





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase  :   Any						      =logging.get_logger(__name__)

__lowercase  :   List[Any]						      ={
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   for attribute in key.split("."  ):
      UpperCAmelCase_											=getattr(lowercase__  ,    lowercase__  )

   if weight_type is not None:
      UpperCAmelCase_											=getattr(lowercase__  ,    lowercase__  ).shape
   else:
      UpperCAmelCase_											=hf_pointer.shape

   assert hf_shape == value.shape, (
       F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
       F' {value.shape} for {full_name}'
   )

   if weight_type == "weight":
      UpperCAmelCase_											=value
   elif weight_type == "weight_g":
      UpperCAmelCase_											=value
   elif weight_type == "weight_v":
      UpperCAmelCase_											=value
   elif weight_type == "bias":
      UpperCAmelCase_											=value
   else:
      UpperCAmelCase_											=value

   logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'  )




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   UpperCAmelCase_											=fairseq_model.state_dict()

   UpperCAmelCase_											=hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCAmelCase_											=False
      if "conv_layers" in name:
         load_conv_layer(
             lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    hf_model.config.feat_extract_norm == "group"  ,    )
         UpperCAmelCase_											=True
      else:
         for key, mapped_key in MAPPING.items():
            UpperCAmelCase_											="hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

            if key in name or (key.split("w2v_model."  )[-1] == name.split("."  )[0] and not is_finetuned):
               UpperCAmelCase_											=True
               if "*" in mapped_key:
                  UpperCAmelCase_											=name.split(lowercase__  )[0].split("."  )[-2]
                  UpperCAmelCase_											=mapped_key.replace("*"  ,    lowercase__  )
               if "weight_g" in name:
                  UpperCAmelCase_											="weight_g"
               elif "weight_v" in name:
                  UpperCAmelCase_											="weight_v"
               elif "weight" in name:
                  UpperCAmelCase_											="weight"
               elif "bias" in name:
                  UpperCAmelCase_											="bias"
               else:
                  UpperCAmelCase_											=None
               set_recursively(lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  )
            continue
      if not is_used:
         unused_weights.append(lowercase__  )

   logger.warning(F'Unused weights: {unused_weights}'  )




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=full_name.split("conv_layers."  )[-1]
   UpperCAmelCase_											=name.split("."  )
   UpperCAmelCase_											=int(items[0]  )
   UpperCAmelCase_											=int(items[1]  )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F'{full_name} has size {value.shape}, but'
             F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
         )
         UpperCAmelCase_											=value
         logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'  )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F'{full_name} has size {value.shape}, but'
             F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
         )
         UpperCAmelCase_											=value
         logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'  )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
             " found."
         )
         UpperCAmelCase_											=value
         logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'{full_name} has size {value.shape}, but'
             F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
         )
         UpperCAmelCase_											=value
         logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
   else:
      unused_weights.append(lowercase__  )




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__=None  ,    lowercase__=None  ,    lowercase__=True  ):






   '''simple docstring'''






   if config_path is not None:
      UpperCAmelCase_											=HubertConfig.from_pretrained(lowercase__  )
   else:
      UpperCAmelCase_											=HubertConfig()

   if is_finetuned:
      if dict_path:
         UpperCAmelCase_											=Dictionary.load(lowercase__  )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCAmelCase_											=target_dict.pad_index
         UpperCAmelCase_											=target_dict.bos_index
         UpperCAmelCase_											=target_dict.eos_index
         UpperCAmelCase_											=len(target_dict.symbols  )
         UpperCAmelCase_											=os.path.join(lowercase__  ,    "vocab.json"  )
         if not os.path.isdir(lowercase__  ):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowercase__  )  )
            return
         os.makedirs(lowercase__  ,    exist_ok=lowercase__  )
         with open(lowercase__  ,    "w"  ,    encoding="utf-8"  ) as vocab_handle:
            json.dump(target_dict.indices  ,    lowercase__  )
         UpperCAmelCase_											=WavaVecaCTCTokenizer(
             lowercase__  ,    unk_token=target_dict.unk_word  ,    pad_token=target_dict.pad_word  ,    bos_token=target_dict.bos_word  ,    eos_token=target_dict.eos_word  ,    word_delimiter_token="|"  ,    do_lower_case=lowercase__  ,    )
         UpperCAmelCase_											=True if config.feat_extract_norm == "layer" else False
         UpperCAmelCase_											=WavaVecaFeatureExtractor(
             feature_size=1  ,    sampling_rate=1_6_0_0_0  ,    padding_value=0  ,    do_normalize=lowercase__  ,    return_attention_mask=lowercase__  ,    )
         UpperCAmelCase_											=WavaVecaProcessor(feature_extractor=lowercase__  ,    tokenizer=lowercase__  )
         processor.save_pretrained(lowercase__  )

      UpperCAmelCase_											=HubertForCTC(lowercase__  )
   else:
      UpperCAmelCase_											=HubertModel(lowercase__  )

   if is_finetuned:
      UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]  ,    arg_overrides={"data": "/".join(dict_path.split("/"  )[:-1]  )}  )
   else:
      UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

   UpperCAmelCase_											=model[0].eval()

   recursively_load_weights(lowercase__  ,    lowercase__  ,    lowercase__  )

   hf_wavavec.save_pretrained(lowercase__  )


if __name__ == "__main__":
     __lowercase  :   Tuple						      =argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     __lowercase  :   Tuple						      =parser.parse_args()
     convert_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )











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

import os
from pathlib import Path




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={
       "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]
   UpperCAmelCase_											={
       "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"],
   }
   UpperCAmelCase_											=F'{src_lang}-{tgt_lang}'

   UpperCAmelCase_											=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__  )
   UpperCAmelCase_											=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
__lowercase  :   int						      =Path(__file__).resolve().parent.parent.parent
__lowercase  :   Any						      =repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     __lowercase      ,			__lowercase      ,			__lowercase  :   Union[str, Any]						      =model_name.split("""-""")
     __lowercase  :   Optional[int]						      =model_cards_dir / """facebook""" / model_name
     write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)




