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 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]




					def __init__(  self       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									assert Path(hparams.data_dir						).exists()
									snake_case__   :      List[str]   				= Path(hparams.output_dir						)
									self.output_dir.mkdir(exist_ok=__UpperCamelCase						)

									snake_case__   :      List[str]   				= self.output_dir.joinpath('student'						)

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

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

									snake_case__				,    snake_case__   :      List[Any]   				= 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:
																	snake_case__   :      int   				= get_layers_to_supervise(
																	    n_student=len(self.e_layer_ids						)       ,			n_teacher=__UpperCamelCase						)
																	snake_case__   :      Tuple   				= 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
																	snake_case__   :      Any   				= self.e_layer_ids
																	snake_case__   :      str   				= self.d_layer_ids
									else:
													snake_case__   :      Optional[Any]   				= None
													snake_case__   :      str   				= None

									snake_case__   :      int   				= nn.KLDivLoss(reduction='batchmean'						)
									snake_case__   :      Any   				= 2.0
									snake_case__   :      Any   				= hparams.alpha_mlm
									snake_case__   :      int   				= hparams.alpha_ce
									snake_case__   :      Optional[Any]   				= hparams.alpha_hid
									gc.collect()
									torch.cuda.empty_cache()




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







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



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase						)	->    tuple:



									'''simple docstring'''







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

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

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

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

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

									snake_case__   :      str   				= (
									    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						)	->    Tuple:



									'''simple docstring'''







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




def 					UpperCamelCase__		(							A__       )  ->   Tuple:
				# 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=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 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """translation"""
					__lowerCamelCase			   =  ["""bleu"""]
					__lowerCamelCase			   =  """bleu"""




					def __init__(  self       ,			__UpperCamelCase       ,			**__UpperCamelCase						)	->    int:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    dict:



									'''simple docstring'''







									return calculate_bleu(__UpperCamelCase       ,			__UpperCamelCase						)




					@staticmethod
					def      __a     (  __UpperCamelCase       ,			__UpperCamelCase						)	->    Union[str, Any]:



									'''simple docstring'''







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




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

				snake_case__   :      List[Any]   				= create_module(A__       )
				return ft_main(A__     ,					model=A__       )


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

				distill_main(args)



import math





class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= len(__UpperCamelCase						)
									snake_case__   :      Dict   				= [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						)	->    Any:



									'''simple docstring'''







									return idx * 2




					def      __a     (  self       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									return idx * 2 + 1




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    int:  # noqa: E741



									'''simple docstring'''







									if l == r:
													snake_case__   :      Dict   				= A[l]
									else:
													snake_case__   :      Optional[Any]   				= (l + r) // 2
													self.build(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase						)
													self.build(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase						)
													snake_case__   :      int   				= max(self.st[self.left(__UpperCamelCase						)]       ,			self.st[self.right(__UpperCamelCase						)]						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									return self.update_recursive(1       ,			0       ,			self.N - 1       ,			a - 1       ,			b - 1       ,			__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Tuple:  # noqa: E741



									'''simple docstring'''







									if r < a or l > b:
													return True
									if l == r:
													snake_case__   :      List[str]   				= val
													return True
									snake_case__   :      str   				= (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						)
									snake_case__   :      Tuple   				= max(self.st[self.left(__UpperCamelCase						)]       ,			self.st[self.right(__UpperCamelCase						)]						)
									return True




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									return self.query_recursive(1       ,			0       ,			self.N - 1       ,			a - 1       ,			b - 1						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:  # noqa: E741



									'''simple docstring'''







									if r < a or l > b:
													return -math.inf
									if l >= a and r <= b:
													return self.st[idx]
									snake_case__   :      str   				= (l + r) // 2
									snake_case__   :      Optional[Any]   				= self.query_recursive(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      List[str]   				= self.query_recursive(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									return max(__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= []
									for i in range(1       ,			N + 1						):
													show_list += [self.query(__UpperCamelCase       ,			__UpperCamelCase						)]
									print(__UpperCamelCase						)


if __name__ == "__main__":
				lowerCAmelCase__	:  Optional[int]						=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
				lowerCAmelCase__	:  Any						=  15
				lowerCAmelCase__	:  List[Any]						=  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()


