ids is not None else inputs_embeds.device

													if attention_mask is None:
																			SCREAMING_SNAKE_CASE__     :				str   =					torch.ones(a							,	device=a    )
													if token_type_ids is None:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					torch.zeros(a							,	dtype=torch.long							,	device=a    )

													# 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.
													SCREAMING_SNAKE_CASE__     :				torch.Tensor   =					self.get_extended_attention_mask(a							,	a							,	a    )

													# 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:
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					encoder_hidden_states.size()
																			SCREAMING_SNAKE_CASE__     :				Any   =					(encoder_batch_size, encoder_sequence_length)
																			if encoder_attention_mask is None:
																									SCREAMING_SNAKE_CASE__     :				int   =					torch.ones(a							,	device=a    )
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.invert_attention_mask(a    )
													else:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					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]
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.get_head_mask(a							,	self.config.num_hidden_layers    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.embeddings(
													    input_ids=a							,	position_ids=a							,	token_type_ids=a							,	inputs_embeds=a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					embedding_output

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

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

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

																									SCREAMING_SNAKE_CASE__     :				int   =					logits
																									if patient_counter == self.patience:
																															break
																			SCREAMING_SNAKE_CASE__     :				Any   =					[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\n    the pooled output) e.g. for GLUE tasks. "					,							lowercase__					,							)
class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     Any							,	a				:     List[str]    )	->Any:
													super().__init__(a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					config.num_labels

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					BertModelWithPabee(a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					nn.Dropout(config.hidden_dropout_prob    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					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      A_  (      self				:     str							,	a				:     List[Any]=None							,	a				:     Optional[int]=None							,	a				:     Tuple=None							,	a				:     Dict=None							,	a				:     str=None							,	a				:     str=None							,	a				:     Optional[int]=None							,	)	->Dict:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.bert(
													    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							,	)

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

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

													return outputs






def       UpperCAmelCase							(     _lowerCamelCase      :  int = 1_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					-1
						SCREAMING_SNAKE_CASE__     :				str   =					0
						for a in range(1			,   n // 3     ):
												# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
												SCREAMING_SNAKE_CASE__     :				Tuple   =					(n * n - 2 * a * n) // (2 * n - 2 * a)
												SCREAMING_SNAKE_CASE__     :				Dict   =					n - a - b
												if c * c == (a * a + b * b):
																		SCREAMING_SNAKE_CASE__     :				str   =					a * b * c
																		if candidate >= product:
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					candidate
						return product


if __name__ == "__main__":
				print(f"{solution() = }")