





'''simple docstring'''






import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A__    :       Union[str, Any]              =logging.get_logger(__name__)

A__    :       Optional[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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
A__    :       List[str]              =[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for attribute in key.split("""."""  ):
            _lowerCAmelCase												=							getattr(lowerCAmelCase							,     lowerCAmelCase  )

      if weight_type is not None:
            _lowerCAmelCase												=							getattr(lowerCAmelCase							,     lowerCAmelCase  ).shape
      else:
            _lowerCAmelCase												=							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":
            _lowerCAmelCase												=							value
      elif weight_type == "weight_g":
            _lowerCAmelCase												=							value
      elif weight_type == "weight_v":
            _lowerCAmelCase												=							value
      elif weight_type == "bias":
            _lowerCAmelCase												=							value
      else:
            _lowerCAmelCase												=							value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      _lowerCAmelCase												=							fairseq_model.state_dict()

      _lowerCAmelCase												=							hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCAmelCase												=							False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     hf_model.config.feat_extract_norm == """group"""							,     )
                  _lowerCAmelCase												=							True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
                              _lowerCAmelCase												=							True
                              if "*" in mapped_key:
                                    _lowerCAmelCase												=							name.split(lowerCAmelCase  )[0].split("""."""  )[-2]
                                    _lowerCAmelCase												=							mapped_key.replace("""*"""							,     lowerCAmelCase  )
                              if "weight_g" in name:
                                    _lowerCAmelCase												=							"""weight_g"""
                              elif "weight_v" in name:
                                    _lowerCAmelCase												=							"""weight_v"""
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    _lowerCAmelCase												=							"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _lowerCAmelCase												=							"""weight"""
                              else:
                                    _lowerCAmelCase												=							None

                              set_recursively(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  )
                        continue
            if not is_used:
                  unused_weights.append(lowerCAmelCase  )

      logger.warning(f"Unused weights: {unused_weights}"  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							full_name.split("""conv_layers."""  )[-1]
      _lowerCAmelCase												=							name.split("""."""  )
      _lowerCAmelCase												=							int(items[0]  )
      _lowerCAmelCase												=							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."
                  )
                  _lowerCAmelCase												=							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."
                  )
                  _lowerCAmelCase												=							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."
                  )
                  _lowerCAmelCase												=							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."
                  )
                  _lowerCAmelCase												=							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
      else:
            unused_weights.append(lowerCAmelCase  )
@torch.no_grad()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None  ):





      """simple docstring"""


      _lowerCAmelCase												=							torch.load(lowerCAmelCase  )
      _lowerCAmelCase												=							WavLMConfigOrig(checkpoint["""cfg"""]  )
      _lowerCAmelCase												=							WavLMOrig(lowerCAmelCase  )
      model.load_state_dict(checkpoint["""model"""]  )
      model.eval()

      if config_path is not None:
            _lowerCAmelCase												=							WavLMConfig.from_pretrained(lowerCAmelCase  )
      else:
            _lowerCAmelCase												=							WavLMConfig()

      _lowerCAmelCase												=							WavLMModel(lowerCAmelCase  )

      recursively_load_weights(lowerCAmelCase							,     lowerCAmelCase  )

      hf_wavlm.save_pretrained(lowerCAmelCase  )


if __name__ == "__main__":
      A__    :       List[str]              =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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
      A__    :       List[Any]              =parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)













'''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)






