





import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
      import sqlitea

      import sqlalchemy




class 			UpperCAmelCase					(      lowercase__       ):




      def __init__(      self      :			Union[str, Any]      ,      __snake_case      :			Union[str, "sqlalchemy.sql.Selectable"]      ,      __snake_case      :			Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]      ,      __snake_case      :			Optional[Features] = None      ,      __snake_case      :			str = None      ,      __snake_case      :			bool = False      ,      **__snake_case      :			List[Any]      ,      )    ->				int:
            super().__init__(features=_a      ,      cache_dir=_a      ,      keep_in_memory=_a      ,      **_a  )
            _lowerCAmelCase												=							Sql(
                cache_dir=_a      ,      features=_a      ,      sql=_a      ,      con=_a      ,      **_a      ,      )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				str:
            _lowerCAmelCase												=							None
            _lowerCAmelCase												=							None
            _lowerCAmelCase												=							None
            _lowerCAmelCase												=							None

            self.builder.download_and_prepare(
                download_config=_a      ,      download_mode=_a      ,      verification_mode=_a      ,      base_path=_a      ,      )

            # Build dataset for splits
            _lowerCAmelCase												=							self.builder.as_dataset(
                split="""train"""      ,      verification_mode=_a      ,      in_memory=self.keep_in_memory  )
            return dataset




class 			UpperCAmelCase					:




      def __init__(      self      :			Optional[Any]      ,      __snake_case      :			Dataset      ,      __snake_case      :			str      ,      __snake_case      :			Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]      ,      __snake_case      :			Optional[int] = None      ,      __snake_case      :			Optional[int] = None      ,      **__snake_case      :			int      ,      )    ->				int:






            if num_proc is not None and num_proc <= 0:
                  raise ValueError(f"num_proc {num_proc} must be an integer > 0."  )

            _lowerCAmelCase												=							dataset
            _lowerCAmelCase												=							name
            _lowerCAmelCase												=							con
            _lowerCAmelCase												=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            _lowerCAmelCase												=							num_proc
            _lowerCAmelCase												=							to_sql_kwargs




      def     lowercase__   (      self      :			List[str]  )    ->				str:
            _lowerCAmelCase												=							self.to_sql_kwargs.pop("""sql"""      ,      _a  )
            _lowerCAmelCase												=							self.to_sql_kwargs.pop("""con"""      ,      _a  )
            _lowerCAmelCase												=							self.to_sql_kwargs.pop("""index"""      ,      _a  )

            _lowerCAmelCase												=							self._write(index=_a      ,      **self.to_sql_kwargs  )
            return written




      def     lowercase__   (      self      :			Any      ,      __snake_case      :			Tuple  )    ->				Any:
            _lowerCAmelCase												=							args
            _lowerCAmelCase												=							{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
            _lowerCAmelCase												=							query_table(
                table=self.dataset.data      ,      key=slice(_a      ,      offset + self.batch_size  )      ,      indices=self.dataset._indices      ,      )
            _lowerCAmelCase												=							batch.to_pandas()
            _lowerCAmelCase												=							df.to_sql(self.name      ,      self.con      ,      index=_a      ,      **_a  )
            return num_rows or len(_a  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			List[str]      ,      **__snake_case      :			List[Any]  )    ->				List[Any]:
            _lowerCAmelCase												=							0

            if self.num_proc is None or self.num_proc == 1:
                  for offset in logging.tqdm(
                      range(0      ,      len(self.dataset  )      ,      self.batch_size  )      ,      unit="""ba"""      ,      disable=not logging.is_progress_bar_enabled()      ,      desc="""Creating SQL from Arrow format"""      ,      ):
                        written += self._batch_sql((offset, index, to_sql_kwargs)  )
            else:
                  _lowerCAmelCase												=							len(self.dataset  ), self.batch_size
                  with multiprocessing.Pool(self.num_proc  ) as pool:
                        for num_rows in logging.tqdm(
                            pool.imap(
                                self._batch_sql      ,      [(offset, index, to_sql_kwargs) for offset in range(0      ,      _a      ,      _a  )]      ,      )      ,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size      ,      unit="""ba"""      ,      disable=not logging.is_progress_bar_enabled()      ,      desc="""Creating SQL from Arrow format"""      ,      ):
                              written += num_rows

            return written













'''simple docstring'''






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

import os
from pathlib import Path
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							{
          """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												=							{
          """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												=							f"{src_lang}-{tgt_lang}"

      _lowerCAmelCase												=							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(lowerCAmelCase							,     exist_ok=lowerCAmelCase  )
      _lowerCAmelCase												=							os.path.join(lowerCAmelCase							,     """README.md"""  )
      print(f"Generating {path}"  )
      with open(lowerCAmelCase							,     """w"""							,     encoding="""utf-8"""  ) as f:
            f.write(lowerCAmelCase  )


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

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      A__       ,						A__       ,						A__    :       Optional[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)






