





from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
@require_tf
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      Union[str, Any]			   =							MarianConfig
						__magic_name__:      List[Any]			   =							{}
						__magic_name__:      Any			   =							"gelu"






						def __init__(   self   :   List[Any]		, _A   :   Dict		, _A   :   Dict=13		, _A   :   Dict=7		, _A   :   Optional[Any]=True		, _A   :   Union[str, Any]=False		, _A   :   Union[str, Any]=99		, _A   :   Optional[Any]=32		, _A   :   str=2		, _A   :   Optional[Any]=4		, _A   :   Tuple=37		, _A   :   List[Any]=0.1		, _A   :   List[Any]=0.1		, _A   :   Tuple=20		, _A   :   Optional[Any]=2		, _A   :   Optional[int]=1		, _A   :   Tuple=0		, )				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						parent
							snake_case_		:      Tuple           =						batch_size
							snake_case_		:      int           =						seq_length
							snake_case_		:      Dict           =						is_training
							snake_case_		:      Any           =						use_labels
							snake_case_		:      List[Any]           =						vocab_size
							snake_case_		:      List[Any]           =						hidden_size
							snake_case_		:      Optional[Any]           =						num_hidden_layers
							snake_case_		:      Dict           =						num_attention_heads
							snake_case_		:      str           =						intermediate_size

							snake_case_		:      Optional[int]           =						hidden_dropout_prob
							snake_case_		:      List[str]           =						attention_probs_dropout_prob
							snake_case_		:      List[str]           =						max_position_embeddings
							snake_case_		:      Dict           =						eos_token_id
							snake_case_		:      Optional[int]           =						pad_token_id
							snake_case_		:      List[str]           =						bos_token_id






						def UpperCAmelCase_					(   self   :   Dict						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      int           =						ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size						)
							snake_case_		:      List[str]           =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)		, 1						)
							snake_case_		:      Dict           =						tf.concat([input_ids, eos_tensor]		, axis=1						)

							snake_case_		:      Optional[int]           =						ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size						)

							snake_case_		:      Tuple           =						self.config_cls(
							    vocab_size=self.vocab_size		, d_model=self.hidden_size		, encoder_layers=self.num_hidden_layers		, decoder_layers=self.num_hidden_layers		, encoder_attention_heads=self.num_attention_heads		, decoder_attention_heads=self.num_attention_heads		, encoder_ffn_dim=self.intermediate_size		, decoder_ffn_dim=self.intermediate_size		, dropout=self.hidden_dropout_prob		, attention_dropout=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, eos_token_ids=[2]		, bos_token_id=self.bos_token_id		, pad_token_id=self.pad_token_id		, decoder_start_token_id=self.pad_token_id		, **self.config_updates		, )
							snake_case_		:      Optional[Any]           =						prepare_marian_inputs_dict(_A		, _A		, _A						)
							return config, inputs_dict






						def UpperCAmelCase_					(   self   :   Dict		, _A   :   int		, _A   :   Optional[Any]						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      Dict           =						TFMarianModel(config=_A						).get_decoder()
							snake_case_		:      str           =						inputs_dict['input_ids']

							snake_case_		:      List[str]           =						input_ids[:1, :]
							snake_case_		:      int           =						inputs_dict['attention_mask'][:1, :]
							snake_case_		:      str           =						inputs_dict['head_mask']
							snake_case_		:      Optional[int]           =						1

							# first forward pass
							snake_case_		:      Optional[int]           =						model(_A		, attention_mask=_A		, head_mask=_A		, use_cache=_A						)

							snake_case_    ,snake_case_		:      str           =						outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_		:      Union[str, Any]           =						ids_tensor((self.batch_size, 3)		, config.vocab_size						)
							snake_case_		:      int           =						tf.cast(ids_tensor((self.batch_size, 3)		, 2						)		, tf.inta						)

							# append to next input_ids and
							snake_case_		:      Optional[Any]           =						tf.concat([input_ids, next_tokens]		, axis=-1						)
							snake_case_		:      Optional[int]           =						tf.concat([attention_mask, next_attn_mask]		, axis=-1						)

							snake_case_		:      Optional[Any]           =						model(_A		, attention_mask=_A						)[0]
							snake_case_		:      Union[str, Any]           =						model(_A		, attention_mask=_A		, past_key_values=_A						)[0]

							self.parent.assertEqual(next_tokens.shape[1]		, output_from_past.shape[1]						)

							# select random slice
							snake_case_		:      str           =						int(ids_tensor((1,)		, output_from_past.shape[-1]						)						)
							snake_case_		:      Optional[Any]           =						output_from_no_past[:, -3:, random_slice_idx]
							snake_case_		:      Union[str, Any]           =						output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(_A		, _A		, rtol=1E-3						)


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ,			__a=None ,			__a=None ,			__a=None ,			__a=None ,			__a=None ,			):
	if attention_mask is None:
		snake_case_		:      Optional[Any]           =						tf.cast(tf.math.not_equal(__a ,			config.pad_token_id ) ,			tf.inta )
	if decoder_attention_mask is None:
		snake_case_		:      Dict           =						tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape ,			dtype=tf.inta ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,			config.pad_token_id ) ,			tf.inta ),
		    ] ,			axis=-1 ,			)
	if head_mask is None:
		snake_case_		:      Dict           =						tf.ones((config.encoder_layers, config.encoder_attention_heads) )
	if decoder_head_mask is None:
		snake_case_		:      Optional[int]           =						tf.ones((config.decoder_layers, config.decoder_attention_heads) )
	if cross_attn_head_mask is None:
		snake_case_		:      Tuple           =						tf.ones((config.decoder_layers, config.decoder_attention_heads) )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}
@require_tf
class SCREAMING_SNAKE_CASE_    (       snake_case_							,					snake_case_							,					unittest.TestCase       ):
						__magic_name__:      Dict			   =							(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
						__magic_name__:      Union[str, Any]			   =							(TFMarianMTModel,) if is_tf_available() else ()
						__magic_name__:      int			   =							(
						    {
						        "conversational": TFMarianMTModel,
						        "feature-extraction": TFMarianModel,
						        "summarization": TFMarianMTModel,
						        "text2text-generation": TFMarianMTModel,
						        "translation": TFMarianMTModel,
						    }
						    if is_tf_available()
						    else {}
						)
						__magic_name__:      List[str]			   =							True
						__magic_name__:      Union[str, Any]			   =							False
						__magic_name__:      int			   =							False






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     str:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						TFMarianModelTester(self						)
							snake_case_		:      Optional[Any]           =						ConfigTester(self		, config_class=_A						)






						def UpperCAmelCase_					(   self   :   List[Any]						)				->     List[str]:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      int           =						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_A						)
@require_tf
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):
						__magic_name__:      str			   =							1000  # show more chars for failing integration tests






						@classmethod
						def UpperCAmelCase_					(   cls   :   List[str]						)				->     None:







							"""simple docstring"""


							snake_case_		:      List[str]           =						F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
							return cls






						@cached_property
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     MarianTokenizer:







							"""simple docstring"""


							return AutoTokenizer.from_pretrained(self.model_name						)






						@property
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     int:







							"""simple docstring"""


							return self.tokenizer.eos_token_id






						@cached_property
						def UpperCAmelCase_					(   self   :   str						)				->     Optional[Any]:







							"""simple docstring"""


							warnings.simplefilter('error'						)
							snake_case_		:      TFMarianMTModel           =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
							assert isinstance(_A		, _A						)
							snake_case_		:      List[str]           =						model.config
							self.assertListEqual(c.bad_words_ids		, [[c.pad_token_id]]						)
							self.assertEqual(c.max_length		, 512						)
							self.assertEqual(c.decoder_start_token_id		, c.pad_token_id						)
							return model






						def UpperCAmelCase_					(   self   :   Any		, **_A   :   Tuple						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						self.translate_src_text(**_A						)
							self.assertListEqual(self.expected_text		, _A						)






						def UpperCAmelCase_					(   self   :   List[Any]		, **_A   :   int						)				->     Any:







							"""simple docstring"""


							snake_case_		:      List[str]           =						self.tokenizer(self.src_text		, **_A		, padding=_A		, return_tensors='tf'						)
							snake_case_		:      Optional[int]           =						self.model.generate(
							    model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, num_beams=2		, max_length=128						)
							snake_case_		:      Dict           =						self.tokenizer.batch_decode(generated_ids.numpy()		, skip_special_tokens=_A						)
							return generated_words
@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      str			   =							"mt"
						__magic_name__:      Dict			   =							"en"
						__magic_name__:      Optional[Any]			   =							["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
						__magic_name__:      Optional[int]			   =							["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]






						@unittest.skip('Skipping until #12647 is resolved.'						)
						@slow
						def UpperCAmelCase_					(   self   :   int						)				->     List[Any]:







							"""simple docstring"""


							self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      List[str]			   =							"en"
						__magic_name__:      int			   =							"zh"
						__magic_name__:      List[Any]			   =							["My name is Wolfgang and I live in Berlin"]
						__magic_name__:      int			   =							["我叫沃尔夫冈 我住在柏林"]






						@unittest.skip('Skipping until #12647 is resolved.'						)
						@slow
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     Dict:







							"""simple docstring"""


							self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Any			   =							"en"
						__magic_name__:      Any			   =							"ROMANCE"
						__magic_name__:      List[Any]			   =							[
						    ">>fr<< Don't spend so much time watching TV.",
						    ">>pt<< Your message has been sent.",
						    ">>es<< He's two years older than me.",
						]
						__magic_name__:      List[Any]			   =							[
						    "Ne passez pas autant de temps à regarder la télé.",
						    "A sua mensagem foi enviada.",
						    "Es dos años más viejo que yo.",
						]






						@unittest.skip('Skipping until #12647 is resolved.'						)
						@slow
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     List[str]:







							"""simple docstring"""


							self._assert_generated_batch_equal_expected()






						@unittest.skip('Skipping until #12647 is resolved.'						)
						@slow
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     int:







							"""simple docstring"""


							snake_case_		:      Dict           =						TranslationPipeline(self.model		, self.tokenizer		, framework='tf'						)
							snake_case_		:      List[str]           =						pipeline(self.src_text						)
							self.assertEqual(self.expected_text		, [x['translation_text'] for x in output]						)






'''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      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']





	def __init__(    self						,						A       )     ->     Any:
								assert Path(hparams.data_dir       ).exists()
								UpperCAmelCase	:						Dict					=						Path(hparams.output_dir       )
								self.output_dir.mkdir(exist_ok=A       )

								UpperCAmelCase	:						int					=						self.output_dir.joinpath("""student"""       )

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

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

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

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

								UpperCAmelCase	:						Any					=						nn.KLDivLoss(reduction="""batchmean"""       )
								UpperCAmelCase	:						Tuple					=						2.0
								UpperCAmelCase	:						Tuple					=						hparams.alpha_mlm
								UpperCAmelCase	:						List[str]					=						hparams.alpha_ce
								UpperCAmelCase	:						Dict					=						hparams.alpha_hid
								gc.collect()
								torch.cuda.empty_cache()





	def 					_lowercase(    self						,						A						,						A						,						A       )     ->     Any:
								UpperCAmelCase	:						Union[str, Any]					=						mask[:, :, None].expand_as(A       )
								UpperCAmelCase	:						str					=						s_logits.size(-1       )
								UpperCAmelCase	:						Optional[Any]					=						torch.masked_select(A						,						A       )  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCAmelCase	:						List[str]					=						torch.masked_select(A						,						A       )  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCAmelCase	:						List[str]					=						s_logits_slct.view(-1						,						A       )  # (bs * seq_length, voc_size) modulo the 1s in mask
								UpperCAmelCase	:						Optional[Any]					=						t_logits_slct.view(-1						,						A       )  # (bs * seq_length, voc_size) modulo the 1s in mask
								assert t_logits_slct.size() == s_logits_slct.size()
								UpperCAmelCase	:						int					=						(
								    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(    A						,						A       )     ->     Optional[int]:
								SummarizationModule.add_model_specific_args(A						,						A       )
								add_distill_args(A       )
								return parser





	def 					_lowercase(    self						,						A       )     ->     tuple:
								UpperCAmelCase	:						Union[str, Any]					=						self.tokenizer.pad_token_id
								UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	:						Optional[int]					=						batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
								if isinstance(self.model						,						A       ):
															UpperCAmelCase	:						List[str]					=						self.model._shift_right(A       )
								else:
															UpperCAmelCase	:						str					=						shift_tokens_right(A						,						A       )

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

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

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

								UpperCAmelCase	:						str					=						self.teacher(
								    A						,						attention_mask=A						,						encoder_outputs=(teacher_enc_outputs,)						,						decoder_input_ids=A						,						output_hidden_states=self.do_calc_hidden_loss						,						use_cache=A						,						)
								UpperCAmelCase	:						Union[str, Any]					=						decoder_input_ids.ne(A       )
								UpperCAmelCase	:						Union[str, Any]					=						self.calc_ce_loss(A						,						A						,						teacher_outputs["""logits"""]       )
								if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
															UpperCAmelCase	:						List[Any]					=						self.calc_hidden_loss(
															    A						,						student_outputs["""decoder_hidden_states"""]						,						teacher_outputs["""decoder_hidden_states"""]						,						self.d_matches						,						normalize_hidden=self.hparams.normalize_hidden						,						)

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




def  __lowerCamelCase    (   _lowercase						) ->     Dict:
							# NOTE: if --student argument was specified and the teacher and student base models
							# are different, the models still have to have the same tokenizer, specified by
							# --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
							# from bart to t5. This s because if the tokenizers are different, the output space
							# for the two models is also different and their logits are not comparable.
							parser.add_argument("""--teacher"""      ,							type=_lowercase						)
							parser.add_argument("""--alpha_ce"""      ,							default=0.8      ,							type=_lowercase						)
							parser.add_argument("""--alpha_mlm"""      ,							default=0.2      ,							type=_lowercase						)
							parser.add_argument("""--alpha_hid"""      ,							default=0.0      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--student"""      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--student_decoder_layers"""      ,							default=1_2      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--student_encoder_layers"""      ,							default=1_2      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--no_teacher"""      ,							action="""store_true"""      ,							default=_lowercase						)
							parser.add_argument("""--length_penalty"""      ,							type=_lowercase      ,							default=-1						)
							parser.add_argument("""--supervise_forward"""      ,							action="""store_true"""      ,							default=_lowercase						)
							parser.add_argument("""--normalize_hidden"""      ,							action="""store_true"""      ,							default=_lowercase						)

class      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					'translation'
	lowercase		=					['bleu']
	lowercase		=					'bleu'





	def __init__(    self						,						A						,						**A       )     ->     str:
								super().__init__(A						,						**A       )
								assert hparams.src_lang is not None
								assert hparams.tgt_lang is not None
								UpperCAmelCase	:						Any					=						hparams.src_lang
								UpperCAmelCase	:						Optional[Any]					=						hparams.tgt_lang
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,						A       ):
															UpperCAmelCase	:						Union[str, Any]					=						self.tokenizer.lang_code_to_id[hparams.tgt_lang]





	def 					_lowercase(    self						,						A						,						A       )     ->     dict:
								return calculate_bleu(A						,						A       )






	@staticmethod
	def 					_lowercase(    A						,						A       )     ->     Dict:
								TranslationModule.add_model_specific_args(A						,						A       )
								add_distill_args(A       )
								return parser




def  __lowerCamelCase    (   _lowercase						) ->     Tuple:
							if args.no_teacher:
														UpperCAmelCase	:						Optional[int]					=						TranslationModule if """translation""" in args.task else SummarizationModule
							else:  # DISTILL WITH TEACHER
														UpperCAmelCase	:						List[Any]					=						TranslationDistiller if """translation""" in args.task else SummarizationDistiller
							UpperCAmelCase	:						str					=						module_cls.__name__
							print(F'''using module {args.setup_cls}'''						)
							UpperCAmelCase	:						Optional[Any]					=						module_cls(_lowercase						)
							return model



def  __lowerCamelCase    (   _lowercase						) ->     Optional[int]:
							Path(args.output_dir						).mkdir(exist_ok=_lowercase						)
							check_output_dir(_lowercase      ,							expected_items=3						)

							UpperCAmelCase	:						int					=						create_module(_lowercase						)
							return ft_main(_lowercase      ,							model=_lowercase						)


if __name__ == "__main__":
							a  :     Any       =       argparse.ArgumentParser()
							a  :     Tuple       =       pl.Trainer.add_argparse_args(parser)
							a  :     Optional[Any]       =       SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
							a  :     Any       =       parser.parse_args()

							distill_main(args)

