


"""simple docstring"""



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

import os
from pathlib import Path







def     __lowerCAmelCase		(		lowercase    :    Dict		,				lowercase    :    int		,				lowercase    :    Tuple					)      ->		List[Any]:


    """simple docstring"""





    snake_case       : 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]
    snake_case       : List[str]    =       {
        "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"],
    }
    snake_case       : List[Any]    =       F'{src_lang}-{tgt_lang}'

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


# make sure we are under the root of the project
__snake_case       =       Path(__file__).resolve().parent.parent.parent
__snake_case       =       repo_dir / """model_cards"""

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









"""simple docstring"""



import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast







class _lowerCAmelCase							( unittest.TestCase      ):







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : List[str]    =       mock.Mock()
       snake_case       : Any    =       500
       snake_case       : int    =       {}
       snake_case       : int    =       HTTPError
       snake_case       : Dict    =       {}

       # Download this model to make sure it's in the cache.
       snake_case       : Dict    =       BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"		)

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch("requests.Session.request"		,      return_value=UpperCamelCase__		) as mock_head:
           snake_case       : str    =       BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"		)
           # This check we did call the fake head request
           mock_head.assert_called()







   @require_tokenizers
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       mock.Mock()
       snake_case       : int    =       500
       snake_case       : Optional[int]    =       {}
       snake_case       : int    =       HTTPError
       snake_case       : int    =       {}

       # Download this model to make sure it's in the cache.
       snake_case       : Any    =       GPTaTokenizerFast.from_pretrained("gpt2"		)

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch("requests.Session.request"		,      return_value=UpperCamelCase__		) as mock_head:
           snake_case       : Tuple    =       GPTaTokenizerFast.from_pretrained("gpt2"		)
           # This check we did call the fake head request
           mock_head.assert_called()







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       try:
           snake_case       : Tuple    =       tempfile.mktemp()
           with open(UpperCamelCase__		,      "wb"		) as f:
               http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"		,      UpperCamelCase__		)

           snake_case       : List[str]    =       AlbertTokenizer.from_pretrained(UpperCamelCase__		)
       finally:
           os.remove(UpperCamelCase__		)

       # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
       # the current folder and have the right name.
       if os.path.isfile("tokenizer.json"		):
           # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
           return
       try:
           with open("tokenizer.json"		,      "wb"		) as f:
               http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"		,      UpperCamelCase__		)
           snake_case       : Dict    =       AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
           # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
           self.assertEqual(tokenizer.vocab_size		,      1000		)
           # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

       finally:
           os.remove("tokenizer.json"		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : int    =       AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"		)







@is_staging_test
class _lowerCAmelCase							( unittest.TestCase      ):
   __UpperCAmelCase					:			Dict				    =    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']







   @classmethod
   def 					lowerCamelCase						(      cls		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : List[str]    =       TOKEN
       HfFolder.save_token(UpperCamelCase__		)







   @classmethod
   def 					lowerCamelCase						(      cls		)      ->						Union[str, Any]:

       '''simple docstring'''




       try:
           delete_repo(token=cls._token		,      repo_id="test-tokenizer"		)
       except HTTPError:
           pass

       try:
           delete_repo(token=cls._token		,      repo_id="valid_org/test-tokenizer-org"		)
       except HTTPError:
           pass

       try:
           delete_repo(token=cls._token		,      repo_id="test-dynamic-tokenizer"		)
       except HTTPError:
           pass







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case       : Any    =       os.path.join(UpperCamelCase__		,      "vocab.txt"		)
           with open(UpperCamelCase__		,      "w"		,      encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
           snake_case       : List[str]    =       BertTokenizer(UpperCamelCase__		)

       tokenizer.push_to_hub("test-tokenizer"		,      use_auth_token=self._token		)
       snake_case       : Dict    =       BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'		)
       self.assertDictEqual(new_tokenizer.vocab		,      tokenizer.vocab		)

       # Reset repo
       delete_repo(token=self._token		,      repo_id="test-tokenizer"		)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(UpperCamelCase__		,      repo_id="test-tokenizer"		,      push_to_hub=UpperCamelCase__		,      use_auth_token=self._token		)

       snake_case       : Dict    =       BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'		)
       self.assertDictEqual(new_tokenizer.vocab		,      tokenizer.vocab		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case       : Dict    =       os.path.join(UpperCamelCase__		,      "vocab.txt"		)
           with open(UpperCamelCase__		,      "w"		,      encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
           snake_case       : Optional[int]    =       BertTokenizer(UpperCamelCase__		)

       tokenizer.push_to_hub("valid_org/test-tokenizer-org"		,      use_auth_token=self._token		)
       snake_case       : Dict    =       BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"		)
       self.assertDictEqual(new_tokenizer.vocab		,      tokenizer.vocab		)

       # Reset repo
       delete_repo(token=self._token		,      repo_id="valid_org/test-tokenizer-org"		)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(
               UpperCamelCase__		,      repo_id="valid_org/test-tokenizer-org"		,      push_to_hub=UpperCamelCase__		,      use_auth_token=self._token		)

       snake_case       : Optional[int]    =       BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"		)
       self.assertDictEqual(new_tokenizer.vocab		,      tokenizer.vocab		)







   @require_tokenizers
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       CustomTokenizer.register_for_auto_class()
       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case       : Tuple    =       os.path.join(UpperCamelCase__		,      "vocab.txt"		)
           with open(UpperCamelCase__		,      "w"		,      encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
           snake_case       : Any    =       CustomTokenizer(UpperCamelCase__		)

       # No fast custom tokenizer
       tokenizer.push_to_hub("test-dynamic-tokenizer"		,      use_auth_token=self._token		)

       snake_case       : Union[str, Any]    =       AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'		,      trust_remote_code=UpperCamelCase__		)
       # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__		,      "CustomTokenizer"		)

       # Fast and slow custom tokenizer
       CustomTokenizerFast.register_for_auto_class()
       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case       : int    =       os.path.join(UpperCamelCase__		,      "vocab.txt"		)
           with open(UpperCamelCase__		,      "w"		,      encoding="utf-8"		) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)

           snake_case       : Optional[Any]    =       BertTokenizerFast.from_pretrained(UpperCamelCase__		)
           bert_tokenizer.save_pretrained(UpperCamelCase__		)
           snake_case       : List[Any]    =       CustomTokenizerFast.from_pretrained(UpperCamelCase__		)

       tokenizer.push_to_hub("test-dynamic-tokenizer"		,      use_auth_token=self._token		)

       snake_case       : Union[str, Any]    =       AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'		,      trust_remote_code=UpperCamelCase__		)
       # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__		,      "CustomTokenizerFast"		)
       snake_case       : Optional[Any]    =       AutoTokenizer.from_pretrained(
           F'{USER}/test-dynamic-tokenizer'		,      use_fast=UpperCamelCase__		,      trust_remote_code=UpperCamelCase__		)
       # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__		,      "CustomTokenizer"		)







class _lowerCAmelCase							( unittest.TestCase      ):







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Any    =       Trie()
       trie.add("Hello 友達"		)
       self.assertEqual(trie.data		,      {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}		)
       trie.add("Hello"		)
       trie.data
       self.assertEqual(trie.data		,      {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Tuple    =       Trie()
       self.assertEqual(trie.split("[CLS] This is a extra_id_100"		)		,      ["[CLS] This is a extra_id_100"]		)
       trie.add("[CLS]"		)
       trie.add("extra_id_1"		)
       trie.add("extra_id_100"		)
       self.assertEqual(trie.split("[CLS] This is a extra_id_100"		)		,      ["[CLS]", " This is a ", "extra_id_100"]		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : str    =       Trie()
       trie.add("A"		)
       self.assertEqual(trie.split("ABC"		)		,      ["A", "BC"]		)
       self.assertEqual(trie.split("BCA"		)		,      ["BC", "A"]		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       Trie()
       trie.add("TOKEN]"		)
       trie.add("[SPECIAL_TOKEN]"		)
       self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"		)		,      ["This is something ", "[SPECIAL_TOKEN]"]		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Optional[int]    =       Trie()
       trie.add("A"		)
       trie.add("P"		)
       trie.add("[SPECIAL_TOKEN]"		)
       self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"		)		,      ["This is something ", "[SPECIAL_TOKEN]"]		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : List[Any]    =       Trie()
       trie.add("AB"		)
       trie.add("B"		)
       trie.add("C"		)
       self.assertEqual(trie.split("ABC"		)		,      ["AB", "C"]		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : str    =       Trie()
       trie.add("ABC"		)
       trie.add("B"		)
       trie.add("CD"		)
       self.assertEqual(trie.split("ABCD"		)		,      ["ABC", "D"]		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       Trie()
       snake_case       : Union[str, Any]    =       trie.cut_text("ABC"		,      [0, 0, 2, 1, 2, 3]		)
       self.assertEqual(UpperCamelCase__		,      ["AB", "C"]		)





