






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def __A   (				__lowerCAmelCase=2			,		__lowerCAmelCase=True			,		__lowerCAmelCase=False     )->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase     =      Accelerator(step_scheduler_with_optimizer=__lowerCAmelCase			,		split_batches=__lowerCAmelCase     )
						_UpperCAmelCase     =      torch.nn.Linear(2			,		4     )
						_UpperCAmelCase     =      torch.optim.AdamW(model.parameters()			,		lr=1.0     )
						_UpperCAmelCase     =      torch.optim.lr_scheduler.OneCycleLR(__lowerCAmelCase			,		max_lr=0.01			,		steps_per_epoch=2			,		epochs=1     )
						_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      accelerator.prepare(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

						# Optimizer has stepped
						scheduler.step()
						if step_scheduler_with_optimizer or (num_processes == 1):
												assert (
												    scheduler.scheduler.last_epoch == num_processes
												), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
						else:
												assert (
												    scheduler.scheduler.last_epoch != num_processes
												), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""


def __A   (				__lowerCAmelCase=2			,		__lowerCAmelCase=True			,		__lowerCAmelCase=False     )->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase     =      Accelerator(step_scheduler_with_optimizer=__lowerCAmelCase			,		split_batches=__lowerCAmelCase     )
						_UpperCAmelCase     =      torch.nn.Linear(2			,		4     )
						_UpperCAmelCase     =      torch.optim.AdamW(model.parameters()			,		lr=1.0     )
						_UpperCAmelCase     =      torch.optim.lr_scheduler.LambdaLR(__lowerCAmelCase			,		lr_lambda=lambda __lowerCAmelCase     : 1 - n / 10     )
						_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      accelerator.prepare(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

						# Optimizer has stepped
						_UpperCAmelCase     =      False
						scheduler.step()
						_UpperCAmelCase     =      1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
						assert (
						    scheduler.get_last_lr()[0] == expected_lr
						), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

						# Optimizer has not stepped
						_UpperCAmelCase     =      True
						scheduler.step()
						if not step_scheduler_with_optimizer:
												_UpperCAmelCase     =      1 - 2 / 10
						assert (
						    scheduler.get_last_lr()[0] == expected_lr
						), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""


def __A   (				__lowerCAmelCase = 2     )->  Union[str, Any]:





						"""simple docstring"""



						from transformers import get_linear_schedule_with_warmup

						_UpperCAmelCase     =      [1, 2, 4]
						for num_steps in steps:
												_UpperCAmelCase     =      GradientAccumulationPlugin(num_steps=__lowerCAmelCase			,		adjust_scheduler=num_steps > 1     )
												_UpperCAmelCase     =      Accelerator(gradient_accumulation_plugin=__lowerCAmelCase     )
												_UpperCAmelCase     =      torch.nn.Linear(2			,		4     )
												_UpperCAmelCase     =      torch.optim.AdamW(model.parameters()			,		lr=10.0     )
												_UpperCAmelCase     =      get_linear_schedule_with_warmup(optimizer=__lowerCAmelCase			,		num_warmup_steps=0			,		num_training_steps=20     )

												_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      accelerator.prepare(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

												for i in range(10 * num_steps     ):
																		with accelerator.accumulate(__lowerCAmelCase     ):
																								optimizer.step()
																								scheduler.step()

																		if i == (10 * num_steps - 2):
																								assert (
																								    scheduler.get_last_lr()[0] != 0
																								), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
												assert (
												    scheduler.get_last_lr()[0] == 0
												), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
												GradientState._reset_state()





@require_cpu
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






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







							debug_launcher(partial(UpperCAmelCase	,   num_processes=1   )	,   num_processes=1   )
							debug_launcher(partial(UpperCAmelCase	,   num_processes=1	,   split_batches=UpperCAmelCase   )	,   num_processes=1   )

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







							debug_launcher(partial(UpperCAmelCase	,   num_processes=1   )	,   num_processes=1   )
							debug_launcher(partial(UpperCAmelCase	,   num_processes=1	,   split_batches=UpperCAmelCase   )	,   num_processes=1   )

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







							debug_launcher(partial(UpperCAmelCase	,   num_processes=1	,   step_scheduler_with_optimizer=UpperCAmelCase   )	,   num_processes=1   )

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







							debug_launcher(partial(UpperCAmelCase	,   num_processes=1	,   step_scheduler_with_optimizer=UpperCAmelCase   )	,   num_processes=1   )

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







							AcceleratorState._reset_state(UpperCAmelCase   )
							debug_launcher(UpperCAmelCase   )
							debug_launcher(partial(UpperCAmelCase	,   num_processes=1	,   split_batches=UpperCAmelCase   )	,   num_processes=1   )

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







							AcceleratorState._reset_state(UpperCAmelCase   )
							debug_launcher(UpperCAmelCase   )
							debug_launcher(partial(UpperCAmelCase	,   num_processes=1	,   split_batches=UpperCAmelCase   )	,   num_processes=1   )

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







							AcceleratorState._reset_state(UpperCAmelCase   )
							debug_launcher(partial(UpperCAmelCase	,   step_scheduler_with_optimizer=UpperCAmelCase   )   )

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







							AcceleratorState._reset_state(UpperCAmelCase   )
							debug_launcher(partial(UpperCAmelCase	,   step_scheduler_with_optimizer=UpperCAmelCase   )   )

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







							AcceleratorState._reset_state(UpperCAmelCase   )
							debug_launcher(partial(UpperCAmelCase	,   num_processes=1   )   )
							debug_launcher(UpperCAmelCase   )












# 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)
