
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path





def 				_lowerCamelCase      (       lowerCamelCase_:	Union[str, Any]					,						lowerCamelCase_:	Any					,						lowerCamelCase_:	Dict					,						lowerCamelCase_:	Tuple		):



				'''simple docstring'''







				A  :	Tuple				=					{
				    "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  :	List[str]				=					{
				    "wmt16-en-de-dist-12-1": [2_8.3, 2_7.5_2],
				    "wmt16-en-de-dist-6-1": [2_7.4, 2_7.1_1],
				    "wmt16-en-de-12-1": [2_6.9, 2_5.7_5],
				}
				A  :	Optional[int]				=					f"""{src_lang}-{tgt_lang}"""

				A  :	List[str]				=					f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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  :	Optional[Any]				=					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
UpperCamelCase_						=						Path(__file__).resolve().parent.parent.parent
UpperCamelCase_						=						repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					UpperCamelCase_						=						model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)






# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE__							:      List[str]         =    Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

SCREAMING_SNAKE_CASE__							:      Any         =    """zero2"""
SCREAMING_SNAKE_CASE__							:      Dict         =    """zero3"""
SCREAMING_SNAKE_CASE__							:      str         =    [ZEROa, ZEROa]






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # customize the test name generator function as we want both params to appear in the sub-test
   # name, as by default it shows only the first param
   a__						:    Optional[Any]   		=	parameterized.to_safe_name("_".join(str(lowerCamelCase ) for x in param.args ) )
   return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 	__lowerCAmelCase      (       _UpperCamelCase	):





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	str:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      pass





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = True       ,      snake_case = True       ,      snake_case = True       ,      )					->	str:
      """simple docstring"""





      a__						:    Tuple   		=	models[model]

      a__						:    int   		=	self.run_trainer(
          stage=snake_case       ,      model_name=snake_case       ,      eval_steps=snake_case       ,      num_train_epochs=1       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )

      self.do_checks(snake_case   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = 1       ,      snake_case = True       ,      snake_case = True       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    str   		=	self.get_auto_remove_tmp_dir("./xxx"       ,      after=snake_case   )
      a__						:    List[Any]   		=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case   )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

      if fpaa:
         args.extend(["--fp16"]   )

      # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
      # hence the separate config files
      a__						:    str   		=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
      a__						:    Dict   		=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
      a__						:    Optional[int]   		=	self.get_launcher(snake_case   )

      a__						:    Optional[int]   		=	launcher + script + args + ds_args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(snake_case       ,      env=self.get_env()   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case=False   )					->	List[Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	min(2       ,      get_gpu_count()   ) if distributed else 1
      return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

