





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_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']


    def __init__(		self			,		A	)		->       Optional[Any]:
        assert Path(hparams.data_dir	).exists()
        _SCREAMING_SNAKE_CASE     =      Path(hparams.output_dir	)
        self.output_dir.mkdir(exist_ok=A	)

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

        _SCREAMING_SNAKE_CASE     =      str(A	)  # Tell lightning we are training the student
        _SCREAMING_SNAKE_CASE     =      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:
            _SCREAMING_SNAKE_CASE     =      AutoModelForSeqaSeqLM.from_pretrained(hparams.student	)
            use_task_specific_params(A			,		hparams.task	)
            _SCREAMING_SNAKE_CASE     =      None, None
        else:
            _SCREAMING_SNAKE_CASE     =      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:
            _SCREAMING_SNAKE_CASE     =      hparams.length_penalty
        _SCREAMING_SNAKE_CASE     =      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":
            _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(A	)	)
        if d_layer_ids is None:
            _SCREAMING_SNAKE_CASE     =      list(range(A	)	)

        _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=A	)
                _SCREAMING_SNAKE_CASE     =      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
                _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       snake_case_(		self			,		A			,		A			,		A	)		->       Any:
        _SCREAMING_SNAKE_CASE     =      mask[:, :, None].expand_as(A	)
        _SCREAMING_SNAKE_CASE     =      s_logits.size(-1	)
        _SCREAMING_SNAKE_CASE     =      torch.masked_select(A			,		A	)  # (bs * seq_length * voc_size) modulo the 1s in mask
        _SCREAMING_SNAKE_CASE     =      torch.masked_select(A			,		A	)  # (bs * seq_length * voc_size) modulo the 1s in mask
        _SCREAMING_SNAKE_CASE     =      s_logits_slct.view(-1			,		A	)  # (bs * seq_length, voc_size) modulo the 1s in mask
        _SCREAMING_SNAKE_CASE     =      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()
        _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       snake_case_(		A			,		A	)		->       str:
        SummarizationModule.add_model_specific_args(A			,		A	)
        add_distill_args(A	)
        return parser


    def       snake_case_(		self			,		A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.tokenizer.pad_token_id
        _SCREAMING_SNAKE_CASE     =      batch["input_ids"], batch["attention_mask"], batch["labels"]
        if isinstance(self.model			,		A	):
            _SCREAMING_SNAKE_CASE     =      self.model._shift_right(A	)
        else:
            _SCREAMING_SNAKE_CASE     =      shift_tokens_right(A			,		A	)

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

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

        _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()(
                A			,		attention_mask=A			,		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(
                    A			,		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(
            A			,		attention_mask=A			,		encoder_outputs=(teacher_enc_outputs,)			,		decoder_input_ids=A			,		output_hidden_states=self.do_calc_hidden_loss			,		use_cache=A			,		)
        _SCREAMING_SNAKE_CASE     =      decoder_input_ids.ne(A	)
        _SCREAMING_SNAKE_CASE     =      self.calc_ce_loss(A			,		A			,		teacher_outputs["""logits"""]	)
        if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
            _SCREAMING_SNAKE_CASE     =      self.calc_hidden_loss(
                A			,		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       snake_case_(		A			,		A			,		A			,		A			,		A	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      "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}'
        _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(A	)	)]	)
        _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(A			,		student_states.shape[1:]	)
            _SCREAMING_SNAKE_CASE     =      nn.functional.layer_norm(A			,		teacher_states.shape[1:]	)
        _SCREAMING_SNAKE_CASE     =      nn.functional.mse_loss(A			,		A			,		reduction="""none"""	)
        _SCREAMING_SNAKE_CASE     =      (mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
        return masked_mse





def lowerCamelCase						(					__lowerCamelCase			:   List[str]   )						->Optional[Any]:
    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  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'translation'
    UpperCamelCase			   =			['bleu']
    UpperCamelCase			   =			'bleu'


    def __init__(		self			,		A			,		**A	)		->       Optional[Any]:
        super().__init__(A			,		**A	)
        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			,		A	):
            _SCREAMING_SNAKE_CASE     =      self.tokenizer.lang_code_to_id[hparams.tgt_lang]


    def       snake_case_(		self			,		A			,		A	)		->       str:
        return calculate_bleu(A			,		A	)





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





def lowerCamelCase						(					__lowerCamelCase			:   Any   )						->List[str]:




    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(_UpperCamelCase   )
    return model





def lowerCamelCase						(					__lowerCamelCase			:   Tuple   )						->Optional[int]:
    Path(args.output_dir   ).mkdir(exist_ok=_UpperCamelCase   )
    check_output_dir(_UpperCamelCase ,	expected_items=3   )

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


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

      distill_main(args)










'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->int:
    return len_board * len_board_column * row + column





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   set[int] ,	)						->bool:

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__lowerCamelCase   ) - 1:
        return True

    _SCREAMING_SNAKE_CASE     =      [(0, 1), (0, -1), (-1, 0), (1, 0)]
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for direction in traverts_directions:
        _SCREAMING_SNAKE_CASE     =      row + direction[0]
        _SCREAMING_SNAKE_CASE     =      column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        _SCREAMING_SNAKE_CASE     =      get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
        if key in visited_points_set:
            continue

        visited_points_set.add(__lowerCamelCase   )
        if exits_word(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	word_index + 1 ,	__lowerCamelCase   ):
            return True

        visited_points_set.remove(__lowerCamelCase   )

    return False





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str   )						->bool:
    _SCREAMING_SNAKE_CASE     =      (
        """The board should be a non empty matrix of single chars strings."""
    )

    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(__lowerCamelCase   )

    for row in board:
        if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
            raise ValueError(__lowerCamelCase   )

        for item in row:
            if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) != 1:
                raise ValueError(__lowerCamelCase   )

    # Validate word
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(
            """The word parameter should be a string of length greater than 0."""   )

    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for i in range(__lowerCamelCase   ):
        for j in range(__lowerCamelCase   ):
            if exits_word(
                __lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	0 ,	{get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )}   ):
                return True

    return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()



