






import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


_a				     =  namedtuple('''ExampleNamedTuple''', '''a b c''')





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






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







							_UpperCAmelCase     =      torch.randn(5	,   2   )
							_UpperCAmelCase     =      torch.device('cuda'   ) if torch.cuda.is_available() else torch.device('cpu'   )

							_UpperCAmelCase     =      send_to_device(_UpperCAmelCase	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta.cpu()	,   _UpperCAmelCase   )   )

							_UpperCAmelCase     =      send_to_device((tensor, [tensor, tensor], 1)	,   _UpperCAmelCase   )
							self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta[0].cpu()	,   _UpperCAmelCase   )   )
							self.assertIsInstance(resulta[1]	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta[1][0].cpu()	,   _UpperCAmelCase   )   )
							self.assertTrue(torch.equal(resulta[1][1].cpu()	,   _UpperCAmelCase   )   )
							self.assertEqual(resulta[2]	,   1   )

							_UpperCAmelCase     =      send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}	,   _UpperCAmelCase   )
							self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta['a'].cpu()	,   _UpperCAmelCase   )   )
							self.assertIsInstance(resulta['b']	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta['b'][0].cpu()	,   _UpperCAmelCase   )   )
							self.assertTrue(torch.equal(resulta['b'][1].cpu()	,   _UpperCAmelCase   )   )
							self.assertEqual(resulta['c']	,   1   )

							_UpperCAmelCase     =      send_to_device(ExampleNamedTuple(a=_UpperCAmelCase	,   b=[tensor, tensor]	,   c=1   )	,   _UpperCAmelCase   )
							self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta.a.cpu()	,   _UpperCAmelCase   )   )
							self.assertIsInstance(resulta.b	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta.b[0].cpu()	,   _UpperCAmelCase   )   )
							self.assertTrue(torch.equal(resulta.b[1].cpu()	,   _UpperCAmelCase   )   )
							self.assertEqual(resulta.c	,   1   )

							_UpperCAmelCase     =      send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}   )	,   _UpperCAmelCase   )
							self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta['a'].cpu()	,   _UpperCAmelCase   )   )
							self.assertIsInstance(resulta['b']	,   _UpperCAmelCase   )
							self.assertTrue(torch.equal(resulta['b'][0].cpu()	,   _UpperCAmelCase   )   )
							self.assertTrue(torch.equal(resulta['b'][1].cpu()	,   _UpperCAmelCase   )   )
							self.assertEqual(resulta['c']	,   1   )

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







							with self.assertRaises(_UpperCAmelCase   ) as cm:
													_UpperCAmelCase     =      recursively_apply(torch.tensor	,   (torch.tensor(1   ), 1)	,   error_on_other_type=_UpperCAmelCase   )
							self.assertEqual(
							    str(cm.exception   )	,   'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'	,   )

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







							_UpperCAmelCase     =      torch.tensor([1, 2, 3, 4, 5]   )
							self.assertEqual(listify(_UpperCAmelCase   )	,   [1, 2, 3, 4, 5]   )

							_UpperCAmelCase     =      torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )
							self.assertEqual(listify(_UpperCAmelCase   )	,   [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )

							_UpperCAmelCase     =      torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]   )
							self.assertEqual(
							    listify(_UpperCAmelCase   )	,   [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]   )

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







							with patch_environment(aa=1	,   BB=2   ):
													self.assertEqual(os.environ.get('AA'   )	,   '1'   )
													self.assertEqual(os.environ.get('BB'   )	,   '2'   )

							self.assertNotIn('AA'	,   os.environ   )
							self.assertNotIn('BB'	,   os.environ   )

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







							_UpperCAmelCase     =      RegressionModel()
							_UpperCAmelCase     =      model.forward
							_UpperCAmelCase     =      convert_outputs_to_fpaa(model.forward   )
							_UpperCAmelCase     =      extract_model_from_parallel(_UpperCAmelCase	,   keep_fpaa_wrapper=_UpperCAmelCase   )
							_UpperCAmelCase     =      pickle.dumps(_UpperCAmelCase   )

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







							_UpperCAmelCase     =      RegressionModel()
							_UpperCAmelCase     =      model.forward
							_UpperCAmelCase     =      torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
							_UpperCAmelCase     =      convert_outputs_to_fpaa(model.forward   )
							_UpperCAmelCase     =      extract_model_from_parallel(_UpperCAmelCase	,   keep_fpaa_wrapper=_UpperCAmelCase   )
							_UpperCAmelCase     =      pickle.dumps(_UpperCAmelCase   )

	@require_cuda
	@require_torch_min_version(version='2.0'   )
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      RegressionModel()
							_UpperCAmelCase     =      model.forward
							_UpperCAmelCase     =      torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
							_UpperCAmelCase     =      convert_outputs_to_fpaa(model.forward   )
							_UpperCAmelCase     =      torch.compile(model.forward	,   backend='inductor'   )
							_UpperCAmelCase     =      torch.randn(4	,   10   ).cuda()
							_UpperCAmelCase     =      model(_UpperCAmelCase   )

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







							_UpperCAmelCase     =      RegressionModel()
							# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
							_UpperCAmelCase     =      torch.nn.parallel.DataParallel(_UpperCAmelCase   )
							_UpperCAmelCase     =      extract_model_from_parallel(_UpperCAmelCase   )

							self.assertEqual(_UpperCAmelCase	,   _UpperCAmelCase   )

	@require_torch_min_version(version='2.0'   )
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      RegressionModel()
							_UpperCAmelCase     =      torch.compile(_UpperCAmelCase   )

							# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
							_UpperCAmelCase     =      torch.nn.parallel.DataParallel(_UpperCAmelCase   )
							_UpperCAmelCase     =      torch.compile(_UpperCAmelCase   )
							_UpperCAmelCase     =      extract_model_from_parallel(_UpperCAmelCase   )

							self.assertEqual(compiled_model._orig_mod	,   compiled_model_unwrapped._orig_mod   )

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







							self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]   )]   )	,   torch.device('cpu'   )   )
							self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]   )}   )	,   torch.device('cpu'   )   )
							self.assertIsNone(find_device([1, 'a']   )   )












# 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)
