


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

import os
from pathlib import Path



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   Any:
      a__	:  Optional[Any]     =   {
          "en": "Machine learning is great, isn't it?",
          "ru": "Машинное обучение - это здорово, не так ли?",
          "de": "Maschinelles Lernen ist großartig, oder?",
      }

      # BLUE scores as follows:
      # "pair": [fairseq, transformers]
      a__	:  Dict     =   {
          "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
          "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
          "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
          "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
      }
      a__	:  Tuple     =   f'''{src_lang}-{tgt_lang}'''

      a__	:  int     =   f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
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

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
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 $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## 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

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
      os.makedirs(__a			,				exist_ok=__a      )
      a__	:  Union[str, Any]     =   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
UpperCamelCase					:       Tuple			    =     Path(__file__).resolve().parent.parent.parent
UpperCamelCase					:       Optional[int]			    =     repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    UpperCamelCase    ,							UpperCamelCase    ,							UpperCamelCase					:       Optional[Any]			    =     model_name.split("""-""")
    UpperCamelCase					:       Dict			    =     model_cards_dir / """facebook""" / model_name
    write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.xglm.modeling_tf_xglm import (
           TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFXGLMForCausalLM,
           TFXGLMModel,
       )
@require_tf
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     XGLMConfig
     __magic_name__           =     {}
     __magic_name__           =     "gelu"







     def __init__(			self   ,						snake_case__   ,						snake_case__=14   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=2   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=0.02   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							parent
            _lowerCAmelCase     :				Tuple       =							batch_size
            _lowerCAmelCase     :				Optional[Any]       =							seq_length
            _lowerCAmelCase     :				Dict       =							is_training
            _lowerCAmelCase     :				str       =							use_input_mask
            _lowerCAmelCase     :				Tuple       =							use_labels
            _lowerCAmelCase     :				Union[str, Any]       =							vocab_size
            _lowerCAmelCase     :				Any       =							d_model
            _lowerCAmelCase     :				Dict       =							num_hidden_layers
            _lowerCAmelCase     :				Optional[Any]       =							num_attention_heads
            _lowerCAmelCase     :				Tuple       =							ffn_dim
            _lowerCAmelCase     :				Tuple       =							activation_function
            _lowerCAmelCase     :				int       =							activation_dropout
            _lowerCAmelCase     :				Optional[Any]       =							attention_dropout
            _lowerCAmelCase     :				Union[str, Any]       =							max_position_embeddings
            _lowerCAmelCase     :				List[Any]       =							initializer_range
            _lowerCAmelCase     :				List[str]       =							None
            _lowerCAmelCase     :				Any       =							0
            _lowerCAmelCase     :				Any       =							2
            _lowerCAmelCase     :				Optional[Any]       =							1







     def a						(			self ):

            '''simple docstring'''

            return XGLMConfig.from_pretrained('facebook/xglm-564M' )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							tf.clip_by_value(
                ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )   ,						clip_value_min=0   ,						clip_value_max=3 )

            _lowerCAmelCase     :				str       =							None
            if self.use_input_mask:
                   _lowerCAmelCase     :				int       =							random_attention_mask([self.batch_size, self.seq_length] )

            _lowerCAmelCase     :				str       =							self.get_config()

            _lowerCAmelCase     :				Union[str, Any]       =							floats_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2 )

            return (
                config,
                input_ids,
                input_mask,
                head_mask,
            )







     def a						(			self ):

            '''simple docstring'''

            return XGLMConfig(
                vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						num_layers=self.num_hidden_layers   ,						attention_heads=self.num_attention_heads   ,						ffn_dim=self.ffn_dim   ,						activation_function=self.activation_function   ,						activation_dropout=self.activation_dropout   ,						attention_dropout=self.attention_dropout   ,						max_position_embeddings=self.max_position_embeddings   ,						initializer_range=self.initializer_range   ,						use_cache=snake_case__   ,						bos_token_id=self.bos_token_id   ,						eos_token_id=self.eos_token_id   ,						pad_token_id=self.pad_token_id   ,						return_dict=snake_case__   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.prepare_config_and_inputs()

            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				Dict       =							config_and_inputs

            _lowerCAmelCase     :				Optional[int]       =							{
                'input_ids': input_ids,
                'head_mask': head_mask,
            }

            return config, inputs_dict
@require_tf
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     __magic_name__           =     (TFXGLMForCausalLM,) if is_tf_available() else ()
     __magic_name__           =     (
         {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
     )
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							TFXGLMModelTester(self )
            _lowerCAmelCase     :				Dict       =							ConfigTester(self   ,						config_class=snake_case__   ,						n_embd=37 )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				Optional[Any]       =							TFXGLMModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )







     @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
     def a						(			self ):

            '''simple docstring'''

            super().test_resize_token_embeddings()
@require_tf
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @slow
     def a						(			self   ,						snake_case__=True ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				str       =							tf.convert_to_tensor([[2, 268, 9865]]   ,						dtype=tf.intaa )  # The dog
            # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
            # fmt: off
            _lowerCAmelCase     :				str       =							[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
            # fmt: on
            _lowerCAmelCase     :				Optional[int]       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						num_beams=1 )
            if verify_outputs:
                   self.assertListEqual(output_ids[0].numpy().tolist()   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

            tf.random.set_seed(0 )
            _lowerCAmelCase     :				Optional[int]       =							tokenizer('Today is a nice day and'   ,						return_tensors='tf' )
            _lowerCAmelCase     :				Dict       =							tokenized.input_ids
            # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
            with tf.device(':/CPU:0' ):
                   _lowerCAmelCase     :				Dict       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						seed=[7, 0] )
            _lowerCAmelCase     :				List[Any]       =							tokenizer.decode(output_ids[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				Tuple       =							(
                'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
            )
            self.assertEqual(snake_case__   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

            _lowerCAmelCase     :				Tuple       =							'left'

            # use different length sentences to test batching
            _lowerCAmelCase     :				Optional[int]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When',
                'Hello, my dog is a little',
            ]

            _lowerCAmelCase     :				Tuple       =							tokenizer(snake_case__   ,						return_tensors='tf'   ,						padding=snake_case__ )
            _lowerCAmelCase     :				Any       =							inputs['input_ids']

            _lowerCAmelCase     :				Any       =							model.generate(input_ids=snake_case__   ,						attention_mask=inputs['attention_mask']   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Dict       =							tokenizer(sentences[0]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				int       =							tokenizer(sentences[1]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Optional[Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.batch_decode(snake_case__   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Any       =							tokenizer.decode(output_non_padded[0]   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.decode(output_padded[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
                'a single',
                'Hello, my dog is a little bit of a shy one, but he is very friendly',
            ]
            self.assertListEqual(snake_case__   ,						snake_case__ )
            self.assertListEqual(snake_case__   ,						[non_padded_sentence, padded_sentence] )

