

"""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  (__A     ):
			lowerCamelCase__				:						str   						=							['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']

			def __init__(   self			:  str      ,			__UpperCAmelCase			:  Union[str, Any]    )				->      Optional[Any]:
						assert Path(hparams.data_dir    ).exists()
						SCREAMING_SNAKE_CASE__									= Path(hparams.output_dir    )
						self.output_dir.mkdir(exist_ok=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.output_dir.joinpath("""student"""    )

						SCREAMING_SNAKE_CASE__									= str(__UpperCAmelCase    )  # Tell lightning we are training the student
						SCREAMING_SNAKE_CASE__									= 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:
									SCREAMING_SNAKE_CASE__									= AutoModelForSeqaSeqLM.from_pretrained(hparams.student    )
									use_task_specific_params(__UpperCAmelCase      ,			hparams.task    )
									SCREAMING_SNAKE_CASE__									= None, None
						else:
									SCREAMING_SNAKE_CASE__									= 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:
									SCREAMING_SNAKE_CASE__									= hparams.length_penalty
						SCREAMING_SNAKE_CASE__									= 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":
									SCREAMING_SNAKE_CASE__									= len(student.get_encoder().block    )
									SCREAMING_SNAKE_CASE__									= len(student.get_decoder().block    )
									SCREAMING_SNAKE_CASE__									= len(teacher.get_encoder().block    )
									SCREAMING_SNAKE_CASE__									= len(teacher.get_decoder().block    )
						else:
									SCREAMING_SNAKE_CASE__									= student.config.encoder_layers
									SCREAMING_SNAKE_CASE__									= student.config.decoder_layers
									SCREAMING_SNAKE_CASE__									= teacher.config.encoder_layers
									SCREAMING_SNAKE_CASE__									= teacher.config.decoder_layers

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

						SCREAMING_SNAKE_CASE__									= 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:
												SCREAMING_SNAKE_CASE__									= get_layers_to_supervise(
												    n_student=len(self.e_layer_ids    )      ,			n_teacher=__UpperCAmelCase    )
												SCREAMING_SNAKE_CASE__									= 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
												SCREAMING_SNAKE_CASE__									= self.e_layer_ids
												SCREAMING_SNAKE_CASE__									= self.d_layer_ids
						else:
									SCREAMING_SNAKE_CASE__									= None
									SCREAMING_SNAKE_CASE__									= None

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  str    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= mask[:, :, None].expand_as(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= s_logits.size(-1    )
						SCREAMING_SNAKE_CASE__									= torch.masked_select(__UpperCAmelCase      ,			__UpperCAmelCase    )  # (bs * seq_length * voc_size) modulo the 1s in mask
						SCREAMING_SNAKE_CASE__									= torch.masked_select(__UpperCAmelCase      ,			__UpperCAmelCase    )  # (bs * seq_length * voc_size) modulo the 1s in mask
						SCREAMING_SNAKE_CASE__									= s_logits_slct.view(-1      ,			__UpperCAmelCase    )  # (bs * seq_length, voc_size) modulo the 1s in mask
						SCREAMING_SNAKE_CASE__									= 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()
						SCREAMING_SNAKE_CASE__									= (
						    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    SCREAMING_SNAKE_CASE			(   __UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Tuple    )				->      List[Any]:
						SummarizationModule.add_model_specific_args(__UpperCAmelCase      ,			__UpperCAmelCase    )
						add_distill_args(__UpperCAmelCase    )
						return parser

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  dict    )				->      tuple:
						SCREAMING_SNAKE_CASE__									= self.tokenizer.pad_token_id
						SCREAMING_SNAKE_CASE__									= batch["input_ids"], batch["attention_mask"], batch["labels"]
						if isinstance(self.model      ,			__UpperCAmelCase    ):
									SCREAMING_SNAKE_CASE__									= self.model._shift_right(__UpperCAmelCase    )
						else:
									SCREAMING_SNAKE_CASE__									= shift_tokens_right(__UpperCAmelCase      ,			__UpperCAmelCase    )

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

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

						SCREAMING_SNAKE_CASE__									= student_outputs[
						    "encoder_last_hidden_state"
						]  # use this unless self.different_base_models
						SCREAMING_SNAKE_CASE__									= zero_tensor(), zero_tensor()
						if self.different_encoder:  # compute encoder hidden state loss
									SCREAMING_SNAKE_CASE__									= self.teacher.get_encoder()(
									    __UpperCAmelCase      ,			attention_mask=__UpperCAmelCase      ,			output_hidden_states=self.do_calc_hidden_loss      ,			)
									if self.different_base_models:
												SCREAMING_SNAKE_CASE__									= all_teacher_encoder_outputs["last_hidden_state"]
									elif self.do_calc_hidden_loss:
												SCREAMING_SNAKE_CASE__									= 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      ,			)

						SCREAMING_SNAKE_CASE__									= 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      ,			)
						SCREAMING_SNAKE_CASE__									= decoder_input_ids.ne(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= self.calc_ce_loss(__UpperCAmelCase      ,			__UpperCAmelCase      ,			teacher_outputs["""logits"""]    )
						if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
									SCREAMING_SNAKE_CASE__									= self.calc_hidden_loss(
									    __UpperCAmelCase      ,			student_outputs["""decoder_hidden_states"""]      ,			teacher_outputs["""decoder_hidden_states"""]      ,			self.d_matches      ,			normalize_hidden=self.hparams.normalize_hidden      ,			)

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

def 		A    (       snake_case__      ):




			'''simple docstring'''


			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 				lowerCamelCase  (__A     ):
			lowerCamelCase__				:						List[Any]   						=							'translation'
			lowerCamelCase__				:						Tuple   						=							['bleu']
			lowerCamelCase__				:						int   						=							'bleu'

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Any      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Tuple    )				->      dict:
						return calculate_bleu(__UpperCAmelCase      ,			__UpperCAmelCase    )





			@staticmethod
			def    SCREAMING_SNAKE_CASE			(   __UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  List[Any]    )				->      int:
						TranslationModule.add_model_specific_args(__UpperCAmelCase      ,			__UpperCAmelCase    )
						add_distill_args(__UpperCAmelCase    )
						return parser

def 		A    (       snake_case__      ):




			'''simple docstring'''


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

def 		A    (       snake_case__      ):




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE__									= create_module(_a      )
			return ft_main(_a						,			model=_a      )


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

						distill_main(args)




"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if start == end:
						return start if lst[start] > item else start + 1
			if start > end:
						return start

			SCREAMING_SNAKE_CASE__									= (start + end) // 2
			if lst[mid] < item:
						return binary_search(snake_case__						,			snake_case__						,			mid + 1						,			snake_case__      )
			elif lst[mid] > item:
						return binary_search(snake_case__						,			snake_case__						,			snake_case__						,			mid - 1      )
			else:
						return mid

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )

			for index in range(1						,			snake_case__      ):
						SCREAMING_SNAKE_CASE__									= lst[index]
						SCREAMING_SNAKE_CASE__									= binary_search(snake_case__						,			snake_case__						,			0						,			index - 1      )
						SCREAMING_SNAKE_CASE__									= lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

			return lst

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if not left:
						return right

			if not right:
						return left

			if left[0] < right[0]:
						return [left[0], *merge(left[1:]						,			snake_case__      )]

			return [right[0], *merge(snake_case__						,			right[1:]      )]

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )
			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= [], []
			SCREAMING_SNAKE_CASE__									= [lst[0]]
			SCREAMING_SNAKE_CASE__									= []
			SCREAMING_SNAKE_CASE__									= 1
			while i < length:
						if lst[i] < lst[i - 1]:
									runs.append(snake_case__      )
									SCREAMING_SNAKE_CASE__									= [lst[i]]
						else:
									new_run.append(lst[i]      )
						i += 1
			runs.append(snake_case__      )

			for run in runs:
						sorted_runs.append(insertion_sort(snake_case__      )      )
			for run in sorted_runs:
						SCREAMING_SNAKE_CASE__									= merge(snake_case__						,			snake_case__      )

			return sorted_array

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
			SCREAMING_SNAKE_CASE__									= tim_sort(snake_case__      )
			print(snake_case__      )


if __name__ == "__main__":
						main()


