



import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase_								=     getLogger(__name__)



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_ = 8						,  SCREAMING_SNAKE_CASE_ = 1024						,  SCREAMING_SNAKE_CASE_="val"						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_="summarization"						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=1						,  SCREAMING_SNAKE_CASE_ = None						,  SCREAMING_SNAKE_CASE_=""						,  **SCREAMING_SNAKE_CASE_						,  ):
			lowercase__					  =				str(SCREAMING_SNAKE_CASE_  )
			assert local_rank is not None
			torch.distributed.init_process_group(backend="nccl"						,  rank=SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				Path(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				save_dir.joinpath(f'''rank_{local_rank}_output.json'''  )
			torch.cuda.set_device(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_  ).cuda()
			if fpaa:
						lowercase__					  =				model.half()
			# determine if we need to increase num_beams
			use_task_specific_params(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )  # update config with task specific params
			lowercase__					  =				generate_kwargs.pop("num_beams"						,  model.config.num_beams  )  # AttributeError risk?
			if num_return_sequences > num_beams:
						lowercase__					  =				num_return_sequences

			lowercase__					  =				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
			logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''  )  # if this is wrong, check config.model_type.

			if max_source_length is None:
						lowercase__					  =				tokenizer.model_max_length
			if prefix is None:
						lowercase__					  =				prefix or getattr(model.config						,  "prefix"						,  ""  ) or ""
			lowercase__					  =				SeqaSeqDataset(
			    SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  max_target_length=1024						,  type_path=SCREAMING_SNAKE_CASE_						,  n_obs=SCREAMING_SNAKE_CASE_						,  prefix=SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_						,  )
			# I set shuffle=True for a more accurate progress bar.
			# If all the longest samples are first, the prog bar estimate is too high at the beginning.
			lowercase__					  =				ds.make_sortish_sampler(SCREAMING_SNAKE_CASE_						,  distributed=SCREAMING_SNAKE_CASE_						,  add_extra_examples=SCREAMING_SNAKE_CASE_						,  shuffle=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  sampler=SCREAMING_SNAKE_CASE_						,  batch_size=SCREAMING_SNAKE_CASE_						,  collate_fn=ds.collate_fn  )
			lowercase__					  =				[]
			for batch in tqdm(SCREAMING_SNAKE_CASE_  ):
						lowercase__					  =				model.generate(
						    input_ids=batch["input_ids"].to(model.device  )						,  attention_mask=batch["attention_mask"].to(model.device  )						,  num_return_sequences=SCREAMING_SNAKE_CASE_						,  num_beams=SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_						,  )
						lowercase__					  =				tokenizer.batch_decode(SCREAMING_SNAKE_CASE_						,  skip_special_tokens=SCREAMING_SNAKE_CASE_						,  clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				batch["ids"]
						if num_return_sequences > 1:
									lowercase__					  =				chunks(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )  # batch size chunks, each of size num_return_seq
						for i, pred in enumerate(SCREAMING_SNAKE_CASE_  ):
									results.append({"pred": pred, "id": ids[i].item()}  )
			save_json(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			return results, sampler.num_replicas



def __lowerCAmelCase (     ):
			lowercase__					  =				argparse.ArgumentParser(
			    epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"  )
			parser.add_argument("--data_dir"						,  type=SCREAMING_SNAKE_CASE_						,  help="like cnn_dm/test.source"  )
			parser.add_argument(
			    "--model_name"						,  type=SCREAMING_SNAKE_CASE_						,  help="like facebook/bart-large-cnn,t5-base, etc."						,  default="sshleifer/distilbart-xsum-12-3"						,  )
			parser.add_argument("--save_dir"						,  type=SCREAMING_SNAKE_CASE_						,  help="where to save"						,  default="tmp_gen"  )
			parser.add_argument("--max_source_length"						,  type=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_  )
			parser.add_argument(
			    "--type_path"						,  type=SCREAMING_SNAKE_CASE_						,  default="test"						,  help="which subset to evaluate typically train/val/test"  )
			parser.add_argument("--task"						,  type=SCREAMING_SNAKE_CASE_						,  default="summarization"						,  help="used for task_specific_params + metrics"  )
			parser.add_argument("--bs"						,  type=SCREAMING_SNAKE_CASE_						,  default=8						,  required=SCREAMING_SNAKE_CASE_						,  help="batch size"  )
			parser.add_argument(
			    "--local_rank"						,  type=SCREAMING_SNAKE_CASE_						,  default=-1						,  required=SCREAMING_SNAKE_CASE_						,  help="should be passed by distributed.launch"  )

			parser.add_argument(
			    "--n_obs"						,  type=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="How many observations. Defaults to all."  )
			parser.add_argument(
			    "--num_return_sequences"						,  type=SCREAMING_SNAKE_CASE_						,  default=1						,  required=SCREAMING_SNAKE_CASE_						,  help="How many sequences to return"  )
			parser.add_argument(
			    "--sync_timeout"						,  type=SCREAMING_SNAKE_CASE_						,  default=600						,  required=SCREAMING_SNAKE_CASE_						,  help="How long should master process wait for other processes to finish."						,  )
			parser.add_argument("--src_lang"						,  type=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_  )
			parser.add_argument("--tgt_lang"						,  type=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_  )
			parser.add_argument(
			    "--prefix"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_						,  help="will be added to the begininng of src examples"  )
			parser.add_argument("--fp16"						,  action="store_true"  )
			parser.add_argument("--debug"						,  action="store_true"  )
			lowercase__					  =				time.time()
			lowercase__  ,       lowercase__					  =				parser.parse_known_args()
			lowercase__					  =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE_  )
			if generate_kwargs and args.local_rank <= 0:
						print(f'''parsed the following generate kwargs: {generate_kwargs}'''  )
			lowercase__					  =				Path(args.save_dir + "_tmp"  )
			Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )  # this handles locking.
			lowercase__					  =				list(json_save_dir.glob("rank_*.json"  )  )
			if intermediate_files:
						raise ValueError(f'''Found files at {json_save_dir} please move or remove them.'''  )
						# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
			lowercase__					  =				{}
			if args.src_lang is not None:
						lowercase__					  =				args.src_lang
			if args.tgt_lang is not None:
						lowercase__					  =				args.tgt_lang

			Path(args.save_dir  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
			lowercase__  ,       lowercase__					  =				eval_data_dir(
			    args.data_dir						,  SCREAMING_SNAKE_CASE_						,  args.model_name						,  type_path=args.type_path						,  bs=args.bs						,  fpaa=args.fpaa						,  task=args.task						,  local_rank=args.local_rank						,  n_obs=args.n_obs						,  max_source_length=args.max_source_length						,  num_return_sequences=args.num_return_sequences						,  prefix=args.prefix						,  dataset_kwargs=SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_						,  )

			if args.local_rank <= 0:
						lowercase__					  =				Path(args.save_dir  )
						save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				gather_results_from_each_node(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  args.sync_timeout  )
						lowercase__					  =				combine_partial_results(SCREAMING_SNAKE_CASE_  )
						if args.num_return_sequences > 1:
									lowercase__					  =				save_dir.joinpath("pseudolabel_results.json"  )
									print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''  )
									save_json(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
									return
						lowercase__					  =				Path(args.data_dir  ).joinpath(args.type_path + ".target"  )
						with open(SCREAMING_SNAKE_CASE_  ) as f:
									lowercase__					  =				[x.rstrip() for x in f.readlines()][: len(SCREAMING_SNAKE_CASE_  )]

						# Calculate metrics, save metrics,  and save _generations.txt
						lowercase__					  =				"translation" in args.task
						lowercase__					  =				calculate_bleu if calc_bleu else calculate_rouge
						lowercase__					  =				"bleu" if calc_bleu else "rouge"
						lowercase__					  =				score_fn(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				len(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				time.time() - start_time
						lowercase__					  =				round(runtime / metrics["n_obs"]						,  4  )
						lowercase__					  =				num_replicas
						# TODO(@stas00): add whatever metadata to metrics
						lowercase__					  =				save_dir.joinpath(f'''{args.type_path}_{metric_name}.json'''  )
						save_json(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  indent=SCREAMING_SNAKE_CASE_  )
						print(SCREAMING_SNAKE_CASE_  )
						write_txt_file(SCREAMING_SNAKE_CASE_						,  save_dir.joinpath(f'''{args.type_path}_generations.txt'''  )  )
						if args.debug:
									write_txt_file(SCREAMING_SNAKE_CASE_						,  save_dir.joinpath(f'''{args.type_path}.target'''  )  )
						else:
									shutil.rmtree(SCREAMING_SNAKE_CASE_  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				[]
			for partial_result in partial_results:
						records.extend(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				sorted(SCREAMING_SNAKE_CASE_						,  key=lambda SCREAMING_SNAKE_CASE_  : x["id"]  )
			lowercase__					  =				[x["pred"] for x in records]
			return preds



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			# WAIT FOR lots of .json files
			lowercase__					  =				time.time()
			logger.info("waiting for all nodes to finish"  )
			lowercase__					  =				None
			while (time.time() - start_wait) < timeout:
						lowercase__					  =				list(save_dir.glob("rank_*.json"  )  )
						if len(SCREAMING_SNAKE_CASE_  ) < num_replicas:
									continue
						try:
									# make sure all json files are fully saved
									lowercase__					  =				lmap(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
									return json_data
						except JSONDecodeError:
									continue
			else:
						raise TimeoutError("Rank 0 gave up on waiting for other processes"  )
			# Unreachable


if __name__ == "__main__":
							# Usage for MT:
							run_generate()









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

import os
from pathlib import Path



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				{
			    "en": "Machine learning is great, isn't it?",
			    "ru": "Машинное обучение - это здорово, не так ли?",
			    "de": "Maschinelles Lernen ist großartig, oder?",
			}

			# BLUE scores as follows:
			# "pair": [fairseq, transformers]
			lowercase__					  =				{
			    "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
			    "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
			    "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
			    "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
			}
			lowercase__					  =				f'''{src_lang}-{tgt_lang}'''

			lowercase__					  =				f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
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

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
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 $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## 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

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
			os.makedirs(SCREAMING_SNAKE_CASE_						,  exist_ok=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				os.path.join(SCREAMING_SNAKE_CASE_						,  "README.md"  )
			print(f'''Generating {path}'''  )
			with open(SCREAMING_SNAKE_CASE_						,  "w"						,  encoding="utf-8"  ) as f:
						f.write(SCREAMING_SNAKE_CASE_  )


# make sure we are under the root of the project
lowercase_								=     Path(__file__).resolve().parent.parent.parent
lowercase_								=     repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowercase_				,    lowercase_				,    lowercase_								=     model_name.split("""-""")
							lowercase_								=     model_cards_dir / """facebook""" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)




