






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__lowerCAmelCase     :			Any      =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def 	_UpperCamelCase							(				lowercase__				):
						if "token" in model_name_or_path:
												return "rag_token"
						if "sequence" in model_name_or_path:
												return "rag_sequence"
						if "bart" in model_name_or_path:
												return "bart"
						return None


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						return max(metric_fn(lowercase__   ,	lowercase__				) for gt in ground_truths				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		[line.strip() for line in open(lowercase__   ,	'''r'''				).readlines()]
						__SCREAMING_SNAKE_CASE						:					Any		      =		[]

						if args.gold_data_mode == "qa":
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		pd.read_csv(lowercase__   ,	sep='''\t'''   ,	header=lowercase__				)
												for answer_list in data[1]:
																		__SCREAMING_SNAKE_CASE						:					Tuple		      =		ast.literal_eval(lowercase__				)
																		answers.append(lowercase__				)
						else:
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		[line.strip() for line in open(lowercase__   ,	'''r'''				).readlines()]
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		[[reference] for reference in references]

						__SCREAMING_SNAKE_CASE						:					Tuple		      =		0
						for prediction, ground_truths in zip(lowercase__   ,	lowercase__				):
												total += 1
												em += metric_max_over_ground_truths(lowercase__   ,	lowercase__   ,	lowercase__				)
												fa += metric_max_over_ground_truths(lowercase__   ,	lowercase__   ,	lowercase__				)

						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		100.0 * em / total
						__SCREAMING_SNAKE_CASE						:					int		      =		100.0 * fa / total

						logger.info(F'''F1: {fa:.2f}'''				)
						logger.info(F'''EM: {em:.2f}'''				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		args.k
						__SCREAMING_SNAKE_CASE						:					Any		      =		[line.strip() for line in open(lowercase__   ,	'''r'''				).readlines()]
						__SCREAMING_SNAKE_CASE						:					int		      =		[line.strip() for line in open(lowercase__   ,	'''r'''				).readlines()]

						__SCREAMING_SNAKE_CASE						:					Any		      =		0
						for hypo, reference in zip(lowercase__   ,	lowercase__				):
												__SCREAMING_SNAKE_CASE						:					str		      =		set(hypo.split('''\t'''				)[:k]				)
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		set(reference.split('''\t'''				)				)
												total += 1
												em += len(hypo_provenance & ref_provenance				) / k

						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		100.0 * em / total
						logger.info(F'''Precision@{k}: {em: .2f}'''				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						def strip_title(lowercase__				):
												if title.startswith('''"'''				):
																		__SCREAMING_SNAKE_CASE						:					Tuple		      =		title[1:]
												if title.endswith('''"'''				):
																		__SCREAMING_SNAKE_CASE						:					Any		      =		title[:-1]
												return title

						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    lowercase__   ,	return_tensors='''pt'''   ,	padding=lowercase__   ,	truncation=lowercase__   ,	)['''input_ids'''].to(args.device				)

						__SCREAMING_SNAKE_CASE						:					str		      =		rag_model.rag.question_encoder(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		question_enc_outputs[0]

						__SCREAMING_SNAKE_CASE						:					Tuple		      =		rag_model.retriever(
						    lowercase__   ,	question_enc_pool_output.cpu().detach().to(torch.floataa				).numpy()   ,	prefix=rag_model.rag.generator.config.prefix   ,	n_docs=rag_model.config.n_docs   ,	return_tensors='''pt'''   ,	)
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		[]
						for docs in all_docs:
												__SCREAMING_SNAKE_CASE						:					str		      =		[strip_title(lowercase__				) for title in docs['''title''']]
												provenance_strings.append('''\t'''.join(lowercase__				)				)
						return provenance_strings


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						with torch.no_grad():
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    lowercase__   ,	return_tensors='''pt'''   ,	padding=lowercase__   ,	truncation=lowercase__				)

												__SCREAMING_SNAKE_CASE						:					str		      =		inputs_dict.input_ids.to(args.device				)
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		inputs_dict.attention_mask.to(args.device				)
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		rag_model.generate(  # rag_model overwrites generate
												    lowercase__   ,	attention_mask=lowercase__   ,	num_beams=args.num_beams   ,	min_length=args.min_length   ,	max_length=args.max_length   ,	early_stopping=lowercase__   ,	num_return_sequences=1   ,	bad_words_ids=[[0, 0]]   ,	)
												__SCREAMING_SNAKE_CASE						:					str		      =		rag_model.retriever.generator_tokenizer.batch_decode(lowercase__   ,	skip_special_tokens=lowercase__				)

												if args.print_predictions:
																		for q, a in zip(lowercase__   ,	lowercase__				):
																								logger.info('''Q: {} - A: {}'''.format(lowercase__   ,	lowercase__				)				)

												return answers


def 	_UpperCamelCase							(				):
						__SCREAMING_SNAKE_CASE						:					str		      =		argparse.ArgumentParser()
						parser.add_argument(
						    '''--model_type'''   ,	choices=['''rag_sequence''', '''rag_token''', '''bart''']   ,	type=lowercase__   ,	help=(
						        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
						        ''' model_name_or_path'''
						    )   ,	)
						parser.add_argument(
						    '''--index_name'''   ,	default=lowercase__   ,	choices=['''exact''', '''compressed''', '''legacy''']   ,	type=lowercase__   ,	help='''RAG model retriever type'''   ,	)
						parser.add_argument(
						    '''--index_path'''   ,	default=lowercase__   ,	type=lowercase__   ,	help='''Path to the retrieval index'''   ,	)
						parser.add_argument('''--n_docs'''   ,	default=5   ,	type=lowercase__   ,	help='''Number of retrieved docs'''				)
						parser.add_argument(
						    '''--model_name_or_path'''   ,	default=lowercase__   ,	type=lowercase__   ,	required=lowercase__   ,	help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''   ,	)
						parser.add_argument(
						    '''--eval_mode'''   ,	choices=['''e2e''', '''retrieval''']   ,	default='''e2e'''   ,	type=lowercase__   ,	help=(
						        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
						        ''' precision@k.'''
						    )   ,	)
						parser.add_argument('''--k'''   ,	default=1   ,	type=lowercase__   ,	help='''k for the precision@k calculation'''				)
						parser.add_argument(
						    '''--evaluation_set'''   ,	default=lowercase__   ,	type=lowercase__   ,	required=lowercase__   ,	help='''Path to a file containing evaluation samples'''   ,	)
						parser.add_argument(
						    '''--gold_data_path'''   ,	default=lowercase__   ,	type=lowercase__   ,	required=lowercase__   ,	help='''Path to a tab-separated file with gold samples'''   ,	)
						parser.add_argument(
						    '''--gold_data_mode'''   ,	default='''qa'''   ,	type=lowercase__   ,	choices=['''qa''', '''ans''']   ,	help=(
						        '''Format of the gold data file'''
						        '''qa - a single line in the following format: question [tab] answer_list'''
						        '''ans - a single line of the gold file contains the expected answer string'''
						    )   ,	)
						parser.add_argument(
						    '''--predictions_path'''   ,	type=lowercase__   ,	default='''predictions.txt'''   ,	help='''Name of the predictions file, to be stored in the checkpoints directory'''   ,	)
						parser.add_argument(
						    '''--eval_all_checkpoints'''   ,	action='''store_true'''   ,	help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''   ,	)
						parser.add_argument(
						    '''--eval_batch_size'''   ,	default=8   ,	type=lowercase__   ,	help='''Batch size per GPU/CPU for evaluation.'''   ,	)
						parser.add_argument(
						    '''--recalculate'''   ,	help='''Recalculate predictions even if the prediction file exists'''   ,	action='''store_true'''   ,	)
						parser.add_argument(
						    '''--num_beams'''   ,	default=4   ,	type=lowercase__   ,	help='''Number of beams to be used when generating answers'''   ,	)
						parser.add_argument('''--min_length'''   ,	default=1   ,	type=lowercase__   ,	help='''Min length of the generated answers'''				)
						parser.add_argument('''--max_length'''   ,	default=50   ,	type=lowercase__   ,	help='''Max length of the generated answers'''				)

						parser.add_argument(
						    '''--print_predictions'''   ,	action='''store_true'''   ,	help='''If True, prints predictions while evaluating.'''   ,	)
						parser.add_argument(
						    '''--print_docs'''   ,	action='''store_true'''   ,	help='''If True, prints docs retried while generating.'''   ,	)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		parser.parse_args()
						__SCREAMING_SNAKE_CASE						:					int		      =		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)
						return args


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Dict		      =		{}
						if args.model_type is None:
												__SCREAMING_SNAKE_CASE						:					int		      =		infer_model_type(args.model_name_or_path				)
												assert args.model_type is not None
						if args.model_type.startswith('''rag'''				):
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
												__SCREAMING_SNAKE_CASE						:					int		      =		args.n_docs
												if args.index_name is not None:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		args.index_name
												if args.index_path is not None:
																		__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		args.index_path
						else:
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		BartForConditionalGeneration

						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(
						    [f.path for f in os.scandir(args.model_name_or_path				) if f.is_dir()]
						    if args.eval_all_checkpoints
						    else [args.model_name_or_path]
						)

						logger.info('''Evaluate the following checkpoints: %s'''   ,	lowercase__				)

						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

						for checkpoint in checkpoints:
												if os.path.exists(args.predictions_path				) and (not args.recalculate):
																		logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path				)				)
																		score_fn(lowercase__   ,	args.predictions_path   ,	args.gold_data_path				)
																		continue

												logger.info('''***** Running evaluation for {} *****'''.format(lowercase__				)				)
												logger.info('''  Batch size = %d'''   ,	args.eval_batch_size				)
												logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path				)				)

												if args.model_type.startswith('''rag'''				):
																		__SCREAMING_SNAKE_CASE						:					str		      =		RagRetriever.from_pretrained(lowercase__   ,	**lowercase__				)
																		__SCREAMING_SNAKE_CASE						:					int		      =		model_class.from_pretrained(lowercase__   ,	retriever=lowercase__   ,	**lowercase__				)
																		model.retriever.init_retrieval()
												else:
																		__SCREAMING_SNAKE_CASE						:					str		      =		model_class.from_pretrained(lowercase__   ,	**lowercase__				)
												model.to(args.device				)

												with open(args.evaluation_set   ,	'''r'''				) as eval_file, open(args.predictions_path   ,	'''w'''				) as preds_file:
																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		[]
																		for line in tqdm(lowercase__				):
																								questions.append(line.strip()				)
																								if len(lowercase__				) == args.eval_batch_size:
																														__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		evaluate_batch_fn(lowercase__   ,	lowercase__   ,	lowercase__				)
																														preds_file.write('''\n'''.join(lowercase__				) + '''\n'''				)
																														preds_file.flush()
																														__SCREAMING_SNAKE_CASE						:					str		      =		[]
																		if len(lowercase__				) > 0:
																								__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		evaluate_batch_fn(lowercase__   ,	lowercase__   ,	lowercase__				)
																								preds_file.write('''\n'''.join(lowercase__				)				)
																								preds_file.flush()

																		score_fn(lowercase__   ,	args.predictions_path   ,	args.gold_data_path				)


if __name__ == "__main__":
						__lowerCAmelCase     :			Tuple      =get_args()
						main(args)




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,
)


__lowerCAmelCase     :			Any      =logging.getLogger(__name__)
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :List[Any]=None      ,      lowerCAmelCase__      :Optional[Any]=None     )		->      List[Any]:
										if current_layer == 0:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.embedding_hidden_mapping_in(lowerCAmelCase__     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		hidden_states[0]

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		int(self.config.num_hidden_layers / self.config.num_hidden_groups     )

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

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.albert_layer_groups[group_idx](
										    lowerCAmelCase__      ,      lowerCAmelCase__      ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]      ,      )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		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.'''						,      A__						,      )
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :int      ,      lowerCAmelCase__      :List[str]     )		->      List[Any]:
										super().__init__(lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		AlbertTransformerWithPabee(lowerCAmelCase__     )

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

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		0


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :Optional[int]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		threshold


				def 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :Optional[int]     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		patience


				def 		__magic_name__(	self      :Union[str, Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Any		      =		0
										__SCREAMING_SNAKE_CASE						:					int		      =		0


				def 		__magic_name__(	self      :Optional[int]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.inference_layers_num / self.inference_instances_num
										__SCREAMING_SNAKE_CASE						:					Union[str, 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 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :Optional[Any]=None      ,      lowerCAmelCase__      :Any=None      ,      lowerCAmelCase__      :Dict=None      ,      lowerCAmelCase__      :Optional[int]=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :str=False      ,      )		->      Union[str, 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						:					List[Any]		      =		input_ids.size()
										elif inputs_embeds is not None:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''     )

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

										if attention_mask is None:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.ones(lowerCAmelCase__      ,      device=lowerCAmelCase__     )
										if token_type_ids is None:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.zeros(lowerCAmelCase__      ,      dtype=torch.long      ,      device=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		attention_mask.unsqueeze(1     ).unsqueeze(2     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
										__SCREAMING_SNAKE_CASE						:					Dict		      =		(1.0 - extended_attention_mask) * -1_0000.0
										__SCREAMING_SNAKE_CASE						:					str		      =		self.get_head_mask(lowerCAmelCase__      ,      self.config.num_hidden_layers     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.embeddings(
										    lowerCAmelCase__      ,      position_ids=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      inputs_embeds=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		embedding_output

										if self.training:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		[]
																for i in range(self.config.num_hidden_layers     ):
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.encoder.adaptive_forward(
																						    lowerCAmelCase__      ,      current_layer=lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      head_mask=lowerCAmelCase__      ,      )

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

																						__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
																						__SCREAMING_SNAKE_CASE						:					str		      =		output_layers[i](lowerCAmelCase__     )
																						if regression:
																												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		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						:					List[str]		      =		0
																						else:
																												__SCREAMING_SNAKE_CASE						:					List[str]		      =		logits.detach().argmax(dim=1     )
																												if patient_result is not None:
																																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		patient_result.detach().argmax(dim=1     )
																												if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase__     )     ):
																																		patient_counter += 1
																												else:
																																		__SCREAMING_SNAKE_CASE						:					str		      =		0

																						__SCREAMING_SNAKE_CASE						:					str		      =		logits
																						if patient_counter == self.patience:
																												break
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		[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. '''						,      A__						,      )
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :List[Any]      ,      lowerCAmelCase__      :int     )		->      Optional[int]:
										super().__init__(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		config.num_labels

										__SCREAMING_SNAKE_CASE						:					str		      =		AlbertModelWithPabee(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		nn.Dropout(config.classifier_dropout_prob     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		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 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :List[str]=None      ,      lowerCAmelCase__      :Dict=None      ,      lowerCAmelCase__      :str=None      ,      lowerCAmelCase__      :Tuple=None      ,      lowerCAmelCase__      :int=None      ,      lowerCAmelCase__      :Any=None      ,      )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		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      ,      )

										__SCREAMING_SNAKE_CASE						:					int		      =		(logits[-1],)

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

										return outputs
