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






				def __init__(		self   : Optional[int]       ,	a   : List[Any]  ):






							'''simple docstring'''







							assert Path(hparams.data_dir  ).exists()
							lowerCAmelCase__ : int   =						Path(hparams.output_dir  )
							self.output_dir.mkdir(exist_ok=a  )

							lowerCAmelCase__ : Optional[Any]   =						self.output_dir.joinpath('student'  )

							lowerCAmelCase__ : List[Any]   =						str(a  )  # Tell lightning we are training the student
							lowerCAmelCase__ : Dict   =						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:
										lowerCAmelCase__ : Union[str, Any]   =						AutoModelForSeqaSeqLM.from_pretrained(hparams.student  )
										use_task_specific_params(a       ,	hparams.task  )
										lowerCAmelCase__ ,							lowerCAmelCase__ : Any   =						None, None
							else:
										lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[Any]   =						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:
										lowerCAmelCase__ : Optional[Any]   =						hparams.length_penalty
							lowerCAmelCase__ : List[Any]   =						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":
										lowerCAmelCase__ : int   =						len(student.get_encoder().block  )
										lowerCAmelCase__ : Union[str, Any]   =						len(student.get_decoder().block  )
										lowerCAmelCase__ : Optional[int]   =						len(teacher.get_encoder().block  )
										lowerCAmelCase__ : Tuple   =						len(teacher.get_decoder().block  )
							else:
										lowerCAmelCase__ : int   =						student.config.encoder_layers
										lowerCAmelCase__ : List[str]   =						student.config.decoder_layers
										lowerCAmelCase__ : List[str]   =						teacher.config.encoder_layers
										lowerCAmelCase__ : Union[str, Any]   =						teacher.config.decoder_layers

							lowerCAmelCase__ : List[str]   =						not (hparams.student is None or hparams.teacher == hparams.student)
							lowerCAmelCase__ : Tuple   =						(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__ : 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:
										lowerCAmelCase__ : Union[str, Any]   =						list(range(a  )  )
							if d_layer_ids is None:
										lowerCAmelCase__ : Union[str, Any]   =						list(range(a  )  )

							lowerCAmelCase__ ,							lowerCAmelCase__ : List[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:
													lowerCAmelCase__ : Dict   =						get_layers_to_supervise(
													    n_student=len(self.e_layer_ids  )       ,	n_teacher=a  )
													lowerCAmelCase__ : Any   =						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
													lowerCAmelCase__ : Optional[Any]   =						self.e_layer_ids
													lowerCAmelCase__ : Optional[int]   =						self.d_layer_ids
							else:
										lowerCAmelCase__ : Optional[Any]   =						None
										lowerCAmelCase__ : List[str]   =						None

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






				def 				_lowerCamelCase						(		self   : Tuple       ,	a   : Tuple       ,	a   : List[str]       ,	a   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						mask[:, :, None].expand_as(a  )
							lowerCAmelCase__ : Any   =						s_logits.size(-1  )
							lowerCAmelCase__ : Any   =						torch.masked_select(a       ,	a  )  # (bs * seq_length * voc_size) modulo the 1s in mask
							lowerCAmelCase__ : Dict   =						torch.masked_select(a       ,	a  )  # (bs * seq_length * voc_size) modulo the 1s in mask
							lowerCAmelCase__ : List[str]   =						s_logits_slct.view(-1       ,	a  )  # (bs * seq_length, voc_size) modulo the 1s in mask
							lowerCAmelCase__ : List[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()
							lowerCAmelCase__ : str   =						(
							    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						(		a   : Optional[int]       ,	a   : List[Any]  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : str       ,	a   : dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						self.tokenizer.pad_token_id
							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : List[str]   =						batch['input_ids'], batch['attention_mask'], batch['labels']
							if isinstance(self.model       ,	a  ):
										lowerCAmelCase__ : int   =						self.model._shift_right(a  )
							else:
										lowerCAmelCase__ : Optional[int]   =						shift_tokens_right(a       ,	a  )

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

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

							lowerCAmelCase__ : Optional[int]   =						student_outputs[
							    'encoder_last_hidden_state'
							]  # use this unless self.different_base_models
							lowerCAmelCase__ ,							lowerCAmelCase__ : int   =						zero_tensor(), zero_tensor()
							if self.different_encoder:  # compute encoder hidden state loss
										lowerCAmelCase__ : List[str]   =						self.teacher.get_encoder()(
										    a       ,	attention_mask=a       ,	output_hidden_states=self.do_calc_hidden_loss       ,	)
										if self.different_base_models:
													lowerCAmelCase__ : Optional[Any]   =						all_teacher_encoder_outputs['last_hidden_state']
										elif self.do_calc_hidden_loss:
													lowerCAmelCase__ : Union[str, Any]   =						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       ,	)

							lowerCAmelCase__ : Any   =						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       ,	)
							lowerCAmelCase__ : Optional[int]   =						decoder_input_ids.ne(a  )
							lowerCAmelCase__ : List[Any]   =						self.calc_ce_loss(a       ,	a       ,	teacher_outputs['logits']  )
							if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
										lowerCAmelCase__ : List[str]   =						self.calc_hidden_loss(
										    a       ,	student_outputs['decoder_hidden_states']       ,	teacher_outputs['decoder_hidden_states']       ,	self.d_matches       ,	normalize_hidden=self.hparams.normalize_hidden       ,	)

							lowerCAmelCase__ : Optional[Any]   =						(
							    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						(		a   : int       ,	a   : Union[str, Any]       ,	a   : Union[str, Any]       ,	a   : Optional[int]       ,	a   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : 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}'''
							lowerCAmelCase__ : Union[str, Any]   =						attention_mask.to(hidden_states[0]  )
							lowerCAmelCase__ : Optional[int]   =						mask.sum() * hidden_states[0].size(-1  )
							lowerCAmelCase__ : int   =						torch.stack([hidden_states[i] for i in range(len(a  )  )]  )
							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__ : Optional[Any]   =						nn.functional.layer_norm(a       ,	student_states.shape[1:]  )
										lowerCAmelCase__ : Tuple   =						nn.functional.layer_norm(a       ,	teacher_states.shape[1:]  )
							lowerCAmelCase__ : List[str]   =						nn.functional.mse_loss(a       ,	a       ,	reduction='none'  )
							lowerCAmelCase__ : int   =						(mse * mask.unsqueeze(0  ).unsqueeze(-1  )).sum() / valid_count
							return masked_mse






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







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






				def __init__(		self   : str       ,	a   : Union[str, Any]       ,	**a   : Optional[int]  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Dict       ,	a   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







							return calculate_bleu(a       ,	a  )






				@staticmethod
				def 				_lowerCamelCase						(		a   : List[Any]       ,	a   : Any  ):






							'''simple docstring'''







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






def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Dict:
			if args.no_teacher:
						lowerCAmelCase__ : List[str]   =						TranslationModule if 'translation' in args.task else SummarizationModule
			else:  # DISTILL WITH TEACHER
						lowerCAmelCase__ : Any   =						TranslationDistiller if 'translation' in args.task else SummarizationDistiller
			lowerCAmelCase__ : str   =						module_cls.__name__
			print(F'''using module {args.setup_cls}'''	)
			lowerCAmelCase__ : Tuple   =						module_cls(SCREAMING_SNAKE_CASE_	)
			return model
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Union[str, Any]:
			Path(args.output_dir	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
			check_output_dir(SCREAMING_SNAKE_CASE_			,				expected_items=3	)

			lowerCAmelCase__ : int   =						create_module(SCREAMING_SNAKE_CASE_	)
			return ft_main(SCREAMING_SNAKE_CASE_			,				model=SCREAMING_SNAKE_CASE_	)


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

					distill_main(args)

lowerCamelCase__        =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	A__				:






				def __init__(		self   : Optional[int]       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						name
							lowerCAmelCase__ : List[Any]   =						[]
							lowerCAmelCase__ : int   =						[]






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Union[str, Any]  ):






							'''simple docstring'''







							self.inbound.append(a  )






				def 				_lowerCamelCase						(		self   : Dict       ,	a   : Union[str, Any]  ):






							'''simple docstring'''







							self.outbound.append(a  )






				def __repr__(		self   : Tuple  ):






							'''simple docstring'''







							return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=3			,				SCREAMING_SNAKE_CASE_=0.85	)							->						Optional[Any]:
			lowerCAmelCase__ : Tuple   =						{}
			for node in nodes:
						lowerCAmelCase__ : Union[str, Any]   =						1

			lowerCAmelCase__ : List[str]   =						{}
			for node in nodes:
						lowerCAmelCase__ : str   =						len(node.outbound	)

			for i in range(SCREAMING_SNAKE_CASE_	):
						print(F'''======= Iteration {i + 1} ======='''	)
						for _, node in enumerate(SCREAMING_SNAKE_CASE_	):
									lowerCAmelCase__ : Optional[int]   =						(1 - d) + d * sum(
									    ranks[ib] / outbounds[ib] for ib in node.inbound	)
						print(SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    )							->						Dict:
			lowerCAmelCase__ : List[Any]   =						list(input('Enter Names of the Nodes: '	).split()	)

			lowerCAmelCase__ : Union[str, Any]   =						[Node(SCREAMING_SNAKE_CASE_	) for name in names]

			for ri, row in enumerate(SCREAMING_SNAKE_CASE_	):
						for ci, col in enumerate(SCREAMING_SNAKE_CASE_	):
									if col == 1:
												nodes[ci].add_inbound(names[ri]	)
												nodes[ri].add_outbound(names[ci]	)

			print('======= Nodes ======='	)
			for node in nodes:
						print(SCREAMING_SNAKE_CASE_	)

			page_rank(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					main()