




'''simple docstring'''







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    (      _lowercase					):
       __a							:	Union[str, Any]  							=   ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
       def __init__(      self		:      str  ,							lowercase		:      Tuple							):



           '''simple docstring'''

           assert Path(hparams.data_dir							).exists()
           UpperCAmelCase     			=						Path(hparams.output_dir							)
           self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE							)

           UpperCAmelCase     			=						self.output_dir.joinpath('''student'''							)

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

           if hparams.length_penalty != -1:
               UpperCAmelCase     			=						hparams.length_penalty
           UpperCAmelCase     			=						hparams.teacher  # Use teacher's tokenizer
           super().__init__(_SCREAMING_SNAKE_CASE  ,							model=_SCREAMING_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(_SCREAMING_SNAKE_CASE							)							)
           if d_layer_ids is None:
               UpperCAmelCase     			=						list(range(_SCREAMING_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=_SCREAMING_SNAKE_CASE							)
                   UpperCAmelCase     			=						get_layers_to_supervise(
                       n_student=len(self.d_layer_ids							)  ,							n_teacher=_SCREAMING_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        A (      self		:      List[Any]  ,							lowercase		:      Optional[Any]  ,							lowercase		:      int  ,							lowercase		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						s_logits.size(-1							)
           UpperCAmelCase     			=						torch.masked_select(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)  # (bs * seq_length * voc_size) modulo the 1s in mask
           UpperCAmelCase     			=						torch.masked_select(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)  # (bs * seq_length * voc_size) modulo the 1s in mask
           UpperCAmelCase     			=						s_logits_slct.view(-1  ,							_SCREAMING_SNAKE_CASE							)  # (bs * seq_length, voc_size) modulo the 1s in mask
           UpperCAmelCase     			=						t_logits_slct.view(-1  ,							_SCREAMING_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        A (      lowercase		:      Any  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
           add_distill_args(_SCREAMING_SNAKE_CASE							)
           return parser
       def        A (      self		:      Any  ,							lowercase		:      dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.tokenizer.pad_token_id
           UpperCAmelCase     			=						batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
           if isinstance(self.model  ,							_SCREAMING_SNAKE_CASE							):
               UpperCAmelCase     			=						self.model._shift_right(_SCREAMING_SNAKE_CASE							)
           else:
               UpperCAmelCase     			=						shift_tokens_right(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)

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

           def zero_tensor():
               return torch.tensor(0.0							).type_as(_SCREAMING_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()(
                   _SCREAMING_SNAKE_CASE  ,							attention_mask=_SCREAMING_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(
                       _SCREAMING_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(
               _SCREAMING_SNAKE_CASE  ,							attention_mask=_SCREAMING_SNAKE_CASE  ,							encoder_outputs=(teacher_enc_outputs,)  ,							decoder_input_ids=_SCREAMING_SNAKE_CASE  ,							output_hidden_states=self.do_calc_hidden_loss  ,							use_cache=_SCREAMING_SNAKE_CASE  ,							)
           UpperCAmelCase     			=						decoder_input_ids.ne(_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						self.calc_ce_loss(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE  ,							teacher_outputs['''logits''']							)
           if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
               UpperCAmelCase     			=						self.calc_hidden_loss(
                   _SCREAMING_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        A (      lowercase		:      List[Any]  ,							lowercase		:      Any  ,							lowercase		:      Dict  ,							lowercase		:      str  ,							lowercase		:      Dict							):



           '''simple docstring'''

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






def 		snake_case_				(_a      :				Optional[Any]	):
    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=1_2       ,  type=_a       ,  required=_a	)
    parser.add_argument('''--student_encoder_layers'''       ,  default=1_2       ,  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      _a    (      _lowercase					):
       __a							:	int  							=   '''translation'''
       __a							:	int  							=   ['''bleu''']
       __a							:	str  							=   '''bleu'''
       def __init__(      self		:      int  ,							lowercase		:      List[Any]  ,							**lowercase		:      int							):



           '''simple docstring'''

           super().__init__(_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_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  ,							_SCREAMING_SNAKE_CASE							):
               UpperCAmelCase     			=						self.tokenizer.lang_code_to_id[hparams.tgt_lang]
       def        A (      self		:      int  ,							lowercase		:      Tuple  ,							lowercase		:      List[Any]							):



           '''simple docstring'''

           return calculate_bleu(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)







       @staticmethod
       def        A (      lowercase		:      Any  ,							lowercase		:      Any							):



           '''simple docstring'''

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






def 		snake_case_				(_a      :				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(_a	)
    return model





def 		snake_case_				(_a      :				int	):
    Path(args.output_dir	).mkdir(exist_ok=_a	)
    check_output_dir(_a       ,  expected_items=3	)

    UpperCAmelCase     			=						create_module(_a	)
    return ft_main(_a       ,  model=_a	)


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

   distill_main(args)








import math






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		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(_a			) + 1			)      ,  6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 	lowerCamelCase_       (			_a = 1_000      ,  _a = 1_000			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   [0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1      ,  _a			):
				for b in range(2      ,  _a			):
						if is_prime(_a			):
								lowerCAmelCase__  :     str       	=   0
								lowerCAmelCase__  :     int       	=   0
								while is_prime((n**2) + (a * n) + b			):
										count += 1
										n += 1
								if count > longest[0]:
										lowerCAmelCase__  :     Dict       	=   [count, a, b]
		lowerCAmelCase__  :     int       	=   longest[1] * longest[2]
		return ans


if __name__ == "__main__":
		print(solution(1000, 1000))




