


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__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   int					=						['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']




			def __init__(							self ,  snake_case       ):

						'''simple docstring'''


						assert Path(hparams.data_dir       ).exists()
						UpperCamelCase__   =    Path(hparams.output_dir       )
						self.output_dir.mkdir(exist_ok=snake_case       )

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

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

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

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

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

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




			def 			snake_case__      (							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    mask[:, :, None].expand_as(snake_case       )
						UpperCamelCase__   =    s_logits.size(-1       )
						UpperCamelCase__   =    torch.masked_select(snake_case ,  snake_case       )  # (bs * seq_length * voc_size) modulo the 1s in mask
						UpperCamelCase__   =    torch.masked_select(snake_case ,  snake_case       )  # (bs * seq_length * voc_size) modulo the 1s in mask
						UpperCamelCase__   =    s_logits_slct.view(-1 ,  snake_case       )  # (bs * seq_length, voc_size) modulo the 1s in mask
						UpperCamelCase__   =    t_logits_slct.view(-1 ,  snake_case       )  # (bs * seq_length, voc_size) modulo the 1s in mask
						assert t_logits_slct.size() == s_logits_slct.size()
						UpperCamelCase__   =    (
						    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 			snake_case__      (							snake_case ,  snake_case       ):

						'''simple docstring'''


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




			def 			snake_case__      (							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.tokenizer.pad_token_id
						UpperCamelCase__   =    batch["input_ids"], batch["attention_mask"], batch["labels"]
						if isinstance(self.model ,  snake_case       ):
									UpperCamelCase__   =    self.model._shift_right(snake_case       )
						else:
									UpperCamelCase__   =    shift_tokens_right(snake_case ,  snake_case       )

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

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

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

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

						UpperCamelCase__   =    (
						    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 			snake_case__      (							snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


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



def     UpperCamelCase_(			_A				:Union[str, Any]					)->						int:
			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__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Dict					=						'translation'
			_UpperCamelCase						:   int					=						['bleu']
			_UpperCamelCase						:   Any					=						'bleu'




			def __init__(							self ,  snake_case ,  **snake_case       ):

						'''simple docstring'''


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




			def 			snake_case__      (							self ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						return calculate_bleu(snake_case ,  snake_case       )







			@staticmethod
			def 			snake_case__      (							snake_case ,  snake_case       ):

						'''simple docstring'''


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



def     UpperCamelCase_(			_A				:Optional[int]					)->						Tuple:




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

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


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

						distill_main(args)






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


__UpperCamelCase      						=    [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

__UpperCamelCase      						=    get_logger(__name__)


__UpperCamelCase      						=    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}
def     UpperCamelCase_(			_A				:dict					)->						Optional[TaskTemplate]:
			UpperCamelCase__   =    task_template_dict.get("task"					)
			if task_name is None:
						logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_A					)}'''					)
						return None
			UpperCamelCase__   =    NAMEaTEMPLATE.get(_A					)
			return template.from_dict(_A					)


