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 SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]







   def __init__(    self	:	Union[str, Any]      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    assert Path(hparams.data_dir							).exists()
    UpperCamelCase      =			Path(hparams.output_dir							)
    self.output_dir.mkdir(exist_ok=lowerCamelCase_							)

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

    UpperCamelCase      =			str(lowerCamelCase_							)  # Tell lightning we are training the student
    UpperCamelCase      =			AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher							).eval()
    use_task_specific_params(lowerCamelCase_      ,      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(lowerCamelCase_      ,      hparams.task							)
     UpperCamelCase     ,    UpperCamelCase      =			None, None
    else:
     UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			create_student_by_copying_alternating_layers(
         lowerCamelCase_      ,      e=hparams.student_encoder_layers      ,      d=hparams.student_decoder_layers      ,      save_path=lowerCamelCase_							)

    if hparams.length_penalty != -1:
     UpperCamelCase      =			hparams.length_penalty
    UpperCamelCase      =			hparams.teacher  # Use teacher's tokenizer
    super().__init__(lowerCamelCase_      ,      model=lowerCamelCase_      ,      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(lowerCamelCase_							)							)
    if d_layer_ids is None:
     UpperCamelCase      =			list(range(lowerCamelCase_							)							)

    UpperCamelCase     ,    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=lowerCamelCase_							)
      UpperCamelCase      =			get_layers_to_supervise(
          n_student=len(self.d_layer_ids							)      ,      n_teacher=lowerCamelCase_							)
     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 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	Optional[int]							):





    """simple docstring"""


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





    """simple docstring"""


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







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	dict							):





    """simple docstring"""


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

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

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

    UpperCamelCase      =			student_outputs[
        """encoder_last_hidden_state"""
    ]  # use this unless self.different_base_models
    UpperCamelCase     ,    UpperCamelCase      =			zero_tensor(), zero_tensor()
    if self.different_encoder:  # compute encoder hidden state loss
     UpperCamelCase      =			self.teacher.get_encoder()(
         lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      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(
          lowerCamelCase_      ,      student_outputs["""encoder_hidden_states"""]      ,      all_teacher_encoder_outputs["""hidden_states"""]      ,      self.e_matches      ,      normalize_hidden=self.hparams.normalize_hidden      ,      )

    UpperCamelCase      =			self.teacher(
        lowerCamelCase_      ,      attention_mask=lowerCamelCase_      ,      encoder_outputs=(teacher_enc_outputs,)      ,      decoder_input_ids=lowerCamelCase_      ,      output_hidden_states=self.do_calc_hidden_loss      ,      use_cache=lowerCamelCase_      ,      )
    UpperCamelCase      =			decoder_input_ids.ne(lowerCamelCase_							)
    UpperCamelCase      =			self.calc_ce_loss(lowerCamelCase_      ,      lowerCamelCase_      ,      teacher_outputs["""logits"""]							)
    if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
     UpperCamelCase      =			self.calc_hidden_loss(
         lowerCamelCase_      ,      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 	lowerCamelCase_  (    lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	List[str]      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Dict      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			"""expected list or tuple for hidden_states, got tensor of shape: """
    assert not isinstance(lowerCamelCase_      ,      torch.Tensor							), f"""{msg}{hidden_states.shape}"""
    assert not isinstance(lowerCamelCase_      ,      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(lowerCamelCase_							)							)]							)
    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(lowerCamelCase_      ,      student_states.shape[1:]							)
     UpperCamelCase      =			nn.functional.layer_norm(lowerCamelCase_      ,      teacher_states.shape[1:]							)
    UpperCamelCase      =			nn.functional.mse_loss(lowerCamelCase_      ,      lowerCamelCase_      ,      reduction="""none"""							)
    UpperCamelCase      =			(mse * mask.unsqueeze(0							).unsqueeze(-1							)).sum() / valid_count
    return masked_mse





def  lowercase(  UpperCamelCase_					)		->  Union[str, Any]:







 '''simple docstring'''
 # 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=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 SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							"""translation"""
   __lowerCAmelCase											=							["""bleu"""]
   __lowerCAmelCase											=							"""bleu"""







   def __init__(    self	:	List[str]      ,      lowerCamelCase_	:	Optional[Any]      ,      **lowerCamelCase_	:	Any							):





    """simple docstring"""


    super().__init__(lowerCamelCase_      ,      **lowerCamelCase_							)
    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      ,      lowerCamelCase_							):
     UpperCamelCase      =			self.tokenizer.lang_code_to_id[hparams.tgt_lang]







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	Dict      ,      lowerCamelCase_	:	Optional[Any]							):





    """simple docstring"""


    return calculate_bleu(lowerCamelCase_      ,      lowerCamelCase_							)







   @staticmethod
   def 	lowerCamelCase_  (    lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


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





def  lowercase(  UpperCamelCase_					)		->  Optional[Any]:







 '''simple docstring'''
 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  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 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__":
     _SCREAMING_SNAKE_CASE   		=			argparse.ArgumentParser()
     _SCREAMING_SNAKE_CASE   		=			pl.Trainer.add_argparse_args(parser)
     _SCREAMING_SNAKE_CASE   		=			SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
     _SCREAMING_SNAKE_CASE   		=			parser.parse_args()

     distill_main(args)

from __future__ import annotations





def  lowercase(  UpperCamelCase_					)		->  Dict:







 '''simple docstring'''
 global graph, reversed_graph, scc, component, visit, stack
 if visit[u]:
  return
 UpperCamelCase      =			True
 for v in graph[u]:
  dfs(UpperCamelCase_					)
 stack.append(UpperCamelCase_					)





def  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 global graph, reversed_graph, scc, component, visit, stack
 if visit[u]:
  return
 UpperCamelCase      =			True
 component.append(UpperCamelCase_					)
 for v in reversed_graph[u]:
  dfsa(UpperCamelCase_					)





def  lowercase(  )		->  Any:







 '''simple docstring'''
 global graph, reversed_graph, scc, component, visit, stack
 for i in range(UpperCamelCase_					):
  dfs(UpperCamelCase_					)
 UpperCamelCase      =			[False] * n
 for i in stack[::-1]:
  if visit[i]:
   continue
  UpperCamelCase      =			[]
  dfsa(UpperCamelCase_					)
  scc.append(UpperCamelCase_					)
 return scc


if __name__ == "__main__":
     # n - no of nodes, m - no of edges
     _SCREAMING_SNAKE_CASE		,  _SCREAMING_SNAKE_CASE   		=			list(map(int, input().strip().split()))

     _SCREAMING_SNAKE_CASE   		=			[[] for _ in range(n)]  # graph
     _SCREAMING_SNAKE_CASE   		=			[[] for i in range(n)]  # reversed graph
     # input graph data (edges)
     for _ in range(m):
          _SCREAMING_SNAKE_CASE		,  _SCREAMING_SNAKE_CASE   		=			list(map(int, input().strip().split()))
          graph[u].append(v)
          reversed_graph[v].append(u)

     _SCREAMING_SNAKE_CASE   		=			[]
     _SCREAMING_SNAKE_CASE   		=			[False] * n
     _SCREAMING_SNAKE_CASE   		=			[]
     _SCREAMING_SNAKE_CASE   		=			[]
     print(kosaraju())
