

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       :		Optional[Any]  	=					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']

  def __init__(   self  ,						_A):
      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_							,     SCREAMING_SNAKE_CASE_													=  None, None
      else:
          SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     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_							,     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   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      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   lowerCAmelCase__       (   _A  ,						_A):
      SummarizationModule.add_model_specific_args(_A  ,						_A)
      add_distill_args(_A)
      return parser

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.tokenizer.pad_token_id
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     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_							,     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_							,     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   lowerCAmelCase__       (   _A  ,						_A  ,						_A  ,						_A  ,						_A):
      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    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[int]    ):





    """simple docstring"""


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



class  __snake_case					(      lowerCAmelCase__						):
  __lowerCAmelCase       :		Tuple  	=					'translation'
  __lowerCAmelCase       :		int  	=					['bleu']
  __lowerCAmelCase       :		Optional[int]  	=					'bleu'

  def __init__(   self  ,						_A  ,						**_A):
      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   lowerCAmelCase__       (   self  ,						_A  ,						_A):
      return calculate_bleu(_A  ,						_A)


  @staticmethod
  def   lowerCAmelCase__       (   _A  ,						_A):
      TranslationModule.add_model_specific_args(_A  ,						_A)
      add_distill_args(_A)
      return parser






def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Any    ):





    """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(_SCREAMING_SNAKE_CASE    )
    return model


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Tuple    ):





    """simple docstring"""


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

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


if __name__ == "__main__":
       UpperCamelCase__					: Tuple								=      argparse.ArgumentParser()
       UpperCamelCase__					: List[Any]								=      pl.Trainer.add_argparse_args(parser)
       UpperCamelCase__					: Any								=      SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
       UpperCamelCase__					: Dict								=      parser.parse_args()

       distill_main(args)





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Queue()
    SCREAMING_SNAKE_CASE_													=  [False] * len(_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  [-1] * len(_SCREAMING_SNAKE_CASE    )

    def bfs():
        while not queue.empty():
            SCREAMING_SNAKE_CASE_													=  queue.get()
            SCREAMING_SNAKE_CASE_													=  True

            for neighbour in graph[u]:
                if neighbour == u:
                    return False

                if color[neighbour] == -1:
                    SCREAMING_SNAKE_CASE_													=  1 - color[u]
                    queue.put(_SCREAMING_SNAKE_CASE    )

                elif color[neighbour] == color[u]:
                    return False

        return True

    for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
        if not visited[i]:
            queue.put(_SCREAMING_SNAKE_CASE    )
            SCREAMING_SNAKE_CASE_													=  0
            if bfs() is False:
                return False

    return True


if __name__ == "__main__":
       # Adjacency List of graph
       print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))


