
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  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            assert Path(hparams.data_dir    ).exists()
            __snake_case						       =   Path(hparams.output_dir    )
            self.output_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_    )

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

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

            __snake_case						       =   not (hparams.student is None or hparams.teacher == hparams.student)
            __snake_case						       =   (not self.different_base_models) and hparams.alpha_hid > 0
            __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
            __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:
                   __snake_case						       =   list(range(SCREAMING_SNAKE_CASE_    )    )
            if d_layer_ids is None:
                   __snake_case						       =   list(range(SCREAMING_SNAKE_CASE_    )    )

            __snake_case       ,       __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:
                          __snake_case						       =   get_layers_to_supervise(
                              n_student=len(self.e_layer_ids    )							,       n_teacher=SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   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
                          __snake_case						       =   self.e_layer_ids
                          __snake_case						       =   self.d_layer_ids
            else:
                   __snake_case						       =   None
                   __snake_case						       =   None

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   mask[:, :, None].expand_as(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   s_logits.size(-1    )
            __snake_case						       =   torch.masked_select(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )  # (bs * seq_length * voc_size) modulo the 1s in mask
            __snake_case						       =   torch.masked_select(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )  # (bs * seq_length * voc_size) modulo the 1s in mask
            __snake_case						       =   s_logits_slct.view(-1							,       SCREAMING_SNAKE_CASE_    )  # (bs * seq_length, voc_size) modulo the 1s in mask
            __snake_case						       =   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()
            __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				(  SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            add_distill_args(SCREAMING_SNAKE_CASE_    )
            return parser

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   self.tokenizer.pad_token_id
            __snake_case       ,       __snake_case       ,       __snake_case						       =   batch['input_ids'], batch['attention_mask'], batch['labels']
            if isinstance(self.model							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   self.model._shift_right(SCREAMING_SNAKE_CASE_    )
            else:
                   __snake_case						       =   shift_tokens_right(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            # noinspection PyCallingNonCallable
            __snake_case						       =   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_							,       )
            __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
                   __snake_case						       =   nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   loss_fct(lm_logits.view(-1							,       lm_logits.shape[-1]    )							,       labels.view(-1    )    )
            else:
                   __snake_case						       =   nn.functional.log_softmax(SCREAMING_SNAKE_CASE_							,       dim=-1    )
                   __snake_case       ,       __snake_case						       =   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_    )

            __snake_case						       =   student_outputs[
                'encoder_last_hidden_state'
            ]  # use this unless self.different_base_models
            __snake_case       ,       __snake_case						       =   zero_tensor(), zero_tensor()
            if self.different_encoder:  # compute encoder hidden state loss
                   __snake_case						       =   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:
                          __snake_case						       =   all_teacher_encoder_outputs['last_hidden_state']
                   elif self.do_calc_hidden_loss:
                          __snake_case						       =   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							,       )

            __snake_case						       =   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_							,       )
            __snake_case						       =   decoder_input_ids.ne(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   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
                   __snake_case						       =   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							,       )

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





def        __lowercase(							__snake_case				:		Dict					)       ->   Dict:
       # 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=__snake_case					)
       parser.add_argument('--alpha_ce'    ,default=0.8    ,type=__snake_case					)
       parser.add_argument('--alpha_mlm'    ,default=0.2    ,type=__snake_case					)
       parser.add_argument('--alpha_hid'    ,default=0.0    ,type=__snake_case    ,required=__snake_case					)
       parser.add_argument('--student'    ,type=__snake_case    ,required=__snake_case					)
       parser.add_argument('--student_decoder_layers'    ,default=12    ,type=__snake_case    ,required=__snake_case					)
       parser.add_argument('--student_encoder_layers'    ,default=12    ,type=__snake_case    ,required=__snake_case					)
       parser.add_argument('--no_teacher'    ,action='store_true'    ,default=__snake_case					)
       parser.add_argument('--length_penalty'    ,type=__snake_case    ,default=-1					)
       parser.add_argument('--supervise_forward'    ,action='store_true'    ,default=__snake_case					)
       parser.add_argument('--normalize_hidden'    ,action='store_true'    ,default=__snake_case					)







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""translation"""
     lowercase__				  =			["""bleu"""]
     lowercase__				  =			"""bleu"""

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            return calculate_bleu(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     @staticmethod
     def 						__lowerCamelCase				(  SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            TranslationModule.add_model_specific_args(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            add_distill_args(SCREAMING_SNAKE_CASE_    )
            return parser





def        __lowercase(							__snake_case				:		Union[str, Any]					)       ->   List[str]:
       if args.no_teacher:
              __snake_case						       =   TranslationModule if 'translation' in args.task else SummarizationModule
       else:  # DISTILL WITH TEACHER
              __snake_case						       =   TranslationDistiller if 'translation' in args.task else SummarizationDistiller
       __snake_case						       =   module_cls.__name__
       print(f'''using module {args.setup_cls}'''					)
       __snake_case						       =   module_cls(__snake_case					)
       return model
def        __lowercase(							__snake_case				:		Union[str, Any]					)       ->   Union[str, Any]:
       Path(args.output_dir					).mkdir(exist_ok=__snake_case					)
       check_output_dir(__snake_case    ,expected_items=3					)

       __snake_case						       =   create_module(__snake_case					)
       return ft_main(__snake_case    ,model=__snake_case					)


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

 distill_main(args)


import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  _lowerCamelCase			(lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			HerbertTokenizer
     lowercase__				  =			HerbertTokenizerFast
     lowercase__				  =			True

     def 						__lowerCamelCase				(  self    ):
            super().setUp()

            # Use a simpler test file without japanese/chinese characters
            with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''							,       encoding='utf-8'    ) as f_data:
                   __snake_case						       =   f_data.read().replace('\n\n'							,       '\n'    ).strip()

            __snake_case						       =   [
                '<s>',
                '</s>',
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                'w</w>',
                'r</w>',
                't</w>',
                'lo',
                'low',
                'er</w>',
                'low</w>',
                'lowest</w>',
                'newer</w>',
                'wider</w>',
                ',</w>',
                '<unk>',
            ]
            __snake_case						       =   dict(zip(SCREAMING_SNAKE_CASE_							,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
            __snake_case						       =   ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['vocab_file']    )
            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['merges_file']    )
            with open(self.vocab_file							,       'w'    ) as fp:
                   fp.write(json.dumps(SCREAMING_SNAKE_CASE_    )    )
            with open(self.merges_file							,       'w'    ) as fp:
                   fp.write('\n'.join(SCREAMING_SNAKE_CASE_    )    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   'lower newer'
            __snake_case						       =   'lower newer'
            return input_text, output_text

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.tokenizer_class(vocab_file=self.vocab_file							,       merges_file=self.merges_file    )

            __snake_case						       =   'lower'
            __snake_case						       =   ['low', 'er</w>']
            __snake_case						       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokens + ['<unk>']
            __snake_case						       =   [16, 17, 23]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            if not self.test_rust_tokenizer:
                   return

            __snake_case						       =   self.get_tokenizer()
            __snake_case						       =   self.get_rust_tokenizer()

            __snake_case						       =   'lower,newer'

            __snake_case						       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokenizer.encode(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   self.get_rust_tokenizer()
            __snake_case						       =   tokenizer.encode(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'    )

            __snake_case						       =   tokenizer.encode('konstruowanie sekwencji'							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   tokenizer.encode('konstruowanie wielu sekwencji'							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            assert encoded_sentence == [0] + text + [2]
            assert encoded_pair == [0] + text + [2] + text_a + [2]
