


"""simple docstring"""







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


__a			 =   logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						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 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						return max(metric_fn(_UpperCamelCase	,			_UpperCamelCase ) for gt in ground_truths )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    [line.strip() for line in open(_UpperCamelCase	,			'''r''' ).readlines()]
						UpperCAmelCase__		=    []

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

						UpperCAmelCase__		=    UpperCAmelCase__		=    UpperCAmelCase__		=    0
						for prediction, ground_truths in zip(_UpperCamelCase	,			_UpperCamelCase ):
												total += 1
												em += metric_max_over_ground_truths(_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase )
												fa += metric_max_over_ground_truths(_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase )

						UpperCAmelCase__		=    100.0 * em / total
						UpperCAmelCase__		=    100.0 * fa / total

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    args.k
						UpperCAmelCase__		=    [line.strip() for line in open(_UpperCamelCase	,			'''r''' ).readlines()]
						UpperCAmelCase__		=    [line.strip() for line in open(_UpperCamelCase	,			'''r''' ).readlines()]

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

						UpperCAmelCase__		=    100.0 * em / total
						logger.info(F"""Precision@{k}: {em: .2f}""" )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						def strip_title(_UpperCamelCase ):
												if title.startswith('''"''' ):
																		UpperCAmelCase__		=    title[1:]
												if title.endswith('''"''' ):
																		UpperCAmelCase__		=    title[:-1]
												return title

						UpperCAmelCase__		=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    _UpperCamelCase	,			return_tensors='''pt'''	,			padding=_UpperCamelCase	,			truncation=_UpperCamelCase	,			)['''input_ids'''].to(args.device )

						UpperCAmelCase__		=    rag_model.rag.question_encoder(_UpperCamelCase )
						UpperCAmelCase__		=    question_enc_outputs[0]

						UpperCAmelCase__		=    rag_model.retriever(
						    _UpperCamelCase	,			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'''	,			)
						UpperCAmelCase__		=    rag_model.retriever.index.get_doc_dicts(result.doc_ids )
						UpperCAmelCase__		=    []
						for docs in all_docs:
												UpperCAmelCase__		=    [strip_title(_UpperCamelCase ) for title in docs['''title''']]
												provenance_strings.append('''\t'''.join(_UpperCamelCase ) )
						return provenance_strings






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						with torch.no_grad():
												UpperCAmelCase__		=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    _UpperCamelCase	,			return_tensors='''pt'''	,			padding=_UpperCamelCase	,			truncation=_UpperCamelCase )

												UpperCAmelCase__		=    inputs_dict.input_ids.to(args.device )
												UpperCAmelCase__		=    inputs_dict.attention_mask.to(args.device )
												UpperCAmelCase__		=    rag_model.generate(  # rag_model overwrites generate
												    _UpperCamelCase	,			attention_mask=_UpperCamelCase	,			num_beams=args.num_beams	,			min_length=args.min_length	,			max_length=args.max_length	,			early_stopping=_UpperCamelCase	,			num_return_sequences=1	,			bad_words_ids=[[0, 0]]	,			)
												UpperCAmelCase__		=    rag_model.retriever.generator_tokenizer.batch_decode(_UpperCamelCase	,			skip_special_tokens=_UpperCamelCase )

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

												return answers






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    argparse.ArgumentParser()
						parser.add_argument(
						    '''--model_type'''	,			choices=['''rag_sequence''', '''rag_token''', '''bart''']	,			type=_UpperCamelCase	,			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=_UpperCamelCase	,			choices=['''exact''', '''compressed''', '''legacy''']	,			type=_UpperCamelCase	,			help='''RAG model retriever type'''	,			)
						parser.add_argument(
						    '''--index_path'''	,			default=_UpperCamelCase	,			type=_UpperCamelCase	,			help='''Path to the retrieval index'''	,			)
						parser.add_argument('''--n_docs'''	,			default=5	,			type=_UpperCamelCase	,			help='''Number of retrieved docs''' )
						parser.add_argument(
						    '''--model_name_or_path'''	,			default=_UpperCamelCase	,			type=_UpperCamelCase	,			required=_UpperCamelCase	,			help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''	,			)
						parser.add_argument(
						    '''--eval_mode'''	,			choices=['''e2e''', '''retrieval''']	,			default='''e2e'''	,			type=_UpperCamelCase	,			help=(
						        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
						        ''' precision@k.'''
						    )	,			)
						parser.add_argument('''--k'''	,			default=1	,			type=_UpperCamelCase	,			help='''k for the precision@k calculation''' )
						parser.add_argument(
						    '''--evaluation_set'''	,			default=_UpperCamelCase	,			type=_UpperCamelCase	,			required=_UpperCamelCase	,			help='''Path to a file containing evaluation samples'''	,			)
						parser.add_argument(
						    '''--gold_data_path'''	,			default=_UpperCamelCase	,			type=_UpperCamelCase	,			required=_UpperCamelCase	,			help='''Path to a tab-separated file with gold samples'''	,			)
						parser.add_argument(
						    '''--gold_data_mode'''	,			default='''qa'''	,			type=_UpperCamelCase	,			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=_UpperCamelCase	,			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=_UpperCamelCase	,			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=_UpperCamelCase	,			help='''Number of beams to be used when generating answers'''	,			)
						parser.add_argument('''--min_length'''	,			default=1	,			type=_UpperCamelCase	,			help='''Min length of the generated answers''' )
						parser.add_argument('''--max_length'''	,			default=50	,			type=_UpperCamelCase	,			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.'''	,			)
						UpperCAmelCase__		=    parser.parse_args()
						UpperCAmelCase__		=    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
						return args






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    {}
						if args.model_type is None:
												UpperCAmelCase__		=    infer_model_type(args.model_name_or_path )
												assert args.model_type is not None
						if args.model_type.startswith('''rag''' ):
												UpperCAmelCase__		=    RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
												UpperCAmelCase__		=    args.n_docs
												if args.index_name is not None:
																		UpperCAmelCase__		=    args.index_name
												if args.index_path is not None:
																		UpperCAmelCase__		=    args.index_path
						else:
												UpperCAmelCase__		=    BartForConditionalGeneration

						UpperCAmelCase__		=    (
						    [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'''	,			_UpperCamelCase )

						UpperCAmelCase__		=    get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
						UpperCAmelCase__		=    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(_UpperCamelCase	,			args.predictions_path	,			args.gold_data_path )
																		continue

												logger.info('''***** Running evaluation for {} *****'''.format(_UpperCamelCase ) )
												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''' ):
																		UpperCAmelCase__		=    RagRetriever.from_pretrained(_UpperCamelCase	,			**_UpperCamelCase )
																		UpperCAmelCase__		=    model_class.from_pretrained(_UpperCamelCase	,			retriever=_UpperCamelCase	,			**_UpperCamelCase )
																		model.retriever.init_retrieval()
												else:
																		UpperCAmelCase__		=    model_class.from_pretrained(_UpperCamelCase	,			**_UpperCamelCase )
												model.to(args.device )

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

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


if __name__ == "__main__":
						__a			 =   get_args()
						main(args)



"""simple docstring"""







import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]






							def __init__(						self :     int		, lowerCamelCase_ :     Tuple				):
													assert Path(hparams.data_dir				).exists()
													UpperCAmelCase__		=    Path(hparams.output_dir				)
													self.output_dir.mkdir(exist_ok=lowerCamelCase_				)

													UpperCAmelCase__		=    self.output_dir.joinpath('''student'''				)

													UpperCAmelCase__		=    str(lowerCamelCase_				)  # Tell lightning we are training the student
													UpperCAmelCase__		=    AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher				).eval()
													use_task_specific_params(lowerCamelCase_		, hparams.task				)  # We copy good generation parameters to student by default
													if hparams.student is not None:
																			UpperCAmelCase__		=    AutoModelForSeqaSeqLM.from_pretrained(hparams.student				)
																			use_task_specific_params(lowerCamelCase_		, hparams.task				)
																			UpperCAmelCase__	,				UpperCAmelCase__		=    None, None
													else:
																			UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    create_student_by_copying_alternating_layers(
																			    lowerCamelCase_		, e=hparams.student_encoder_layers		, d=hparams.student_decoder_layers		, save_path=lowerCamelCase_				)

													if hparams.length_penalty != -1:
																			UpperCAmelCase__		=    hparams.length_penalty
													UpperCAmelCase__		=    hparams.teacher  # Use teacher's tokenizer
													super().__init__(lowerCamelCase_		, model=lowerCamelCase_		, config=student.config				)
													assert student.config.model_type == teacher.config.model_type, (
													    f"""teacher, student model types should be the same, got {student.config.model_type} !="""
													    f""" {teacher.config.model_type}"""
													)

													if student.config.model_type == "t5":
																			UpperCAmelCase__		=    len(student.get_encoder().block				)
																			UpperCAmelCase__		=    len(student.get_decoder().block				)
																			UpperCAmelCase__		=    len(teacher.get_encoder().block				)
																			UpperCAmelCase__		=    len(teacher.get_decoder().block				)
													else:
																			UpperCAmelCase__		=    student.config.encoder_layers
																			UpperCAmelCase__		=    student.config.decoder_layers
																			UpperCAmelCase__		=    teacher.config.encoder_layers
																			UpperCAmelCase__		=    teacher.config.decoder_layers

													UpperCAmelCase__		=    not (hparams.student is None or hparams.teacher == hparams.student)
													UpperCAmelCase__		=    (not self.different_base_models) and hparams.alpha_hid > 0
													UpperCAmelCase__		=    self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
													# self.different_encoder determines whether we need to run the teacher encoder
													UpperCAmelCase__		=    teacher
													freeze_params(self.teacher				)

													if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
																			try:
																									del self.teacher.model.encoder
																			except AttributeError:  # T5
																									del self.teacher.encoder

													if e_layer_ids is None:
																			UpperCAmelCase__		=    list(range(lowerCamelCase_				)				)
													if d_layer_ids is None:
																			UpperCAmelCase__		=    list(range(lowerCamelCase_				)				)

													UpperCAmelCase__	,				UpperCAmelCase__		=    e_layer_ids, d_layer_ids  # type: List[int], List[int]

													if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
																			if hparams.supervise_forward:
																									UpperCAmelCase__		=    get_layers_to_supervise(
																									    n_student=len(self.e_layer_ids				)		, n_teacher=lowerCamelCase_				)
																									UpperCAmelCase__		=    get_layers_to_supervise(
																									    n_student=len(self.d_layer_ids				)		, n_teacher=lowerCamelCase_				)
																			else:  # student layer should emulate hidden states of the teacher layer it was copied from
																									UpperCAmelCase__		=    self.e_layer_ids
																									UpperCAmelCase__		=    self.d_layer_ids
													else:
																			UpperCAmelCase__		=    None
																			UpperCAmelCase__		=    None

													UpperCAmelCase__		=    nn.KLDivLoss(reduction='''batchmean'''				)
													UpperCAmelCase__		=    2.0
													UpperCAmelCase__		=    hparams.alpha_mlm
													UpperCAmelCase__		=    hparams.alpha_ce
													UpperCAmelCase__		=    hparams.alpha_hid
													gc.collect()
													torch.cuda.empty_cache()






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     Dict		, lowerCamelCase_ :     Union[str, Any]				):
													UpperCAmelCase__		=    mask[:, :, None].expand_as(lowerCamelCase_				)
													UpperCAmelCase__		=    s_logits.size(-1				)
													UpperCAmelCase__		=    torch.masked_select(lowerCamelCase_		, lowerCamelCase_				)  # (bs * seq_length * voc_size) modulo the 1s in mask
													UpperCAmelCase__		=    torch.masked_select(lowerCamelCase_		, lowerCamelCase_				)  # (bs * seq_length * voc_size) modulo the 1s in mask
													UpperCAmelCase__		=    s_logits_slct.view(-1		, lowerCamelCase_				)  # (bs * seq_length, voc_size) modulo the 1s in mask
													UpperCAmelCase__		=    t_logits_slct.view(-1		, lowerCamelCase_				)  # (bs * seq_length, voc_size) modulo the 1s in mask
													assert t_logits_slct.size() == s_logits_slct.size()
													UpperCAmelCase__		=    (
													    self.ce_loss_fct(
													        nn.functional.log_softmax(s_logits_slct / self.temperature		, dim=-1				)		, nn.functional.softmax(t_logits_slct / self.temperature		, dim=-1				)		, )
													    * (self.temperature) ** 2
													)
													return loss_ce






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     int		, lowerCamelCase_ :     Optional[Any]				):
													SummarizationModule.add_model_specific_args(lowerCamelCase_		, lowerCamelCase_				)
													add_distill_args(lowerCamelCase_				)
													return parser






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     dict				):
													UpperCAmelCase__		=    self.tokenizer.pad_token_id
													UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
													if isinstance(self.model		, lowerCamelCase_				):
																			UpperCAmelCase__		=    self.model._shift_right(lowerCamelCase_				)
													else:
																			UpperCAmelCase__		=    shift_tokens_right(lowerCamelCase_		, lowerCamelCase_				)

													# noinspection PyCallingNonCallable
													UpperCAmelCase__		=    self(
													    lowerCamelCase_		, attention_mask=lowerCamelCase_		, decoder_input_ids=lowerCamelCase_		, output_hidden_states=self.do_calc_hidden_loss		, output_attentions=lowerCamelCase_		, use_cache=lowerCamelCase_		, )
													UpperCAmelCase__		=    student_outputs['''logits''']

													# Same cross entropy vs. label smoothing logic as finetune.py
													assert lm_logits.shape[-1] == self.model.config.vocab_size
													if self.hparams.label_smoothing == 0:
																			# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																			UpperCAmelCase__		=    nn.CrossEntropyLoss(ignore_index=lowerCamelCase_				)
																			UpperCAmelCase__		=    loss_fct(lm_logits.view(-1		, lm_logits.shape[-1]				)		, labels.view(-1				)				)
													else:
																			UpperCAmelCase__		=    nn.functional.log_softmax(lowerCamelCase_		, dim=-1				)
																			UpperCAmelCase__	,				UpperCAmelCase__		=    label_smoothed_nll_loss(
																			    lowerCamelCase_		, lowerCamelCase_		, self.hparams.label_smoothing		, ignore_index=lowerCamelCase_				)

													def zero_tensor():
																			return torch.tensor(0.0				).type_as(lowerCamelCase_				)

													UpperCAmelCase__		=    student_outputs[
													    '''encoder_last_hidden_state'''
													]  # use this unless self.different_base_models
													UpperCAmelCase__	,				UpperCAmelCase__		=    zero_tensor(), zero_tensor()
													if self.different_encoder:  # compute encoder hidden state loss
																			UpperCAmelCase__		=    self.teacher.get_encoder()(
																			    lowerCamelCase_		, attention_mask=lowerCamelCase_		, output_hidden_states=self.do_calc_hidden_loss		, )
																			if self.different_base_models:
																									UpperCAmelCase__		=    all_teacher_encoder_outputs['''last_hidden_state''']
																			elif self.do_calc_hidden_loss:
																									UpperCAmelCase__		=    self.calc_hidden_loss(
																									    lowerCamelCase_		, student_outputs['''encoder_hidden_states''']		, all_teacher_encoder_outputs['''hidden_states''']		, self.e_matches		, normalize_hidden=self.hparams.normalize_hidden		, )

													UpperCAmelCase__		=    self.teacher(
													    lowerCamelCase_		, attention_mask=lowerCamelCase_		, encoder_outputs=(teacher_enc_outputs,)		, decoder_input_ids=lowerCamelCase_		, output_hidden_states=self.do_calc_hidden_loss		, use_cache=lowerCamelCase_		, )
													UpperCAmelCase__		=    decoder_input_ids.ne(lowerCamelCase_				)
													UpperCAmelCase__		=    self.calc_ce_loss(lowerCamelCase_		, lowerCamelCase_		, teacher_outputs['''logits''']				)
													if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
																			UpperCAmelCase__		=    self.calc_hidden_loss(
																			    lowerCamelCase_		, student_outputs['''decoder_hidden_states''']		, teacher_outputs['''decoder_hidden_states''']		, self.d_matches		, normalize_hidden=self.hparams.normalize_hidden		, )

													UpperCAmelCase__		=    (
													    self.alpha_ce * loss_ce
													    + self.alpha_mlm * student_lm_loss
													    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
													)
													return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     Optional[Any]		, lowerCamelCase_ :     Optional[Any]		, lowerCamelCase_ :     int		, lowerCamelCase_ :     Optional[Any]		, lowerCamelCase_ :     int				):
													UpperCAmelCase__		=    '''expected list or tuple for hidden_states, got tensor of shape: '''
													assert not isinstance(lowerCamelCase_		, torch.Tensor				), f"""{msg}{hidden_states.shape}"""
													assert not isinstance(lowerCamelCase_		, torch.Tensor				), f"""{msg}{hidden_states_T.shape}"""
													UpperCAmelCase__		=    attention_mask.to(hidden_states[0]				)
													UpperCAmelCase__		=    mask.sum() * hidden_states[0].size(-1				)
													UpperCAmelCase__		=    torch.stack([hidden_states[i] for i in range(len(lowerCamelCase_				)				)]				)
													UpperCAmelCase__		=    torch.stack([hidden_states_T[j] for j in matches]				)
													assert student_states.shape == teacher_states.shape, f"""{student_states.shape} != {teacher_states.shape}"""
													if normalize_hidden:
																			UpperCAmelCase__		=    nn.functional.layer_norm(lowerCamelCase_		, student_states.shape[1:]				)
																			UpperCAmelCase__		=    nn.functional.layer_norm(lowerCamelCase_		, teacher_states.shape[1:]				)
													UpperCAmelCase__		=    nn.functional.mse_loss(lowerCamelCase_		, lowerCamelCase_		, reduction='''none'''				)
													UpperCAmelCase__		=    (mse * mask.unsqueeze(0				).unsqueeze(-1				)).sum() / valid_count
													return masked_mse






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						parser.add_argument('''--teacher'''	,			type=_UpperCamelCase )
						parser.add_argument('''--alpha_ce'''	,			default=0.8	,			type=_UpperCamelCase )
						parser.add_argument('''--alpha_mlm'''	,			default=0.2	,			type=_UpperCamelCase )
						parser.add_argument('''--alpha_hid'''	,			default=0.0	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--student'''	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--student_decoder_layers'''	,			default=12	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--student_encoder_layers'''	,			default=12	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--no_teacher'''	,			action='''store_true'''	,			default=_UpperCamelCase )
						parser.add_argument('''--length_penalty'''	,			type=_UpperCamelCase	,			default=-1 )
						parser.add_argument('''--supervise_forward'''	,			action='''store_true'''	,			default=_UpperCamelCase )
						parser.add_argument('''--normalize_hidden'''	,			action='''store_true'''	,			default=_UpperCamelCase )
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """translation"""
							SCREAMING_SNAKE_CASE										=      ["""bleu"""]
							SCREAMING_SNAKE_CASE										=      """bleu"""






							def __init__(						self :     Any		, lowerCamelCase_ :     Any		, **lowerCamelCase_ :     str				):
													super().__init__(lowerCamelCase_		, **lowerCamelCase_				)
													assert hparams.src_lang is not None
													assert hparams.tgt_lang is not None
													UpperCAmelCase__		=    hparams.src_lang
													UpperCAmelCase__		=    hparams.tgt_lang
													if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		, lowerCamelCase_				):
																			UpperCAmelCase__		=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     List[str]		, lowerCamelCase_ :     int				):
													return calculate_bleu(lowerCamelCase_		, lowerCamelCase_				)






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     List[Any]		, lowerCamelCase_ :     str				):
													TranslationModule.add_model_specific_args(lowerCamelCase_		, lowerCamelCase_				)
													add_distill_args(lowerCamelCase_				)
													return parser






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if args.no_teacher:
												UpperCAmelCase__		=    TranslationModule if '''translation''' in args.task else SummarizationModule
						else:  # DISTILL WITH TEACHER
												UpperCAmelCase__		=    TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
						UpperCAmelCase__		=    module_cls.__name__
						print(F"""using module {args.setup_cls}""" )
						UpperCAmelCase__		=    module_cls(_UpperCamelCase )
						return model






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						Path(args.output_dir ).mkdir(exist_ok=_UpperCamelCase )
						check_output_dir(_UpperCamelCase	,			expected_items=3 )

						UpperCAmelCase__		=    create_module(_UpperCamelCase )
						return ft_main(_UpperCamelCase	,			model=_UpperCamelCase )


if __name__ == "__main__":
						__a			 =   argparse.ArgumentParser()
						__a			 =   pl.Trainer.add_argparse_args(parser)
						__a			 =   SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
						__a			 =   parser.parse_args()

						distill_main(args)