





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      return ConvertCommand(
          args.model_type							,     args.tf_checkpoint							,     args.pytorch_dump_output							,     args.config							,     args.finetuning_task_name  )


A__    :       int              ='''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''





class 			UpperCAmelCase					(      snake_case_       ):




      @staticmethod
      def     lowercase__   (      __snake_case      :			ArgumentParser  )    ->				Optional[Any]:
            _lowerCAmelCase												=							parser.add_parser(
                """convert"""      ,      help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""      ,      )
            train_parser.add_argument("""--model_type"""      ,      type=__snake_case      ,      required=__snake_case      ,      help="""Model's type."""  )
            train_parser.add_argument(
                """--tf_checkpoint"""      ,      type=__snake_case      ,      required=__snake_case      ,      help="""TensorFlow checkpoint path or folder."""  )
            train_parser.add_argument(
                """--pytorch_dump_output"""      ,      type=__snake_case      ,      required=__snake_case      ,      help="""Path to the PyTorch saved model output."""  )
            train_parser.add_argument("""--config"""      ,      type=__snake_case      ,      default=""""""      ,      help="""Configuration file path or folder."""  )
            train_parser.add_argument(
                """--finetuning_task_name"""      ,      type=__snake_case      ,      default=__snake_case      ,      help="""Optional fine-tuning task name if the TF model was a finetuned model."""      ,      )
            train_parser.set_defaults(func=__snake_case  )




      def __init__(      self      :			Any      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      *__snake_case      :			List[str]      ,      )    ->				Any:
            _lowerCAmelCase												=							logging.get_logger("""transformers-cli/converting"""  )

            self._logger.info(f"Loading model {model_type}"  )
            _lowerCAmelCase												=							model_type
            _lowerCAmelCase												=							tf_checkpoint
            _lowerCAmelCase												=							pytorch_dump_output
            _lowerCAmelCase												=							config
            _lowerCAmelCase												=							finetuning_task_name




      def     lowercase__   (      self      :			Tuple  )    ->				List[Any]:
            if self._model_type == "albert":
                  try:
                        from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                            convert_tf_checkpoint_to_pytorch,
                        )
                  except ImportError:
                        raise ImportError(__snake_case  )

                  convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output  )
            elif self._model_type == "bert":
                  try:
                        from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                            convert_tf_checkpoint_to_pytorch,
                        )
                  except ImportError:
                        raise ImportError(__snake_case  )

                  convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output  )
            elif self._model_type == "funnel":
                  try:
                        from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                            convert_tf_checkpoint_to_pytorch,
                        )
                  except ImportError:
                        raise ImportError(__snake_case  )

                  convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output  )
            elif self._model_type == "t5":
                  try:
                        from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                  except ImportError:
                        raise ImportError(__snake_case  )

                  convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output  )
            elif self._model_type == "gpt":
                  from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                      convert_openai_checkpoint_to_pytorch,
                  )

                  convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output  )
            elif self._model_type == "transfo_xl":
                  try:
                        from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                            convert_transfo_xl_checkpoint_to_pytorch,
                        )
                  except ImportError:
                        raise ImportError(__snake_case  )

                  if "ckpt" in self._tf_checkpoint.lower():
                        _lowerCAmelCase												=							self._tf_checkpoint
                        _lowerCAmelCase												=							""""""
                  else:
                        _lowerCAmelCase												=							self._tf_checkpoint
                        _lowerCAmelCase												=							""""""
                  convert_transfo_xl_checkpoint_to_pytorch(
                      __snake_case      ,      self._config      ,      self._pytorch_dump_output      ,      __snake_case  )
            elif self._model_type == "gpt2":
                  try:
                        from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                            convert_gpta_checkpoint_to_pytorch,
                        )
                  except ImportError:
                        raise ImportError(__snake_case  )

                  convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output  )
            elif self._model_type == "xlnet":
                  try:
                        from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                            convert_xlnet_checkpoint_to_pytorch,
                        )
                  except ImportError:
                        raise ImportError(__snake_case  )

                  convert_xlnet_checkpoint_to_pytorch(
                      self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name  )
            elif self._model_type == "xlm":
                  from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                      convert_xlm_checkpoint_to_pytorch,
                  )

                  convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output  )
            elif self._model_type == "lxmert":
                  from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                      convert_lxmert_checkpoint_to_pytorch,
                  )

                  convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output  )
            elif self._model_type == "rembert":
                  from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                      convert_rembert_tf_checkpoint_to_pytorch,
                  )

                  convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output  )
            else:
                  raise ValueError(
                      """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""  )













'''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)






