


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

import os
from pathlib import Path



def     __lowerCAmelCase						(							snake_case	:    Tuple						,      snake_case	:    int						,      snake_case	:    Tuple )       ->							Dict:
   __lowerCamelCase:				int     =	{
       """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]
   __lowerCamelCase:				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"""],
   }
   __lowerCamelCase:				str     =	f'{src_lang}-{tgt_lang}'

   __lowerCamelCase:				Optional[Any]     =	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(snake_case						,      exist_ok=snake_case )
   __lowerCamelCase:				Any     =	os.path.join(snake_case						,      """README.md""" )
   print(f'Generating {path}' )
   with open(snake_case						,      """w"""						,      encoding="""utf-8""" ) as f:
      f.write(snake_case )


# make sure we are under the root of the project
_A		:				Optional[int]									=       Path(__file__).resolve().parent.parent.parent
_A		:				Optional[Any]									=       repo_dir / '''model_cards'''

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






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
   import tensorflow as tf

   from transformers.models.distilbert.modeling_tf_distilbert import (
       TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFDistilBertForMaskedLM,
       TFDistilBertForMultipleChoice,
       TFDistilBertForQuestionAnswering,
       TFDistilBertForSequenceClassification,
       TFDistilBertForTokenClassification,
       TFDistilBertModel,
   )



class        a					:





     def __init__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	):
        __lowerCamelCase:				Optional[int]     =	parent
        __lowerCamelCase:				Dict     =	13
        __lowerCamelCase:				List[Any]     =	7
        __lowerCamelCase:				Optional[int]     =	True
        __lowerCamelCase:				str     =	True
        __lowerCamelCase:				Dict     =	False
        __lowerCamelCase:				Union[str, Any]     =	True
        __lowerCamelCase:				Any     =	99
        __lowerCamelCase:				Dict     =	32
        __lowerCamelCase:				str     =	2
        __lowerCamelCase:				Optional[int]     =	4
        __lowerCamelCase:				Optional[Any]     =	37
        __lowerCamelCase:				Union[str, Any]     =	"""gelu"""
        __lowerCamelCase:				List[Any]     =	0.1
        __lowerCamelCase:				Dict     =	0.1
        __lowerCamelCase:				str     =	512
        __lowerCamelCase:				Dict     =	16
        __lowerCamelCase:				List[str]     =	2
        __lowerCamelCase:				Optional[int]     =	0.02
        __lowerCamelCase:				Dict     =	3
        __lowerCamelCase:				Optional[int]     =	4
        __lowerCamelCase:				Tuple     =	None





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				int     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				Any     =	None
        if self.use_input_mask:
           __lowerCamelCase:				Optional[int]     =	random_attention_mask([self.batch_size, self.seq_length]							)

        __lowerCamelCase:				Optional[Any]     =	None
        __lowerCamelCase:				Optional[Any]     =	None
        __lowerCamelCase:				List[str]     =	None
        if self.use_labels:
           __lowerCamelCase:				Any     =	ids_tensor([self.batch_size]    ,	self.type_sequence_label_size							)
           __lowerCamelCase:				List[Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.num_labels							)
           __lowerCamelCase:				Optional[int]     =	ids_tensor([self.batch_size]    ,	self.num_choices							)

        __lowerCamelCase:				Any     =	DistilBertConfig(
            vocab_size=self.vocab_size    ,	dim=self.hidden_size    ,	n_layers=self.num_hidden_layers    ,	n_heads=self.num_attention_heads    ,	hidden_dim=self.intermediate_size    ,	hidden_act=self.hidden_act    ,	dropout=self.hidden_dropout_prob    ,	attention_dropout=self.attention_probs_dropout_prob    ,	max_position_embeddings=self.max_position_embeddings    ,	initializer_range=self.initializer_range    ,	)

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
        __lowerCamelCase:				str     =	TFDistilBertModel(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}

        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	[input_ids, input_mask]

        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)

        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				Any     =	TFDistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
        }
        __lowerCamelCase:				Tuple     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.start_logits.shape    ,	(self.batch_size, self.seq_length)							)
        self.parent.assertEqual(result.end_logits.shape    ,	(self.batch_size, self.seq_length)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				Any     =	self.num_labels
        __lowerCamelCase:				Optional[Any]     =	TFDistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				Optional[int]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				Tuple     =	self.num_choices
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForMultipleChoice(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_    ,	1							)    ,	(1, self.num_choices, 1)							)
        __lowerCamelCase:				str     =	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_    ,	1							)    ,	(1, self.num_choices, 1)							)
        __lowerCamelCase:				List[Any]     =	{
            """input_ids""": multiple_choice_inputs_ids,
            """attention_mask""": multiple_choice_input_mask,
        }
        __lowerCamelCase:				int     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_choices)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Union[str, Any]     =	self.num_labels
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForTokenClassification(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Dict     =	self.prepare_config_and_inputs()
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Tuple     =	config_and_inputs
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict



@require_tf
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Any						      =	(
         (
             TFDistilBertModel,
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
             TFDistilBertForTokenClassification,
             TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
     )
     UpperCAmelCase__ :  Dict						      =	(
         {
             "feature-extraction": TFDistilBertModel,
             "fill-mask": TFDistilBertForMaskedLM,
             "question-answering": TFDistilBertForQuestionAnswering,
             "text-classification": TFDistilBertForSequenceClassification,
             "token-classification": TFDistilBertForTokenClassification,
             "zero-shot": TFDistilBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Tuple						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	TFDistilBertModelTester(self							)
        __lowerCamelCase:				Union[str, Any]     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	dim=37							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Dict     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
           __lowerCamelCase:				List[str]     =	TFDistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



@require_tf
class        a					(			unittest.TestCase   ):





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				int     =	TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
        __lowerCamelCase:				Optional[Any]     =	tf.constant([[0, 1, 2, 3, 4, 5]]							)
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_							)[0]

        __lowerCamelCase:				Union[str, Any]     =	[1, 6, 768]
        self.assertEqual(output.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	tf.constant(
            [
                [
                    [0.1926_1885, -0.1373_2955, 0.411_9799],
                    [0.2215_0156, -0.0742_2661, 0.3903_7204],
                    [0.2275_6018, -0.089_6414, 0.370_1467],
                ]
            ]							)
        tf.debugging.assert_near(output[:, :3, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)


