



"""simple docstring"""






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

import os
from pathlib import Path






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Union[str, Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     ={
			    '''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]
			lowerCamelCase__		:		Optional[Any]		     ={
			    '''wmt16-en-de-dist-12-1''': [28.3, 27.52],
			    '''wmt16-en-de-dist-6-1''': [27.4, 27.11],
			    '''wmt16-en-de-12-1''': [26.9, 25.75],
			}
			lowerCamelCase__		:		Any		     =f'''{src_lang}-{tgt_lang}'''

			lowerCamelCase__		:		Any		     =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     )
			lowerCamelCase__		:		str		     =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
_lowercase			:			List[str]										=						Path(__file__).resolve().parent.parent.parent
_lowercase			:			Dict										=						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"]:
		_lowercase			:			int										=						model_cards_dir / "allenai" / model_name
		write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)









"""simple docstring"""






# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowercase			:			Dict										=						2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_lowercase			:			str										=						{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
_lowercase			:			str										=						{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		_lowercase			:			Any										=						"facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
		_lowercase			:			List[Any]										=						"allenai"






def 					snake_case__       (		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
			# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
			lowerCamelCase__		:		Optional[Any]		     =dict((re.sub(R'''@@$'''							,		''''''							,		__lowerCamelCase     ), v) if k.endswith('''@@'''     ) else (re.sub(R'''$'''							,		'''</w>'''							,		__lowerCamelCase     ), v) for k, v in d.items()     )
			lowerCamelCase__		:		Tuple		     ='''<s> <pad> </s> <unk>'''.split()
			# restore the special tokens
			for k in keep_keys:
						del da[f'''{k}</w>''']
						lowerCamelCase__		:		str		     =d[k]  # restore
			return da






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			# prep
			assert os.path.exists(__lowerCamelCase     )
			os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase     )
			print(f'''Writing results to {pytorch_dump_folder_path}'''     )

			# handle various types of models

			lowerCamelCase__		:		Union[str, Any]		     =basename(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =dirname(__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			lowerCamelCase__		:		Union[str, Any]		     =cls.hub_models()
			lowerCamelCase__		:		Optional[Any]		     ={'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
			lowerCamelCase__		:		Any		     ='''.'''
			# note: since the model dump is old, fairseq has upgraded its model some
			# time later, and it does a whole lot of rewrites and splits on the saved
			# weights, therefore we can't use torch.load() directly on the model file.
			# see: upgrade_state_dict(state_dict) in fairseq_model.py
			print(f'''using checkpoint {checkpoint_file}'''     )
			lowerCamelCase__		:		Optional[int]		     =hub_utils.from_pretrained(
			    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		archive_map=__lowerCamelCase							,		**__lowerCamelCase     )

			lowerCamelCase__		:		Any		     =vars(chkpt['''args''']['''model''']     )

			lowerCamelCase__		:		int		     =args['''source_lang''']
			lowerCamelCase__		:		Optional[Any]		     =args['''target_lang''']

			lowerCamelCase__		:		Dict		     =dirname(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =basename(__lowerCamelCase     )

			# dicts
			lowerCamelCase__		:		Optional[Any]		     =os.path.join(__lowerCamelCase							,		f'''dict.{src_lang}.txt'''     )
			lowerCamelCase__		:		int		     =os.path.join(__lowerCamelCase							,		f'''dict.{tgt_lang}.txt'''     )

			lowerCamelCase__		:		Dict		     =Dictionary.load(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =rewrite_dict_keys(src_dict.indices     )
			lowerCamelCase__		:		Optional[int]		     =len(__lowerCamelCase     )
			lowerCamelCase__		:		Dict		     =os.path.join(__lowerCamelCase							,		'''vocab-src.json'''     )
			print(f'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''     )
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''     ) as f:
						f.write(json.dumps(__lowerCamelCase							,		ensure_ascii=__lowerCamelCase							,		indent=__lowerCamelCase     )     )

			# detect whether this is a do_lower_case situation, which can be derived by checking whether we
			# have at least one uppercase letter in the source vocab
			lowerCamelCase__		:		Optional[int]		     =True
			for k in src_vocab.keys():
						if not k.islower():
									lowerCamelCase__		:		int		     =False
									break

			lowerCamelCase__		:		Any		     =Dictionary.load(__lowerCamelCase     )
			lowerCamelCase__		:		Tuple		     =rewrite_dict_keys(tgt_dict.indices     )
			lowerCamelCase__		:		str		     =len(__lowerCamelCase     )
			lowerCamelCase__		:		Dict		     =os.path.join(__lowerCamelCase							,		'''vocab-tgt.json'''     )
			print(f'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''     )
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''     ) as f:
						f.write(json.dumps(__lowerCamelCase							,		ensure_ascii=__lowerCamelCase							,		indent=__lowerCamelCase     )     )

			# merges_file (bpecodes)
			lowerCamelCase__		:		Union[str, Any]		     =os.path.join(__lowerCamelCase							,		VOCAB_FILES_NAMES['''merges_file''']     )
			for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
						lowerCamelCase__		:		Tuple		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )
						if os.path.exists(__lowerCamelCase     ):
									break
			with open(__lowerCamelCase							,		encoding='''utf-8'''     ) as fin:
						lowerCamelCase__		:		Optional[Any]		     =fin.read()
			lowerCamelCase__		:		List[Any]		     =re.sub(R''' \d+$'''							,		''''''							,		__lowerCamelCase							,		0							,		re.M     )  # remove frequency number
			print(f'''Generating {merges_file}'''     )
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''     ) as fout:
						fout.write(__lowerCamelCase     )

			# model config
			lowerCamelCase__		:		List[Any]		     =os.path.join(__lowerCamelCase							,		'''config.json'''     )

			# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
			# may have to modify the tokenizer if a different type is used by a future model
			assert args["bpe"] == "fastbpe", f'''need to extend tokenizer to support bpe={args["bpe"]}'''
			assert args["tokenizer"] == "moses", f'''need to extend tokenizer to support bpe={args["tokenizer"]}'''

			lowerCamelCase__		:		str		     ={
			    '''architectures''': ['''FSMTForConditionalGeneration'''],
			    '''model_type''': '''fsmt''',
			    '''activation_dropout''': args['''activation_dropout'''],
			    '''activation_function''': '''relu''',
			    '''attention_dropout''': args['''attention_dropout'''],
			    '''d_model''': args['''decoder_embed_dim'''],
			    '''dropout''': args['''dropout'''],
			    '''init_std''': 0.02,
			    '''max_position_embeddings''': args['''max_source_positions'''],
			    '''num_hidden_layers''': args['''encoder_layers'''],
			    '''src_vocab_size''': src_vocab_size,
			    '''tgt_vocab_size''': tgt_vocab_size,
			    '''langs''': [src_lang, tgt_lang],
			    '''encoder_attention_heads''': args['''encoder_attention_heads'''],
			    '''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
			    '''encoder_layerdrop''': args['''encoder_layerdrop'''],
			    '''encoder_layers''': args['''encoder_layers'''],
			    '''decoder_attention_heads''': args['''decoder_attention_heads'''],
			    '''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
			    '''decoder_layerdrop''': args['''decoder_layerdrop'''],
			    '''decoder_layers''': args['''decoder_layers'''],
			    '''bos_token_id''': 0,
			    '''pad_token_id''': 1,
			    '''eos_token_id''': 2,
			    '''is_encoder_decoder''': True,
			    '''scale_embedding''': not args['''no_scale_embedding'''],
			    '''tie_word_embeddings''': args['''share_all_embeddings'''],
			}

			# good hparam defaults to start with
			lowerCamelCase__		:		Optional[int]		     =5
			lowerCamelCase__		:		List[str]		     =False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						lowerCamelCase__		:		Optional[int]		     =best_score_hparams[model_dir]['''length_penalty''']
			else:
						lowerCamelCase__		:		Union[str, Any]		     =1.0

			print(f'''Generating {fsmt_model_config_file}'''     )
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''     ) as f:
						f.write(json.dumps(__lowerCamelCase							,		ensure_ascii=__lowerCamelCase							,		indent=__lowerCamelCase     )     )

			# tokenizer config
			lowerCamelCase__		:		Any		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		int		     ={
			    '''langs''': [src_lang, tgt_lang],
			    '''model_max_length''': 1024,
			    '''do_lower_case''': do_lower_case,
			}

			print(f'''Generating {fsmt_tokenizer_config_file}'''     )
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''     ) as f:
						f.write(json.dumps(__lowerCamelCase							,		ensure_ascii=__lowerCamelCase							,		indent=__lowerCamelCase     )     )

			# model
			lowerCamelCase__		:		int		     =chkpt['''models'''][0]
			lowerCamelCase__		:		Union[str, Any]		     =model.state_dict()

			# rename keys to start with 'model.'
			lowerCamelCase__		:		Union[str, Any]		     =OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()     )

			# remove unneeded keys
			lowerCamelCase__		:		str		     =[
			    '''model.model''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''model.encoder_embed_tokens.weight''',
			    '''model.decoder_embed_tokens.weight''',
			    '''model.encoder.embed_positions._float_tensor''',
			    '''model.decoder.embed_positions._float_tensor''',
			]
			for k in ignore_keys:
						model_state_dict.pop(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =FSMTConfig.from_pretrained(__lowerCamelCase     )
			lowerCamelCase__		:		int		     =FSMTForConditionalGeneration(__lowerCamelCase     )

			# check that it loads ok
			model_new.load_state_dict(__lowerCamelCase							,		strict=__lowerCamelCase     )

			# save
			lowerCamelCase__		:		Optional[int]		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )
			print(f'''Generating {pytorch_weights_dump_path}'''     )
			torch.save(__lowerCamelCase							,		__lowerCamelCase     )

			print('''Conversion is done!'''     )
			print('''\nLast step is to upload the files to s3'''     )
			print(f'''cd {data_root}'''     )
			print(f'''transformers-cli upload {model_dir}'''     )


if __name__ == "__main__":
		_lowercase			:			List[Any]										=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--fsmt_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
		        " bpecodes, etc."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase			:			str										=						parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




