




import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
							from transformers.models.dpr.modeling_dpr import (
							    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							)


class        __SCREAMING_SNAKE_CASE							:

					def __init__(       self				, __lowercase				, __lowercase=13				, __lowercase=7				, __lowercase=False				, __lowercase=True				, __lowercase=True				, __lowercase=True				, __lowercase=99				, __lowercase=32				, __lowercase=5				, __lowercase=4				, __lowercase=37				, __lowercase="gelu"				, __lowercase=0.1				, __lowercase=0.1				, __lowercase=512				, __lowercase=16				, __lowercase=2				, __lowercase=0.02				, __lowercase=3				, __lowercase=4				, __lowercase=None				, __lowercase=0				, ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							parent
						lowerCamelCase_							:	List[Any]							     =							batch_size
						lowerCamelCase_							:	int							     =							seq_length
						lowerCamelCase_							:	Any							     =							is_training
						lowerCamelCase_							:	Optional[Any]							     =							use_input_mask
						lowerCamelCase_							:	List[str]							     =							use_token_type_ids
						lowerCamelCase_							:	List[Any]							     =							use_labels
						lowerCamelCase_							:	Dict							     =							vocab_size
						lowerCamelCase_							:	Tuple							     =							hidden_size
						lowerCamelCase_							:	List[Any]							     =							num_hidden_layers
						lowerCamelCase_							:	Any							     =							num_attention_heads
						lowerCamelCase_							:	Optional[int]							     =							intermediate_size
						lowerCamelCase_							:	str							     =							hidden_act
						lowerCamelCase_							:	Optional[Any]							     =							hidden_dropout_prob
						lowerCamelCase_							:	Tuple							     =							attention_probs_dropout_prob
						lowerCamelCase_							:	Union[str, Any]							     =							max_position_embeddings
						lowerCamelCase_							:	int							     =							type_vocab_size
						lowerCamelCase_							:	Any							     =							type_sequence_label_size
						lowerCamelCase_							:	Optional[Any]							     =							initializer_range
						lowerCamelCase_							:	Optional[int]							     =							num_labels
						lowerCamelCase_							:	Dict							     =							num_choices
						lowerCamelCase_							:	int							     =							scope
						lowerCamelCase_							:	Optional[int]							     =							projection_dim

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size      )

						lowerCamelCase_							:	Tuple							     =							None
						if self.use_input_mask:
							lowerCamelCase_							:	str							     =							random_attention_mask([self.batch_size, self.seq_length]      )

						lowerCamelCase_							:	Dict							     =							None
						if self.use_token_type_ids:
							lowerCamelCase_							:	Optional[int]							     =							ids_tensor([self.batch_size, self.seq_length]				, self.type_vocab_size      )

						lowerCamelCase_							:	Any							     =							None
						lowerCamelCase_							:	Optional[Any]							     =							None
						lowerCamelCase_							:	int							     =							None
						if self.use_labels:
							lowerCamelCase_							:	List[Any]							     =							ids_tensor([self.batch_size]				, self.type_sequence_label_size      )
							lowerCamelCase_							:	Dict							     =							ids_tensor([self.batch_size, self.seq_length]				, self.num_labels      )
							lowerCamelCase_							:	List[Any]							     =							ids_tensor([self.batch_size]				, self.num_choices      )

						lowerCamelCase_							:	Tuple							     =							self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return DPRConfig(
						    projection_dim=self.projection_dim				, vocab_size=self.vocab_size				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, type_vocab_size=self.type_vocab_size				, initializer_range=self.initializer_range				, )

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[int]							     =							DPRContextEncoder(config=__lowercase      )
						model.to(__lowercase      )
						model.eval()
						lowerCamelCase_							:	int							     =							model(__lowercase				, attention_mask=__lowercase				, token_type_ids=__lowercase      )
						lowerCamelCase_							:	Optional[int]							     =							model(__lowercase				, token_type_ids=__lowercase      )
						lowerCamelCase_							:	Dict							     =							model(__lowercase      )
						self.parent.assertEqual(result.pooler_output.shape				, (self.batch_size, self.projection_dim or self.hidden_size)      )

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							DPRQuestionEncoder(config=__lowercase      )
						model.to(__lowercase      )
						model.eval()
						lowerCamelCase_							:	int							     =							model(__lowercase				, attention_mask=__lowercase				, token_type_ids=__lowercase      )
						lowerCamelCase_							:	Optional[Any]							     =							model(__lowercase				, token_type_ids=__lowercase      )
						lowerCamelCase_							:	Dict							     =							model(__lowercase      )
						self.parent.assertEqual(result.pooler_output.shape				, (self.batch_size, self.projection_dim or self.hidden_size)      )

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							DPRReader(config=__lowercase      )
						model.to(__lowercase      )
						model.eval()
						lowerCamelCase_							:	Union[str, Any]							     =							model(
						    __lowercase				, attention_mask=__lowercase				, )

						self.parent.assertEqual(result.start_logits.shape				, (self.batch_size, self.seq_length)      )
						self.parent.assertEqual(result.end_logits.shape				, (self.batch_size, self.seq_length)      )
						self.parent.assertEqual(result.relevance_logits.shape				, (self.batch_size,)      )





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.prepare_config_and_inputs()
						(
						    lowerCamelCase_    
						)							:	Optional[Any]							     =							config_and_inputs
						lowerCamelCase_							:	Tuple							     =							{'input_ids': input_ids}
						return config, inputs_dict


@require_torch
class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase     ,  lowerCamelCase     ,  unittest.TestCase						):
					_lowerCamelCase							=				(
					    (
					        DPRContextEncoder,
					        DPRQuestionEncoder,
					        DPRReader,
					    )
					    if is_torch_available()
					    else ()
					)
					_lowerCamelCase							=				{"""feature-extraction""": DPRQuestionEncoder} if is_torch_available() else {}

					_lowerCamelCase							=				False
					_lowerCamelCase							=				False  # why?
					_lowerCamelCase							=				False
					_lowerCamelCase							=				False

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							DPRModelTester(self      )
						lowerCamelCase_							:	Dict							     =							ConfigTester(self				, config_class=__lowercase				, hidden_size=37      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self.config_tester.run_common_tests()

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_context_encoder(*__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_question_encoder(*__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_reader(*__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.model_tester.prepare_config_and_inputs()[0]

						lowerCamelCase_							:	Tuple							     =							DPRQuestionEncoder(config=__lowercase      )
						model.to(__lowercase      )
						model.eval()

						with tempfile.TemporaryDirectory() as tmp_dirname:
							model.save_pretrained(__lowercase      )
							lowerCamelCase_							:	Optional[int]							     =							DPRQuestionEncoder.from_pretrained(__lowercase				, projection_dim=512      )

						self.assertIsNotNone(__lowercase      )





					@slow
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCamelCase_							:	Optional[Any]							     =							DPRContextEncoder.from_pretrained(__lowercase      )
							self.assertIsNotNone(__lowercase      )

						for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCamelCase_							:	Optional[Any]							     =							DPRContextEncoder.from_pretrained(__lowercase      )
							self.assertIsNotNone(__lowercase      )

						for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCamelCase_							:	Optional[Any]							     =							DPRQuestionEncoder.from_pretrained(__lowercase      )
							self.assertIsNotNone(__lowercase      )

						for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCamelCase_							:	Optional[Any]							     =							DPRReader.from_pretrained(__lowercase      )
							self.assertIsNotNone(__lowercase      )


@require_torch
class        __SCREAMING_SNAKE_CASE							(	unittest.TestCase						):

					@slow
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'				, return_dict=__lowercase      )
						model.to(__lowercase      )

						lowerCamelCase_							:	Tuple							     =							torch.tensor(
						    [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]				, dtype=torch.long				, device=__lowercase      )  # [CLS] hello, is my dog cute? [SEP]
						lowerCamelCase_							:	str							     =							model(__lowercase      )[0]  # embedding shape = (1, 768)
						# compare the actual values for a slice.
						lowerCamelCase_							:	List[Any]							     =							torch.tensor(
						    [
						        [
						            0.03_23_62_53,
						            0.12_75_33_35,
						            0.16_81_85_09,
						            0.00_27_97_86,
						            0.3_89_69_33,
						            0.24_26_49_45,
						            0.2_17_89_71,
						            -0.02_33_52_27,
						            -0.08_48_19_59,
						            -0.14_32_41_17,
						        ]
						    ]				, dtype=torch.float				, device=__lowercase				, )
						self.assertTrue(torch.allclose(output[:, :10]				, __lowercase				, atol=1E-4      )      )





					@slow
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base'      )
						lowerCamelCase_							:	List[str]							     =							DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base'      )
						model.to(__lowercase      )

						lowerCamelCase_							:	Any							     =							tokenizer(
						    questions='What is love ?'				, titles='Haddaway'				, texts='What Is Love is a song recorded by the artist Haddaway'				, padding=__lowercase				, return_tensors='pt'				, )
						encoded_inputs.to(__lowercase      )

						lowerCamelCase_							:	int							     =							model(**__lowercase      )

						# compare the actual values for a slice.
						lowerCamelCase_							:	List[Any]							     =							torch.tensor(
						    [[-10.30_05, -10.77_65, -11.48_72, -11.68_41, -11.93_12, -10.30_02, -9.85_44, -11.73_78, -12.08_21, -10.29_75]]				, dtype=torch.float				, device=__lowercase				, )

						lowerCamelCase_							:	Tuple							     =							torch.tensor(
						    [[-11.06_84, -11.70_41, -11.53_97, -10.34_65, -10.87_91, -6.84_43, -11.99_59, -11.03_64, -10.00_96, -6.84_05]]				, dtype=torch.float				, device=__lowercase				, )
						self.assertTrue(torch.allclose(outputs.start_logits[:, :10]				, __lowercase				, atol=1E-4      )      )
						self.assertTrue(torch.allclose(outputs.end_logits[:, :10]				, __lowercase				, atol=1E-4      )      )






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        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]

					def __init__(       self				, __lowercase      ):







						'''simple docstring'''




						assert Path(hparams.data_dir      ).exists()
						lowerCamelCase_							:	str							     =							Path(hparams.output_dir      )
						self.output_dir.mkdir(exist_ok=__lowercase      )

						lowerCamelCase_							:	Union[str, Any]							     =							self.output_dir.joinpath('student'      )

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

						if hparams.length_penalty != -1:
							lowerCamelCase_							:	Tuple							     =							hparams.length_penalty
						lowerCamelCase_							:	List[Any]							     =							hparams.teacher  # Use teacher's tokenizer
						super().__init__(__lowercase				, model=__lowercase				, 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":
							lowerCamelCase_							:	int							     =							len(student.get_encoder().block      )
							lowerCamelCase_							:	str							     =							len(student.get_decoder().block      )
							lowerCamelCase_							:	List[str]							     =							len(teacher.get_encoder().block      )
							lowerCamelCase_							:	Any							     =							len(teacher.get_decoder().block      )
						else:
							lowerCamelCase_							:	Union[str, Any]							     =							student.config.encoder_layers
							lowerCamelCase_							:	Optional[Any]							     =							student.config.decoder_layers
							lowerCamelCase_							:	str							     =							teacher.config.encoder_layers
							lowerCamelCase_							:	Tuple							     =							teacher.config.decoder_layers

						lowerCamelCase_							:	Tuple							     =							not (hparams.student is None or hparams.teacher == hparams.student)
						lowerCamelCase_							:	int							     =							(not self.different_base_models) and hparams.alpha_hid > 0
						lowerCamelCase_							:	Tuple							     =							self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
						# self.different_encoder determines whether we need to run the teacher encoder
						lowerCamelCase_							:	List[Any]							     =							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:
							lowerCamelCase_							:	Optional[Any]							     =							list(range(__lowercase      )      )
						if d_layer_ids is None:
							lowerCamelCase_							:	str							     =							list(range(__lowercase      )      )

						lowerCamelCase_     ,    lowerCamelCase_							:	int							     =							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:
								lowerCamelCase_							:	int							     =							get_layers_to_supervise(
								    n_student=len(self.e_layer_ids      )				, n_teacher=__lowercase      )
								lowerCamelCase_							:	str							     =							get_layers_to_supervise(
								    n_student=len(self.d_layer_ids      )				, n_teacher=__lowercase      )
							else:  # student layer should emulate hidden states of the teacher layer it was copied from
								lowerCamelCase_							:	Optional[Any]							     =							self.e_layer_ids
								lowerCamelCase_							:	Tuple							     =							self.d_layer_ids
						else:
							lowerCamelCase_							:	str							     =							None
							lowerCamelCase_							:	List[Any]							     =							None

						lowerCamelCase_							:	Dict							     =							nn.KLDivLoss(reduction='batchmean'      )
						lowerCamelCase_							:	int							     =							2.0
						lowerCamelCase_							:	Union[str, Any]							     =							hparams.alpha_mlm
						lowerCamelCase_							:	List[Any]							     =							hparams.alpha_ce
						lowerCamelCase_							:	Optional[int]							     =							hparams.alpha_hid
						gc.collect()
						torch.cuda.empty_cache()

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							mask[:, :, None].expand_as(__lowercase      )
						lowerCamelCase_							:	Tuple							     =							s_logits.size(-1      )
						lowerCamelCase_							:	List[Any]							     =							torch.masked_select(__lowercase				, __lowercase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
						lowerCamelCase_							:	Tuple							     =							torch.masked_select(__lowercase				, __lowercase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
						lowerCamelCase_							:	int							     =							s_logits_slct.view(-1				, __lowercase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
						lowerCamelCase_							:	Any							     =							t_logits_slct.view(-1				, __lowercase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
						assert t_logits_slct.size() == s_logits_slct.size()
						lowerCamelCase_							:	Tuple							     =							(
						    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 			lowercase_							(       __lowercase				, __lowercase      ):







						'''simple docstring'''




						SummarizationModule.add_model_specific_args(__lowercase				, __lowercase      )
						add_distill_args(__lowercase      )
						return parser

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							self.tokenizer.pad_token_id
						lowerCamelCase_     ,    lowerCamelCase_     ,    lowerCamelCase_							:	List[Any]							     =							batch['input_ids'], batch['attention_mask'], batch['labels']
						if isinstance(self.model				, __lowercase      ):
							lowerCamelCase_							:	Optional[Any]							     =							self.model._shift_right(__lowercase      )
						else:
							lowerCamelCase_							:	Optional[Any]							     =							shift_tokens_right(__lowercase				, __lowercase      )

						# noinspection PyCallingNonCallable
						lowerCamelCase_							:	Optional[int]							     =							self(
						    __lowercase				, attention_mask=__lowercase				, decoder_input_ids=__lowercase				, output_hidden_states=self.do_calc_hidden_loss				, output_attentions=__lowercase				, use_cache=__lowercase				, )
						lowerCamelCase_							:	List[Any]							     =							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
							lowerCamelCase_							:	int							     =							nn.CrossEntropyLoss(ignore_index=__lowercase      )
							lowerCamelCase_							:	List[Any]							     =							loss_fct(lm_logits.view(-1				, lm_logits.shape[-1]      )				, labels.view(-1      )      )
						else:
							lowerCamelCase_							:	List[Any]							     =							nn.functional.log_softmax(__lowercase				, dim=-1      )
							lowerCamelCase_     ,    lowerCamelCase_							:	Union[str, Any]							     =							label_smoothed_nll_loss(
							    __lowercase				, __lowercase				, self.hparams.label_smoothing				, ignore_index=__lowercase      )

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

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

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

						lowerCamelCase_							:	Optional[int]							     =							(
						    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 			lowercase_							(       __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase      ):







						'''simple docstring'''




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






def       snake_case      (							A    )							->							Optional[int]:




	'''simple docstring'''


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


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				"""translation"""
					_lowerCamelCase							=				["""bleu"""]
					_lowerCamelCase							=				"""bleu"""

					def __init__(       self				, __lowercase				, **__lowercase      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						return calculate_bleu(__lowercase				, __lowercase      )





					@staticmethod
					def 			lowercase_							(       __lowercase				, __lowercase      ):







						'''simple docstring'''




						TranslationModule.add_model_specific_args(__lowercase				, __lowercase      )
						add_distill_args(__lowercase      )
						return parser






def       snake_case      (							A    )							->							str:




	'''simple docstring'''


	if args.no_teacher:
		lowerCamelCase_							:	Optional[Any]							     =							TranslationModule if 'translation' in args.task else SummarizationModule
	else:  # DISTILL WITH TEACHER
		lowerCamelCase_							:	Dict							     =							TranslationDistiller if 'translation' in args.task else SummarizationDistiller
	lowerCamelCase_							:	str							     =							module_cls.__name__
	print(f"using module {args.setup_cls}"    )
	lowerCamelCase_							:	Dict							     =							module_cls(A    )
	return model






def       snake_case      (							A    )							->							str:




	'''simple docstring'''


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

	lowerCamelCase_							:	str							     =							create_module(A    )
	return ft_main(A		,     model=A    )


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

							distill_main(args)
