



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 (     __a	):
				_lowercase      		=['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']




				def __init__( self    ,    _UpperCamelCase					)    ->				Dict:
							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_      =     None, None
							else:
										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_      =     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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Tuple:
							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 					__a						( _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							SummarizationModule.add_model_specific_args(_UpperCamelCase    ,    _UpperCamelCase					)
							add_distill_args(_UpperCamelCase					)
							return parser




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Optional[Any]:
							lowerCAmelCase_      =     self.tokenizer.pad_token_id
							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_      =     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_      =     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 					__a						( _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Any:
							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        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]    ):




			"""simple docstring"""


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





class      _lowerCAmelCase (     __a	):
				_lowercase      		='translation'
				_lowercase      		=['bleu']
				_lowercase      		='bleu'




				def __init__( self    ,    _UpperCamelCase    ,    **_UpperCamelCase					)    ->				int:
							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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[int]:
							return calculate_bleu(_UpperCamelCase    ,    _UpperCamelCase					)




				@staticmethod
				def 					__a						( _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							TranslationModule.add_model_specific_args(_UpperCamelCase    ,    _UpperCamelCase					)
							add_distill_args(_UpperCamelCase					)
							return parser






def        lowerCamelCase__    (    __lowerCAmelCase  :  int    ):




			"""simple docstring"""


			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(_UpperCamelCase    )
			return model






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


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

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


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

			distill_main(args)





import math





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase					)    ->				Dict:
							lowerCAmelCase_      =     len(_UpperCamelCase					)
							lowerCAmelCase_      =     [0] * (
							    4 * self.N
							)  # approximate the overall size of segment tree with array N
							self.build(1    ,    0    ,    self.N - 1					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[str]:
							return idx * 2




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							return idx * 2 + 1




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:  # noqa: E741
							if l == r:
										lowerCAmelCase_      =     A[l]
							else:
										lowerCAmelCase_      =     (l + r) // 2
										self.build(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase					)
										self.build(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:
							return self.update_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:  # noqa: E741
							if r < a or l > b:
										return True
							if l == r:
										lowerCAmelCase_      =     val
										return True
							lowerCAmelCase_      =     (l + r) // 2
							self.update_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							self.update_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)
							return True




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[Any]:
							return self.query_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Any:  # noqa: E741
							if r < a or l > b:
										return -math.inf
							if l >= a and r <= b:
										return self.st[idx]
							lowerCAmelCase_      =     (l + r) // 2
							lowerCAmelCase_      =     self.query_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     self.query_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							return max(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     []
							for i in range(1    ,    N + 1					):
										show_list += [self.query(_UpperCamelCase    ,    _UpperCamelCase					)]
							print(_UpperCamelCase					)


if __name__ == "__main__":
			_A       =					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			_A       =					15
			_A       =					SegmentTree(A)
			print(segt.query(4, 6))
			print(segt.query(7, 11))
			print(segt.query(7, 12))
			segt.update(1, 3, 1_11)
			print(segt.query(1, 15))
			segt.update(7, 8, 2_35)
			segt.show_data()



