
"""simple docstring"""


import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class  __lowerCamelCase  ( unittest.TestCase			):
				a__:      Union[str, Any]          =    JukeboxTokenizer
				a__:      Dict          =    {
				    'artist': 'Zac Brown Band',
				    'genres': 'Country',
				    'lyrics': 'I met a traveller from an antique land,\n        Who said "Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ',
				}




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							import torch

							lowerCamelCase_					=  JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''   )
							lowerCamelCase_					=  tokenizer(**self.metas   )['''input_ids''']
							# fmt: off
							lowerCamelCase_					=  [
							    torch.tensor([[
							        0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
							        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
							        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
							        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
							        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
							        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
							        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
							        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
							        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
							        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
							        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
							        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
							        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
							        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
							        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
							        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
							        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
							        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
							        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
							        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
							        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
							        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
							        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
							        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
							        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
							        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
							        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
							        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
							        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
							        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
							        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
							        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
							        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
							        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
							        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
							        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
							        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
							        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
							        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
							        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
							        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
							        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
							        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
							        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
							        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
							        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
							        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
							        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
							        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
							        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
							        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
							        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
							        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
							        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
							        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
							        76, 76]]   ),
							    torch.tensor([[0, 0, 0, 1069, 11]]   ),
							    torch.tensor([[0, 0, 0, 1069, 11]]   ),
							]
							# fmt: on
							self.assertTrue(torch.allclose(tokens[0]							,						EXPECTED_OUTPUT[0]   )   )
							self.assertTrue(torch.allclose(tokens[1]							,						EXPECTED_OUTPUT[1]   )   )
							self.assertTrue(torch.allclose(tokens[2]							,						EXPECTED_OUTPUT[2]   )   )





				@require_torch
				def        UpperCAmelCase__						(				self   ):
							import torch

							lowerCamelCase_					=  JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''   )
							lowerCamelCase_					=  tokenizer(**self.metas   )['''input_ids''']
							# fmt: off
							lowerCamelCase_					=  [
							    torch.tensor([[
							        0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
							        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
							        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
							        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
							        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
							        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
							        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
							        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
							        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
							        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
							        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
							        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
							        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
							        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
							        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
							        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
							        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
							        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
							        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
							        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
							        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
							        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
							        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
							        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
							        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
							        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
							        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
							        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
							        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
							        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
							        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
							        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
							        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
							        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
							        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
							        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
							        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
							        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
							        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
							        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
							        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
							        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
							        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
							        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
							        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
							        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
							        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
							        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
							        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
							        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
							        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
							        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
							        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
							        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
							        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
							        77, 77, 77, 77, 77, 77]]   ),
							    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]   ),
							    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]   ),
							]
							# fmt: on
							self.assertTrue(torch.allclose(tokens[0]							,						EXPECTED_OUTPUT[0]   )   )
							self.assertTrue(torch.allclose(tokens[1]							,						EXPECTED_OUTPUT[1]   )   )
							self.assertTrue(torch.allclose(tokens[2]							,						EXPECTED_OUTPUT[2]   )   )





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

import os
from pathlib import Path

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  {
						    """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]
						__lowerCAmelCase     =  {
						    """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],
						}
						__lowerCAmelCase     =  f"""{src_lang}-{tgt_lang}"""

						__lowerCAmelCase     =  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=_lowerCAmelCase ,    exist_ok=_lowerCAmelCase						)
						__lowerCAmelCase     =  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
SCREAMING_SNAKE_CASE_					     =	Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_					     =	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"]:
			SCREAMING_SNAKE_CASE_					     =	model_cards_dir / '''allenai''' / model_name
			write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)
