






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

import os
from pathlib import Path







def     a_   (__A				,       __A				,       __A      )				->						List[str]:



					"""simple docstring"""
					__a					:						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					:						List[Any]      =					{
					    "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					:						int      =					f'{src_lang}-{tgt_lang}'

					__a					:						Tuple      =					f'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- 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)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$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\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
					os.makedirs(__A				,       exist_ok=__A      )
					__a					:						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__      			=						Path(__file__).resolve().parent.parent.parent
UpperCAmelCase__      			=						repo_dir / '''model_cards'''

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













import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)




class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						def __init__(self:					Optional[int] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=13 ,					__UpperCAmelCase:					Any=7 ,					__UpperCAmelCase:					List[str]=True ,					__UpperCAmelCase:					Optional[int]=True ,					__UpperCAmelCase:					Dict=True ,					__UpperCAmelCase:					Optional[Any]=True ,					__UpperCAmelCase:					Optional[int]=99 ,					__UpperCAmelCase:					Optional[Any]=32 ,					__UpperCAmelCase:					int=5 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Optional[int]=37 ,					__UpperCAmelCase:					int="gelu" ,					__UpperCAmelCase:					Tuple=0.1 ,					__UpperCAmelCase:					Any=0.1 ,					__UpperCAmelCase:					Union[str, Any]=512 ,					__UpperCAmelCase:					Optional[Any]=16 ,					__UpperCAmelCase:					List[Any]=2 ,					__UpperCAmelCase:					str=0.02 ,					__UpperCAmelCase:					int=4 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Tuple      =					parent
											__a					:						int      =					batch_size
											__a					:						Optional[int]      =					seq_length
											__a					:						List[Any]      =					is_training
											__a					:						Tuple      =					use_attention_mask
											__a					:						Optional[int]      =					use_token_type_ids
											__a					:						Tuple      =					use_labels
											__a					:						str      =					vocab_size
											__a					:						Union[str, Any]      =					hidden_size
											__a					:						List[str]      =					num_hidden_layers
											__a					:						Optional[int]      =					num_attention_heads
											__a					:						Any      =					intermediate_size
											__a					:						Any      =					hidden_act
											__a					:						List[str]      =					hidden_dropout_prob
											__a					:						Dict      =					attention_probs_dropout_prob
											__a					:						Tuple      =					max_position_embeddings
											__a					:						Optional[int]      =					type_vocab_size
											__a					:						Tuple      =					type_sequence_label_size
											__a					:						List[Any]      =					initializer_range
											__a					:						int      =					num_choices



						def        UpperCAmelCase__					(self:					Tuple   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

											__a					:						Dict      =					None
											if self.use_attention_mask:
																__a					:						List[Any]      =					random_attention_mask([self.batch_size, self.seq_length]   )

											__a					:						Optional[int]      =					None
											if self.use_token_type_ids:
																__a					:						Dict      =					ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size   )

											__a					:						Dict      =					RobertaConfig(
											    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=__UpperCAmelCase ,					initializer_range=self.initializer_range ,					)

											return config, input_ids, token_type_ids, attention_mask



						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Dict      =					config_and_inputs
											__a					:						str      =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
											return config, inputs_dict



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											__a					:						Optional[Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					config_and_inputs

											__a					:						int      =					True
											__a					:						List[str]      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					vocab_size=2   )

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




@require_flax
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      True

						snake_case__        =      (
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)



						def        UpperCAmelCase__					(self:					Dict   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Tuple      =					FlaxRobertaModelTester(self   )



						@slow
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     List[Any]:




											'''simple docstring'''



											for model_class_name in self.all_model_classes:
																__a					:						int      =					model_class_name.from_pretrained("roberta-base" ,					from_pt=__UpperCAmelCase   )
																__a					:						Tuple      =					model(np.ones((1, 1)   )   )
																self.assertIsNotNone(__UpperCAmelCase   )





