






import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode


def __A   (				__lowerCAmelCase			,		**__lowerCAmelCase     )->  Optional[int]:





						"""simple docstring"""



						return RunBeamCommand(
						    args.dataset			,		args.name			,		args.cache_dir			,		args.beam_pipeline_options			,		args.data_dir			,		args.all_configs			,		args.save_info or args.save_infos			,		args.ignore_verifications			,		args.force_redownload			,		**SCREAMING_SNAKE_CASE_			,		)





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	@staticmethod
	def 	UpperCamelCase   (							UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      parser.add_parser('run_beam'	,   help='Run a Beam dataset processing pipeline'   )
							run_beam_parser.add_argument('dataset'	,   type=__A	,   help='Name of the dataset to download'   )
							run_beam_parser.add_argument('--name'	,   type=__A	,   default=__A	,   help='Dataset config name'   )
							run_beam_parser.add_argument(
							    '--cache_dir'	,   type=__A	,   default=__A	,   help='Cache directory where the datasets are stored'	,   )
							run_beam_parser.add_argument(
							    '--beam_pipeline_options'	,   type=__A	,   default=''	,   help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'	,   )
							run_beam_parser.add_argument(
							    '--data_dir'	,   type=__A	,   default=__A	,   help='Can be used to specify a manual directory to get the files from'	,   )
							run_beam_parser.add_argument('--all_configs'	,   action='store_true'	,   help='Test all dataset configurations'   )
							run_beam_parser.add_argument('--save_info'	,   action='store_true'	,   help='Save the dataset infos file'   )
							run_beam_parser.add_argument(
							    '--ignore_verifications'	,   action='store_true'	,   help='Run the test without checksums and splits checks'   )
							run_beam_parser.add_argument('--force_redownload'	,   action='store_true'	,   help='Force dataset redownload'   )
							# aliases
							run_beam_parser.add_argument('--save_infos'	,   action='store_true'	,   help='alias for save_info'   )
							run_beam_parser.set_defaults(func=__A   )

	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      dataset
							_UpperCAmelCase     =      name
							_UpperCAmelCase     =      cache_dir
							_UpperCAmelCase     =      beam_pipeline_options
							_UpperCAmelCase     =      data_dir
							_UpperCAmelCase     =      all_configs
							_UpperCAmelCase     =      save_infos
							_UpperCAmelCase     =      ignore_verifications
							_UpperCAmelCase     =      force_redownload
							_UpperCAmelCase     =      config_kwargs

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							import apache_beam as beam

							if self._name is not None and self._all_configs:
													print('Both parameters `name` and `all_configs` can\'t be used at once.'   )
													exit(1   )
							_UpperCAmelCase     =      self._dataset, self._name
							_UpperCAmelCase     =      dataset_module_factory(__A   )
							_UpperCAmelCase     =      import_main_class(dataset_module.module_path   )
							_UpperCAmelCase     =      []
							if self._beam_pipeline_options:
													_UpperCAmelCase     =      beam.options.pipeline_options.PipelineOptions(
													    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','   ) if opt]   )
							else:
													_UpperCAmelCase     =      None
							if self._all_configs and len(builder_cls.BUILDER_CONFIGS   ) > 0:
													for builder_config in builder_cls.BUILDER_CONFIGS:
																			builders.append(
																			    builder_cls(
																			        config_name=builder_config.name	,   data_dir=self._data_dir	,   hash=dataset_module.hash	,   beam_options=__A	,   cache_dir=self._cache_dir	,   base_path=dataset_module.builder_kwargs.get('base_path'   )	,   )   )
							else:
													builders.append(
													    builder_cls(
													        config_name=__A	,   data_dir=self._data_dir	,   beam_options=__A	,   cache_dir=self._cache_dir	,   base_path=dataset_module.builder_kwargs.get('base_path'   )	,   **self._config_kwargs	,   )   )

							for builder in builders:
													builder.download_and_prepare(
													    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
													    if not self._force_redownload
													    else DownloadMode.FORCE_REDOWNLOAD	,   download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH   )	,   verification_mode=VerificationMode.NO_CHECKS
													    if self._ignore_verifications
													    else VerificationMode.ALL_CHECKS	,   try_from_hf_gcs=__A	,   )
													if self._save_infos:
																			builder._save_infos()

							print('Apache beam run successful.'   )

							# If save_infos=True, the dataset infos file is created next to the loaded module file.
							# Let's move it to the original directory of the dataset script, to allow the user to
							# upload them on S3 at the same time afterwards.
							if self._save_infos:
													_UpperCAmelCase     =      os.path.join(builder_cls.get_imported_module_dir()	,   config.DATASETDICT_INFOS_FILENAME   )

													_UpperCAmelCase     =      Path(__A   ).name + ".py"

													_UpperCAmelCase     =      os.path.join(__A	,   __A   )
													if os.path.isfile(__A   ):
																			_UpperCAmelCase     =      os.path.dirname(__A   )
													elif os.path.isfile(__A   ):
																			_UpperCAmelCase     =      path
													else:  # in case of a remote dataset
																			print(F"""Dataset Infos file saved at {dataset_infos_path}"""   )
																			exit(1   )

													# Move datasetinfo back to the user
													_UpperCAmelCase     =      os.path.join(__A	,   config.DATASETDICT_INFOS_FILENAME   )
													copyfile(__A	,   __A   )
													print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""   )












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

import os
from pathlib import Path


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      {
						    '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]
						_UpperCAmelCase     =      {
						    'wmt19-de-en-6-6-base': [0, 38.37],
						    'wmt19-de-en-6-6-big': [0, 39.90],
						}
						_UpperCAmelCase     =      F"""{src_lang}-{tgt_lang}"""

						_UpperCAmelCase     =      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     )
						_UpperCAmelCase     =      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
_a				     =  Path(__file__).resolve().parent.parent.parent
_a				     =  repo_dir / '''model_cards'''

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