
"""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__ ):
				a__:			Tuple									=						['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']




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

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

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

							if hparams.length_penalty != -1:
										lowerCamelCase_ 		=     hparams.length_penalty
							lowerCamelCase_ 		=     hparams.teacher  # Use teacher's tokenizer
							super().__init__(UpperCAmelCase       ,	model=UpperCAmelCase       ,	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_ 		=     len(student.get_encoder().block      )
										lowerCamelCase_ 		=     len(student.get_decoder().block      )
										lowerCamelCase_ 		=     len(teacher.get_encoder().block      )
										lowerCamelCase_ 		=     len(teacher.get_decoder().block      )
							else:
										lowerCamelCase_ 		=     student.config.encoder_layers
										lowerCamelCase_ 		=     student.config.decoder_layers
										lowerCamelCase_ 		=     teacher.config.encoder_layers
										lowerCamelCase_ 		=     teacher.config.decoder_layers

							lowerCamelCase_ 		=     not (hparams.student is None or hparams.teacher == hparams.student)
							lowerCamelCase_ 		=     (not self.different_base_models) and hparams.alpha_hid > 0
							lowerCamelCase_ 		=     self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
							# self.different_encoder determines whether we need to run the teacher encoder
							lowerCamelCase_ 		=     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_ 		=     list(range(UpperCAmelCase      )      )
							if d_layer_ids is None:
										lowerCamelCase_ 		=     list(range(UpperCAmelCase      )      )

							lowerCamelCase_		,   lowerCamelCase_ 		=     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_ 		=     get_layers_to_supervise(
													    n_student=len(self.e_layer_ids      )       ,	n_teacher=UpperCAmelCase      )
													lowerCamelCase_ 		=     get_layers_to_supervise(
													    n_student=len(self.d_layer_ids      )       ,	n_teacher=UpperCAmelCase      )
										else:  # student layer should emulate hidden states of the teacher layer it was copied from
													lowerCamelCase_ 		=     self.e_layer_ids
													lowerCamelCase_ 		=     self.d_layer_ids
							else:
										lowerCamelCase_ 		=     None
										lowerCamelCase_ 		=     None

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




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     mask[:, :, None].expand_as(UpperCAmelCase      )
							lowerCamelCase_ 		=     s_logits.size(-1      )
							lowerCamelCase_ 		=     torch.masked_select(UpperCAmelCase       ,	UpperCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
							lowerCamelCase_ 		=     torch.masked_select(UpperCAmelCase       ,	UpperCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
							lowerCamelCase_ 		=     s_logits_slct.view(-1       ,	UpperCAmelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
							lowerCamelCase_ 		=     t_logits_slct.view(-1       ,	UpperCAmelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
							assert t_logits_slct.size() == s_logits_slct.size()
							lowerCamelCase_ 		=     (
							    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 					UpperCAmelCase__	(							UpperCAmelCase       ,	UpperCAmelCase      ):
							SummarizationModule.add_model_specific_args(UpperCAmelCase       ,	UpperCAmelCase      )
							add_distill_args(UpperCAmelCase      )
							return parser




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

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

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

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

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

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






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




class    __lowerCamelCase     (   SCREAMING_SNAKE_CASE__ ):
				a__:			Optional[int]									=						'translation'
				a__:			Dict									=						['bleu']
				a__:			Union[str, Any]									=						'bleu'




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




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							return calculate_bleu(UpperCAmelCase       ,	UpperCAmelCase      )





				@staticmethod
				def 					UpperCAmelCase__	(							UpperCAmelCase       ,	UpperCAmelCase      ):
							TranslationModule.add_model_specific_args(UpperCAmelCase       ,	UpperCAmelCase      )
							add_distill_args(UpperCAmelCase      )
							return parser






def    lowercase	(  lowerCAmelCase__	):







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






def    lowercase	(  lowerCAmelCase__	):
			Path(args.output_dir	).mkdir(exist_ok=lowerCAmelCase__	)
			check_output_dir(lowerCAmelCase__  ,expected_items=3	)

			lowerCamelCase_ 		=     create_module(lowerCAmelCase__	)
			return ft_main(lowerCAmelCase__  ,model=lowerCAmelCase__	)


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)





"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils




class    __lowerCamelCase     (   unittest.TestCase ):




				@patch('''diffusers.utils.hub_utils.get_full_repo_name'''      )
				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     '''full_repo_name'''
							with TemporaryDirectory() as tmpdir:
										# Dummy args values
										lowerCamelCase_ 		=     Mock()
										lowerCamelCase_ 		=     tmpdir
										lowerCamelCase_ 		=     0
										lowerCamelCase_ 		=     '''hub_token'''
										lowerCamelCase_ 		=     '''dataset_name'''
										lowerCamelCase_ 		=     0.0_1
										lowerCamelCase_ 		=     10_0000
										lowerCamelCase_ 		=     1_0000
										lowerCamelCase_ 		=     0.0_1
										lowerCamelCase_ 		=     0.0_2
										lowerCamelCase_ 		=     0.0_3
										lowerCamelCase_ 		=     0.0_0_0_5
										lowerCamelCase_ 		=     0.0_0_0_0_0_1
										lowerCamelCase_ 		=     1
										lowerCamelCase_ 		=     10
										lowerCamelCase_ 		=     0.0_0_1
										lowerCamelCase_ 		=     0.1
										lowerCamelCase_ 		=     0.2
										lowerCamelCase_ 		=     True

										# Model card mush be rendered and saved
										diffusers.utils.hub_utils.create_model_card(UpperCAmelCase       ,	model_name='''model_name'''      )
										self.assertTrue((Path(UpperCAmelCase      ) / '''README.md''').is_file()      )



