


'''simple docstring'''

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
	import transformers

def 							__magic_name__(						lowerCamelCase):
						random.seed(lowerCamelCase)
						np.random.seed(lowerCamelCase)
						torch.manual_seed(lowerCamelCase)
						torch.cuda.manual_seed_all(lowerCamelCase)
						# ^^ safe to call this function even if cuda is not available




class       a__				:







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase = 0.9_9_9_9       ,			__lowercase = 0.0       ,			__lowercase = 0       ,			__lowercase = False       ,			__lowercase = 1.0       ,			__lowercase = 2 / 3       ,			__lowercase = None       ,			__lowercase = None       ,			**__lowercase       ,			):

										if isinstance(__lowercase       ,			torch.nn.Module		):
																__lowerCAmelCase							       =				(
																    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
																    '''Please pass the parameters of the module instead.'''
																)
																deprecate(
																    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''       ,			'''1.0.0'''       ,			__lowercase       ,			standard_warn=__lowercase       ,			)
																__lowerCAmelCase							       =				parameters.parameters()

																# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																__lowerCAmelCase							       =				True

										if kwargs.get('''max_value'''       ,			__lowercase		) is not None:
																__lowerCAmelCase							       =				'''The `max_value` argument is deprecated. Please use `decay` instead.'''
																deprecate('''max_value'''       ,			'''1.0.0'''       ,			__lowercase       ,			standard_warn=__lowercase		)
																__lowerCAmelCase							       =				kwargs['''max_value''']

										if kwargs.get('''min_value'''       ,			__lowercase		) is not None:
																__lowerCAmelCase							       =				'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
																deprecate('''min_value'''       ,			'''1.0.0'''       ,			__lowercase       ,			standard_warn=__lowercase		)
																__lowerCAmelCase							       =				kwargs['''min_value''']

										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				[p.clone().detach() for p in parameters]

										if kwargs.get('''device'''       ,			__lowercase		) is not None:
																__lowerCAmelCase							       =				'''The `device` argument is deprecated. Please use `to` instead.'''
																deprecate('''device'''       ,			'''1.0.0'''       ,			__lowercase       ,			standard_warn=__lowercase		)
																self.to(device=kwargs['''device''']		)

										__lowerCAmelCase							       =				None

										__lowerCAmelCase							       =				decay
										__lowerCAmelCase							       =				min_decay
										__lowerCAmelCase							       =				update_after_step
										__lowerCAmelCase							       =				use_ema_warmup
										__lowerCAmelCase							       =				inv_gamma
										__lowerCAmelCase							       =				power
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				None  # set in `step()`

										__lowerCAmelCase							       =				model_cls
										__lowerCAmelCase							       =				model_config




				@classmethod
				def 					_snake_case  (cls       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				model_cls.load_config(__lowercase       ,			return_unused_kwargs=__lowercase		)
										__lowerCAmelCase							       =				model_cls.from_pretrained(__lowercase		)

										__lowerCAmelCase							       =				cls(model.parameters()       ,			model_cls=__lowercase       ,			model_config=model.config		)

										ema_model.load_state_dict(__lowercase		)
										return ema_model




				def 					_snake_case  (self       ,			__lowercase		):
										if self.model_cls is None:
																raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''		)

										if self.model_config is None:
																raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''		)

										__lowerCAmelCase							       =				self.model_cls.from_config(self.model_config		)
										__lowerCAmelCase							       =				self.state_dict()
										state_dict.pop('''shadow_params'''       ,			__lowercase		)

										model.register_to_config(**__lowercase		)
										self.copy_to(model.parameters()		)
										model.save_pretrained(__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				max(0       ,			optimization_step - self.update_after_step - 1		)

										if step <= 0:
																return 0.0

										if self.use_ema_warmup:
																__lowerCAmelCase							       =				1 - (1 + step / self.inv_gamma) ** -self.power
										else:
																__lowerCAmelCase							       =				(1 + step) / (10 + step)

										__lowerCAmelCase							       =				min(__lowercase       ,			self.decay		)
										# make sure decay is not smaller than min_decay
										__lowerCAmelCase							       =				max(__lowercase       ,			self.min_decay		)
										return cur_decay_value




				@torch.no_grad()
				def 					_snake_case  (self       ,			__lowercase		):
										if isinstance(__lowercase       ,			torch.nn.Module		):
																__lowerCAmelCase							       =				(
																    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
																    '''Please pass the parameters of the module instead.'''
																)
																deprecate(
																    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''       ,			'''1.0.0'''       ,			__lowercase       ,			standard_warn=__lowercase       ,			)
																__lowerCAmelCase							       =				parameters.parameters()

										__lowerCAmelCase							       =				list(__lowercase		)

										self.optimization_step += 1

										# Compute the decay factor for the exponential moving average.
										__lowerCAmelCase							       =				self.get_decay(self.optimization_step		)
										__lowerCAmelCase							       =				decay
										__lowerCAmelCase							       =				1 - decay

										__lowerCAmelCase							       =				contextlib.nullcontext
										if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																import deepspeed

										for s_param, param in zip(self.shadow_params       ,			__lowercase		):
																if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																						__lowerCAmelCase							       =				deepspeed.zero.GatheredParameters(__lowercase       ,			modifier_rank=__lowercase		)

																with context_manager():
																						if param.requires_grad:
																												s_param.sub_(one_minus_decay * (s_param - param)		)
																						else:
																												s_param.copy_(__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				list(__lowercase		)
										for s_param, param in zip(self.shadow_params       ,			__lowercase		):
																param.data.copy_(s_param.to(param.device		).data		)




				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				[
										    p.to(device=__lowercase       ,			dtype=__lowercase		) if p.is_floating_point() else p.to(device=__lowercase		)
										    for p in self.shadow_params
										]




				def 					_snake_case  (self		):
										return {
										    "decay": self.decay,
										    "min_decay": self.min_decay,
										    "optimization_step": self.optimization_step,
										    "update_after_step": self.update_after_step,
										    "use_ema_warmup": self.use_ema_warmup,
										    "inv_gamma": self.inv_gamma,
										    "power": self.power,
										    "shadow_params": self.shadow_params,
										}




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[param.detach().cpu().clone() for param in parameters]




				def 					_snake_case  (self       ,			__lowercase		):
										if self.temp_stored_params is None:
																raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''		)
										for c_param, param in zip(self.temp_stored_params       ,			__lowercase		):
																param.data.copy_(c_param.data		)

										# Better memory-wise.
										__lowerCAmelCase							       =				None




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				copy.deepcopy(__lowercase		)

										__lowerCAmelCase							       =				state_dict.get('''decay'''       ,			self.decay		)
										if self.decay < 0.0 or self.decay > 1.0:
																raise ValueError('''Decay must be between 0 and 1'''		)

										__lowerCAmelCase							       =				state_dict.get('''min_decay'''       ,			self.min_decay		)
										if not isinstance(self.min_decay       ,			__lowercase		):
																raise ValueError('''Invalid min_decay'''		)

										__lowerCAmelCase							       =				state_dict.get('''optimization_step'''       ,			self.optimization_step		)
										if not isinstance(self.optimization_step       ,			__lowercase		):
																raise ValueError('''Invalid optimization_step'''		)

										__lowerCAmelCase							       =				state_dict.get('''update_after_step'''       ,			self.update_after_step		)
										if not isinstance(self.update_after_step       ,			__lowercase		):
																raise ValueError('''Invalid update_after_step'''		)

										__lowerCAmelCase							       =				state_dict.get('''use_ema_warmup'''       ,			self.use_ema_warmup		)
										if not isinstance(self.use_ema_warmup       ,			__lowercase		):
																raise ValueError('''Invalid use_ema_warmup'''		)

										__lowerCAmelCase							       =				state_dict.get('''inv_gamma'''       ,			self.inv_gamma		)
										if not isinstance(self.inv_gamma       ,			(float, int)		):
																raise ValueError('''Invalid inv_gamma'''		)

										__lowerCAmelCase							       =				state_dict.get('''power'''       ,			self.power		)
										if not isinstance(self.power       ,			(float, int)		):
																raise ValueError('''Invalid power'''		)

										__lowerCAmelCase							       =				state_dict.get('''shadow_params'''       ,			__lowercase		)
										if shadow_params is not None:
																__lowerCAmelCase							       =				shadow_params
																if not isinstance(self.shadow_params       ,			__lowercase		):
																						raise ValueError('''shadow_params must be a list'''		)
																if not all(isinstance(__lowercase       ,			torch.Tensor		) for p in self.shadow_params		):
																						raise ValueError('''shadow_params must all be Tensors'''		)





'''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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


_UpperCAmelCase				: Optional[Any]							    =				logging.getLogger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				self.layer[current_layer](__lowercase       ,			__lowercase       ,			head_mask[current_layer]		)

										__lowerCAmelCase							       =				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.'							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



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

										__lowerCAmelCase							       =				BertEncoderWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0

										__lowerCAmelCase							       =				0




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				threshold




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				patience




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0




				def 					_snake_case  (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(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=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(__lowercase       ,			device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase							       =				torch.zeros(__lowercase       ,			dtype=torch.long       ,			device=__lowercase		)

										# 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.
										__lowerCAmelCase							       =				self.get_extended_attention_mask(__lowercase       ,			__lowercase       ,			__lowercase		)

										# 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:
																__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				encoder_hidden_states.size()
																__lowerCAmelCase							       =				(encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																						__lowerCAmelCase							       =				torch.ones(__lowercase       ,			device=__lowercase		)
																__lowerCAmelCase							       =				self.invert_attention_mask(__lowercase		)
										else:
																__lowerCAmelCase							       =				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]
										__lowerCAmelCase							       =				self.get_head_mask(__lowercase       ,			self.config.num_hidden_layers		)

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

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

																						__lowerCAmelCase							       =				self.pooler(__lowercase		)
																						__lowerCAmelCase							       =				output_layers[i](output_dropout(__lowercase		)		)
																						res.append(__lowercase		)
										elif self.patience == 0:  # Use all layers for inference
																__lowerCAmelCase							       =				self.encoder(
																    __lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase       ,			encoder_hidden_states=__lowercase       ,			encoder_attention_mask=__lowercase       ,			)
																__lowerCAmelCase							       =				self.pooler(encoder_outputs[0]		)
																__lowerCAmelCase							       =				[output_layers[self.config.num_hidden_layers - 1](__lowercase		)]
										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(
																						    __lowercase       ,			current_layer=__lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase		)

																						__lowerCAmelCase							       =				self.pooler(__lowercase		)
																						__lowerCAmelCase							       =				output_layers[i](__lowercase		)
																						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(__lowercase		)		):
																																		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(
    '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. '							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase							       =				config.num_labels

										__lowerCAmelCase							       =				BertModelWithPabee(__lowercase		)
										__lowerCAmelCase							       =				nn.Dropout(config.hidden_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(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			):
										__lowerCAmelCase							       =				self.bert(
										    input_ids=__lowercase       ,			attention_mask=__lowercase       ,			token_type_ids=__lowercase       ,			position_ids=__lowercase       ,			head_mask=__lowercase       ,			inputs_embeds=__lowercase       ,			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(__lowercase		):
																						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

