






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__	):



     """simple docstring"""



     __snake_case		:				Tuple  					=['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']





     def __init__(		self					:	Optional[int]  ,  lowerCAmelCase__					:	Optional[Any])							->	int:

      """simple docstring"""
      assert Path(hparams.data_dir).exists()
      SCREAMING_SNAKE_CASE				    =		Path(hparams.output_dir)
      self.output_dir.mkdir(exist_ok=lowerCAmelCase__)

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

      SCREAMING_SNAKE_CASE				    =		str(lowerCAmelCase__)  # Tell lightning we are training the student
      SCREAMING_SNAKE_CASE				    =		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:
       SCREAMING_SNAKE_CASE				    =		AutoModelForSeqaSeqLM.from_pretrained(hparams.student)
       use_task_specific_params(lowerCAmelCase__  ,  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(
           lowerCAmelCase__  ,  e=hparams.student_encoder_layers  ,  d=hparams.student_decoder_layers  ,  save_path=lowerCAmelCase__)

      if hparams.length_penalty != -1:
       SCREAMING_SNAKE_CASE				    =		hparams.length_penalty
      SCREAMING_SNAKE_CASE				    =		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":
       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(lowerCAmelCase__))
      if d_layer_ids is None:
       SCREAMING_SNAKE_CASE				    =		list(range(lowerCAmelCase__))

      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=lowerCAmelCase__)
        SCREAMING_SNAKE_CASE				    =		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
        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     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	List[Any])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		mask[:, :, None].expand_as(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		s_logits.size(-1)
      SCREAMING_SNAKE_CASE				    =		torch.masked_select(lowerCAmelCase__  ,  lowerCAmelCase__)  # (bs * seq_length * voc_size) modulo the 1s in mask
      SCREAMING_SNAKE_CASE				    =		torch.masked_select(lowerCAmelCase__  ,  lowerCAmelCase__)  # (bs * seq_length * voc_size) modulo the 1s in mask
      SCREAMING_SNAKE_CASE				    =		s_logits_slct.view(-1  ,  lowerCAmelCase__)  # (bs * seq_length, voc_size) modulo the 1s in mask
      SCREAMING_SNAKE_CASE				    =		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()
      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     __UpperCamelCase						(		lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	Any)							->	List[str]:

      """simple docstring"""
      SummarizationModule.add_model_specific_args(lowerCAmelCase__  ,  lowerCAmelCase__)
      add_distill_args(lowerCAmelCase__)
      return parser





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	dict)							->	tuple:

      """simple docstring"""
      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  ,  lowerCAmelCase__):
       SCREAMING_SNAKE_CASE				    =		self.model._shift_right(lowerCAmelCase__)
      else:
       SCREAMING_SNAKE_CASE				    =		shift_tokens_right(lowerCAmelCase__  ,  lowerCAmelCase__)

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

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

      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()(
           lowerCAmelCase__  ,  attention_mask=lowerCAmelCase__  ,  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(
            lowerCAmelCase__  ,  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(
          lowerCAmelCase__  ,  attention_mask=lowerCAmelCase__  ,  encoder_outputs=(teacher_enc_outputs,)  ,  decoder_input_ids=lowerCAmelCase__  ,  output_hidden_states=self.do_calc_hidden_loss  ,  use_cache=lowerCAmelCase__  ,  )
      SCREAMING_SNAKE_CASE				    =		decoder_input_ids.ne(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		self.calc_ce_loss(lowerCAmelCase__  ,  lowerCAmelCase__  ,  teacher_outputs['logits'])
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       SCREAMING_SNAKE_CASE				    =		self.calc_hidden_loss(
           lowerCAmelCase__  ,  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     __UpperCamelCase						(		lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		'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}'''
      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(lowerCAmelCase__))])
      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(lowerCAmelCase__  ,  student_states.shape[1:])
       SCREAMING_SNAKE_CASE				    =		nn.functional.layer_norm(lowerCAmelCase__  ,  teacher_states.shape[1:])
      SCREAMING_SNAKE_CASE				    =		nn.functional.mse_loss(lowerCAmelCase__  ,  lowerCAmelCase__  ,  reduction='none')
      SCREAMING_SNAKE_CASE				    =		(mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
      return masked_mse





def 						A_					(						lowercase_			)				->int:







 """simple docstring"""

 parser.add_argument('--teacher'	,  type=lowercase_			)
 parser.add_argument('--alpha_ce'	,  default=0.8	,  type=lowercase_			)
 parser.add_argument('--alpha_mlm'	,  default=0.2	,  type=lowercase_			)
 parser.add_argument('--alpha_hid'	,  default=0.0	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--student'	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--student_decoder_layers'	,  default=1_2	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--student_encoder_layers'	,  default=1_2	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--no_teacher'	,  action='store_true'	,  default=lowercase_			)
 parser.add_argument('--length_penalty'	,  type=lowercase_	,  default=-1			)
 parser.add_argument('--supervise_forward'	,  action='store_true'	,  default=lowercase_			)
 parser.add_argument('--normalize_hidden'	,  action='store_true'	,  default=lowercase_			)
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Any  					='''translation'''
     __snake_case		:				Dict  					=['''bleu''']
     __snake_case		:				Optional[Any]  					='''bleu'''





     def __init__(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	int  ,  **lowerCAmelCase__					:	List[str])							->	List[Any]:

      """simple docstring"""
      super().__init__(lowerCAmelCase__  ,  **lowerCAmelCase__)
      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  ,  lowerCAmelCase__):
       SCREAMING_SNAKE_CASE				    =		self.tokenizer.lang_code_to_id[hparams.tgt_lang]





     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	List[Any])							->	dict:

      """simple docstring"""
      return calculate_bleu(lowerCAmelCase__  ,  lowerCAmelCase__)





     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Tuple)							->	Optional[int]:

      """simple docstring"""
      TranslationModule.add_model_specific_args(lowerCAmelCase__  ,  lowerCAmelCase__)
      add_distill_args(lowerCAmelCase__)
      return parser





def 						A_					(						lowercase_			)				->Optional[int]:







 """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(lowercase_			)
 return model





def 						A_					(						lowercase_			)				->Tuple:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE				    =		create_module(lowercase_			)
 return ft_main(lowercase_	,  model=lowercase_			)


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

      distill_main(args)












# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    "en": "Machine learning is great, isn't it?",
		    "ru": "Машинное обучение - это здорово, не так ли?",
		    "de": "Maschinelles Lernen ist großartig, nicht wahr?",
		}

		# BLUE scores as follows:
		# "pair": [fairseq, transformers]
		__A    =							{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		__A    =							F"""{src_lang}-{tgt_lang}"""

		__A    =							F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
		model_card_dir.mkdir(parents=lowerCAmelCase__					,     exist_ok=lowerCAmelCase__	)
		__A    =							os.path.join(lowerCAmelCase__					,     "README.md"	)
		print(F"""Generating {path}"""	)
		with open(lowerCAmelCase__					,     "w"					,     encoding="utf-8"	) as f:
				f.write(lowerCAmelCase__	)


# make sure we are under the root of the project
snake_case_   :	Optional[int]							      =Path(__file__).resolve().parent.parent.parent
snake_case_   :	Dict							      =repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
					snake_case_   :	Union[str, Any]							      =model_cards_dir / '''allenai''' / model_name
					write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)

