



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       _lowercase				(  __SCREAMING_SNAKE_CASE      ):





	"""simple docstring"""
	__A		      =				["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						assert Path(hparams.data_dir						).exists()
						a   	=  Path(hparams.output_dir						)
						self.output_dir.mkdir(exist_ok=__UpperCAmelCase						)

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

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

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

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

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  mask[:, :, None].expand_as(__UpperCAmelCase						)
						a   	=  s_logits.size(-1						)
						a   	=  torch.masked_select(__UpperCAmelCase						,			__UpperCAmelCase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
						a   	=  torch.masked_select(__UpperCAmelCase						,			__UpperCAmelCase						)  # (bs * seq_length * voc_size) modulo the 1s in mask
						a   	=  s_logits_slct.view(-1						,			__UpperCAmelCase						)  # (bs * seq_length, voc_size) modulo the 1s in mask
						a   	=  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()
						a   	=  (
						    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 	UpperCamelCase_			(lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

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

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

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

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

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

						a   	=  (
						    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 	UpperCamelCase_			(lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  "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}'''
						a   	=  attention_mask.to(hidden_states[0]						)
						a   	=  mask.sum() * hidden_states[0].size(-1						)
						a   	=  torch.stack([hidden_states[i] for i in range(len(__UpperCAmelCase						)						)]						)
						a   	=  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:
											a   	=  nn.functional.layer_norm(__UpperCAmelCase						,			student_states.shape[1:]						)
											a   	=  nn.functional.layer_norm(__UpperCAmelCase						,			teacher_states.shape[1:]						)
						a   	=  nn.functional.mse_loss(__UpperCAmelCase						,			__UpperCAmelCase						,			reduction="none"						)
						a   	=  (mse * mask.unsqueeze(0						).unsqueeze(-1						)).sum() / valid_count
						return masked_mse






def        a( A			:       Any			)						->			str:
					"""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       _lowercase				(  __SCREAMING_SNAKE_CASE      ):





	"""simple docstring"""
	__A		      =				"translation"
	__A		      =				["bleu"]
	__A		      =				"bleu"




	def __init__(self						,			lowerCamelCase_						,			**lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						return calculate_bleu(__UpperCAmelCase						,			__UpperCAmelCase						)




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

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






def        a( A			:       str			)						->			Optional[Any]:
					"""simple docstring"""





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






def        a( A			:       Dict			)						->			str:
					"""simple docstring"""





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

					a   	=  create_module(A			)
					return ft_main(A					,	model=A			)


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

				distill_main(args)


"""simple docstring"""

import math
from itertools import permutations






def 				A		(							snake_case :int						) ->      bool:

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5							,   int(math.sqrt(snake_case						) + 1						)							,   6						):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 				A		(							snake_case :int							,   snake_case :list						) ->      bool:
 __UpperCamelCase    ,      __UpperCamelCase							    =			0, len(snake_case						) - 1
 while left <= right:
  __UpperCamelCase							    =			(left + right) // 2
  if prime_list[middle] == target:
   return True
  elif prime_list[middle] < target:
   __UpperCamelCase							    =			middle + 1
  else:
   __UpperCamelCase							    =			middle - 1

 return False






def 				A		(							) ->      int:
 __UpperCamelCase							    =			[n for n in range(1_0_0_1							,   1_0_0_0_0							,   2						) if is_prime(snake_case						)]
 __UpperCamelCase							    =			[]

 for number in prime_list:
  __UpperCamelCase							    =			[]

  for prime_member in permutations(list(str(snake_case						)						)						):
   __UpperCamelCase							    =			int(''.join(snake_case						)						)

   if prime % 2 == 0:
    continue

   if search(snake_case							,   snake_case						):
    tmp_numbers.append(snake_case						)

  tmp_numbers.sort()
  if len(snake_case						) >= 3:
   candidates.append(snake_case						)

 __UpperCamelCase							    =			[]
 for candidate in candidates:
  __UpperCamelCase							    =			len(snake_case						)
  __UpperCamelCase							    =			False

  for i in range(snake_case						):
   for j in range(i + 1							,   snake_case						):
    for k in range(j + 1							,   snake_case						):
     if (
         abs(candidate[i] - candidate[j]						)
         == abs(candidate[j] - candidate[k]						)
         and len({candidate[i], candidate[j], candidate[k]}						) == 3
     ):
      passed.append(
          sorted([candidate[i], candidate[j], candidate[k]]						)						)
      __UpperCamelCase							    =			True

     if found:
      break
    if found:
     break
   if found:
    break

 __UpperCamelCase							    =			set()
 for seq in passed:
  answer.add(''.join([str(snake_case						) for i in seq]						)						)

 return max(int(snake_case						) for x in answer						)


if __name__ == "__main__":
      print(solution())



