


'''simple docstring'''







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

import os
from pathlib import Path


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Any  )  ->			List[Any]:
       _a    : List[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    : Optional[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    : str     					=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(_UpperCAmelCase    ,exist_ok=_UpperCAmelCase  )
       _a    : int     					=os.path.join(_UpperCAmelCase    ,"""README.md"""  )
       print(F"Generating {path}"  )
       with open(_UpperCAmelCase    ,"""w"""    ,encoding="""utf-8"""  ) as f:
              f.write(_UpperCAmelCase  )


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

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
   A__    ,       A__    ,       A__:    List[str]					    =       model_name.split('''-''')
   A__:    List[Any]					    =       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







class A__       :






      def __init__( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:str	)       ->					Optional[int]:




             '''simple docstring'''
             _a					,    _a    : List[str]     					=text, pattern
             _a					,    _a    : Union[str, Any]     					=len(SCREAMING_SNAKE_CASE	), len(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					int:




             '''simple docstring'''

             for i in range(self.patLen - 1     ,						-1     ,						-1	):
                    if char == self.pattern[i]:
                           return i
             return -1






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:int	)       ->					int:




             '''simple docstring'''

             for i in range(self.patLen - 1     ,						-1     ,						-1	):
                    if self.pattern[i] != self.text[current_pos + i]:
                           return current_pos + i
             return -1






      def __UpperCAmelCase       ( self			:Union[str, Any]	)       ->					list[int]:




             '''simple docstring'''
             # searches pattern in text and returns index positions
             _a    : Union[str, Any]     					=[]
             for i in range(self.textLen - self.patLen + 1	):
                    _a    : Any     					=self.mismatch_in_text(SCREAMING_SNAKE_CASE	)
                    if mismatch_index == -1:
                           positions.append(SCREAMING_SNAKE_CASE	)
                    else:
                           _a    : int     					=self.match_in_pattern(self.text[mismatch_index]	)
                           _a    : List[str]     					=(
                               mismatch_index - match_index
                           )  # shifting index lgtm [py/multiple-definition]
             return positions

A__:    Any					    =       '''ABAABA'''
A__:    int					    =       '''AB'''
A__:    Optional[int]					    =       BoyerMooreSearch(text, pattern)
A__:    Optional[Any]					    =       bms.bad_character_heuristic()

if len(positions) == 0:
   print('''No match found''')
else:
   print('''Pattern found in following positions: ''')
   print(positions)


