


'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase   : Union[str, Any]             =				"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""




def 	_A  (       A=None							)  ->				int:
	if subparsers is not None:
		lowercase						:		List[Any]   					=		subparsers.add_parser("tpu-config"							,description=_description							)
	else:
		lowercase						:		List[str]   					=		argparse.ArgumentParser("Accelerate tpu-config command"							,description=_description							)
	# Core arguments
	lowercase						:		Dict   					=		parser.add_argument_group(
	    "Config Arguments"							,"Arguments that can be configured through `accelerate config`."							)
	config_args.add_argument(
	    "--config_file"							,type=A							,default=A							,help="Path to the config file to use for accelerate."							,)
	config_args.add_argument(
	    "--tpu_name"							,default=A							,help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."							,)
	config_args.add_argument(
	    "--tpu_zone"							,default=A							,help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."							,)
	lowercase						:		List[Any]   					=		parser.add_argument_group("TPU Arguments"							,"Arguments for options ran inside the TPU."							)
	pod_args.add_argument(
	    "--use_alpha"							,action="store_true"							,help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."							,)
	pod_args.add_argument(
	    "--command_file"							,default=A							,help="The path to the file containing the commands to run on the pod on startup."							,)
	pod_args.add_argument(
	    "--command"							,action="append"							,nargs="+"							,help="A command to run on the pod. Can be passed multiple times."							,)
	pod_args.add_argument(
	    "--install_accelerate"							,action="store_true"							,help="Whether to install accelerate on the pod. Defaults to False."							,)
	pod_args.add_argument(
	    "--accelerate_version"							,default="latest"							,help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."							,)
	pod_args.add_argument(
	    "--debug"							,action="store_true"							,help="If set, will print the command that would be run instead of running it."							)

	if subparsers is not None:
		parser.set_defaults(func=A							)
	return parser




def 	_A  (       A							)  ->				List[str]:
	lowercase						:		Optional[Any]   					=		None

	# Get the default from the config file if it exists.
	if args.config_file is not None or os.path.isfile(A							):
		lowercase						:		Optional[int]   					=		load_config_from_file(args.config_file							)
		if not args.command_file and defaults.command_file is not None and not args.command:
			lowercase						:		List[str]   					=		defaults.command_file
		if not args.command and defaults.commands is not None:
			lowercase						:		int   					=		defaults.commands
		if not args.tpu_name:
			lowercase						:		Any   					=		defaults.tpu_name
		if not args.tpu_zone:
			lowercase						:		Union[str, Any]   					=		defaults.tpu_zone
	if args.accelerate_version == "dev":
		lowercase						:		Union[str, Any]   					=		"git+https://github.com/huggingface/accelerate.git"
	elif args.accelerate_version == "latest":
		lowercase						:		str   					=		"accelerate -U"
	elif isinstance(parse(args.accelerate_version							)							,A							):
		lowercase						:		Any   					=		F'''accelerate=={args.accelerate_version}'''

	if not args.command_file and not args.command:
		raise ValueError("You must specify either a command file or a command to run on the pod."							)

	if args.command_file:
		with open(args.command_file							,"r"							) as f:
			lowercase						:		List[str]   					=		[f.read().splitlines()]

    # To turn list of lists into list of strings
	if isinstance(args.command[0]							,A							):
		lowercase						:		List[Any]   					=		[line for cmd in args.command for line in cmd]
	# Default to the shared folder and install accelerate
	lowercase						:		Dict   					=		["cd /usr/share"]
	if args.install_accelerate:
		new_cmd += [F'''pip install {args.accelerate_version}''']
	new_cmd += args.command
	lowercase						:		Dict   					=		"; ".join(A							)

	# Then send it to gcloud
	# Eventually try to use google-api-core to do this instead of subprocess
	lowercase						:		Dict   					=		["gcloud"]
	if args.use_alpha:
		cmd += ["alpha"]
	cmd += [
	    "compute",
	    "tpus",
	    "tpu-vm",
	    "ssh",
	    args.tpu_name,
	    "--zone",
	    args.tpu_zone,
	    "--command",
	    args.command,
	    "--worker",
	    "all",
	]
	if args.debug:
		print(F'''Running {' '.join(A							)}'''							)
		return
	subprocess.run(A							)
	print("Successfully setup pod."							)






def 	_A  (       )  ->				int:
	lowercase						:		Any   					=		tpu_command_parser()
	lowercase						:		Optional[int]   					=		parser.parse_args()

	tpu_command_launcher(A							)






'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "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]
	lowercase						:		Optional[int]   					=		{
	    "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],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, 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=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				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"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


