

import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
  from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
  raise ImportError("""You need to install the version of fairseq from the Mega repo!""")







class     a__    (				nn.Module    ):







  """simple docstring"""




  def __init__(     self      :	Dict			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	Union[str, Any]			,			UpperCAmelCase__      :	Dict						)   ->Tuple:
        """simple docstring"""







        super().__init__()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				mega_args
        SCREAMING_SNAKE_CASE	: str					       =				nn.Embedding(UpperCAmelCase__			,			self.mega_args.encoder_embed_dim						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				nn.ModuleList([MegaEncoderLayer(self.mega_args						) for _ in range(UpperCAmelCase__						)]						)
        SCREAMING_SNAKE_CASE	: Any					       =				depth




  def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	str=True			,			UpperCAmelCase__      :	List[Any]=0						)   ->int:
        """simple docstring"""







        if batch_first:
              SCREAMING_SNAKE_CASE	: int					       =				input_ids.T

        # to make things more confusing, Mega expects the attention mask to
        # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
        # which is the opposite of what HF returns
        if ignore_mask_value == 0:
              SCREAMING_SNAKE_CASE	: List[str]					       =				1 - attention_mask

        # get token embeddings from IDs
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.embedding_layer(UpperCAmelCase__						)

        # pass through the Mega layers
        # input is (time, batch, encoder dim) and output is the same
        for encoder in self.encoders:
              SCREAMING_SNAKE_CASE	: Any					       =				encoder(UpperCAmelCase__			,			UpperCAmelCase__						)

        # return according to the shape specified
        if batch_first:
              # (T, B, H) --> (B, T, H)
              return torch.transpose(UpperCAmelCase__			,			0			,			1						)
        else:
              return embeds







class     a__    (				nn.Module    ):







  """simple docstring"""




  def __init__(     self      :	List[Any]			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Union[str, Any]			,			UpperCAmelCase__      :	List[str]						)   ->Optional[Any]:
        """simple docstring"""







        super().__init__()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				MegaLM(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: str					       =				nn.Linear(mega_args.encoder_embed_dim			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				nn.Dropout(p=0.1						)




  def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	List[str]			,			UpperCAmelCase__      :	str=True			,			UpperCAmelCase__      :	List[Any]=0						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				self.mega(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)
        return self.mlm_head(self.dropout(UpperCAmelCase__						)						)






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Dict:
      with open(os.path.join(_A  ,      """model_args.pkl"""     )  ,      """rb"""     ) as f:
            SCREAMING_SNAKE_CASE	: Tuple					       =				pkl.load(_A     )

      # load the original encoder
      SCREAMING_SNAKE_CASE	: List[Any]					       =				OriginalMegaForMaskedLM(**_A     ).eval()

      # load its weights
      print(
          """Original Mega encoder:"""  ,      original_mlm.mega.load_state_dict(
              torch.load(os.path.join(_A  ,      """encoder_weights.pt"""     )  ,      map_location="""cpu"""     )     )  ,      )
      print(
          """Original Mega MLM layer:"""  ,      original_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(_A  ,      """mlm_head_weights.pt"""     )  ,      map_location="""cpu"""     )     )  ,      )

      # create a new config from the old one
      SCREAMING_SNAKE_CASE	: List[str]					       =				MegaConfig(
          num_hidden_layers=mega_original_args["""depth"""]  ,      vocab_size=mega_original_args["""vocab_size"""]  ,      hidden_size=mega_original_args["""mega_args"""].encoder_embed_dim  ,      shared_representation_size=mega_original_args["""mega_args"""].encoder_z_dim  ,      intermediate_size=mega_original_args["""mega_args"""].encoder_hidden_dim  ,      ema_projection_size=mega_original_args["""mega_args"""].encoder_n_dim  ,      dropout_prob=mega_original_args["""mega_args"""].dropout  ,      attention_probs_dropout_prob=mega_original_args["""mega_args"""].attention_dropout  ,      hidden_dropout_prob=mega_original_args["""mega_args"""].hidden_dropout  ,      activation=mega_original_args["""mega_args"""].activation_fn  ,      attention_activation=mega_original_args["""mega_args"""].attention_activation_fn  ,      bidirectional=mega_original_args["""mega_args"""].bidirectional  ,      use_chunking=mega_original_args["""mega_args"""].encoder_chunk_size > 0  ,      chunk_size=mega_original_args["""mega_args"""].encoder_chunk_size  ,      truncation=mega_original_args["""mega_args"""].truncation_length  ,      normalization_type=mega_original_args["""mega_args"""].normalization_type  ,      normalize_before_mega=_A  ,      norm_affine=_A  ,      use_feature_dropout=mega_original_args["""mega_args"""].feature_dropout  ,      relative_positional_bias=mega_original_args["""mega_args"""].rel_pos_bias  ,      max_positions=mega_original_args["""mega_args"""].max_source_positions  ,      nffn_hidden_size=mega_original_args["""mega_args"""].encoder_ffn_embed_dim  ,      normalize_before_ffn=mega_original_args["""mega_args"""].normalize_before  ,      nffn_activation_dropout_prob=0.0  ,      add_token_type_embeddings=_A  ,      add_lm_hidden_dense_layer=_A  ,      )

      SCREAMING_SNAKE_CASE	: List[Any]					       =				MegaForMaskedLM(_A     ).eval()

      # the originl checkpoint just uses nn.Embedding for the word embeddings
      # we use a wrapper module for embeddings to add support for positional embeddings
      SCREAMING_SNAKE_CASE	: Dict					       =				original_mlm.mega.embedding_layer.weight

      # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
      # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
      # also renaming previously confusing parameter names
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				original_mlm.mega.encoders.state_dict()
      SCREAMING_SNAKE_CASE	: List[Any]					       =				{}
      for module_name in original_state_dict.keys():
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				None
            # have to handle gamma, beta, and alpha differently due to their use
            # in multiple modules within the original repository;
            # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
            # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
            if "beta" in module_name:
                  # EMA sub-layers were always called "move" in the original repo
                  if "move.beta" in module_name:
                        SCREAMING_SNAKE_CASE	: List[Any]					       =				module_name.replace("""move.beta"""  ,      """ema_gate.ema_expansion_matrix"""     )
                  elif "mega_layer.beta" in module_name:
                        SCREAMING_SNAKE_CASE	: List[Any]					       =				module_name.replace("""beta"""  ,      """qk_bias"""     )
                  else:
                        SCREAMING_SNAKE_CASE	: Any					       =				module_name.replace("""beta"""  ,      """b_param"""     )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
            elif "gamma" in module_name:
                  if "move.gamma" in module_name:
                        SCREAMING_SNAKE_CASE	: Tuple					       =				module_name.replace("""move.gamma"""  ,      """ema_gate.kernel_projection_matrix"""     )
                  elif "mega_layer.gamma" in module_name:
                        SCREAMING_SNAKE_CASE	: List[str]					       =				module_name.replace("""gamma"""  ,      """qk_weight"""     )
                  else:
                        SCREAMING_SNAKE_CASE	: List[str]					       =				module_name.replace("""gamma"""  ,      """g_param"""     )
        # alpha is used in EMA and positional bias; renaming to improve readability
            elif "move.alpha" in module_name:
                  SCREAMING_SNAKE_CASE	: str					       =				module_name.replace("""move.alpha"""  ,      """ema_gate.decay_factor"""     )
            # delta is only used in EMA; renaming to improve readability
            elif "move.delta" in module_name:
                  SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				module_name.replace("""move.delta"""  ,      """ema_gate.damping_factor"""     )
            # omega is only used in EMA; renaming to improve readability
            elif "omega" in module_name:
                  SCREAMING_SNAKE_CASE	: str					       =				module_name.replace("""move.omega"""  ,      """ema_gate.residual_weight"""     )

            if new_module_name:
                  SCREAMING_SNAKE_CASE	: List[str]					       =				new_module_name

      if len(_A     ) != 0:
            print(F"Renaming these keys: {updated_keys.keys()}"     )
      else:
            print("""No need to rename state dict entries"""     )
      for old, new in updated_keys.items():
            SCREAMING_SNAKE_CASE	: Optional[Any]					       =				original_state_dict.pop(_A     )

      # now attempt to load the state dictionary with updated names
      # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
      print("""HF Mega encoder:"""  ,      hf_mlm.mega.layers.load_state_dict(_A     )     )

      # load the MLM head weights directly
      print(
          """HF Mega MLM layer:"""  ,      hf_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(_A  ,      """mlm_head_weights.pt"""     )  ,      map_location="""cpu"""     )     )  ,      )

      # test on a randomly generated input sequence
      SCREAMING_SNAKE_CASE	: Any					       =				torch.randint(0  ,      hf_config.vocab_size  ,      size=(4, 256)     )
      SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.ones_like(_A     )
      # mask a few tokens to make sure masking is applied appropriately :)
      SCREAMING_SNAKE_CASE	: str					       =				0

      # run forward passes
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				original_mlm(_A  ,      _A  ,      batch_first=_A  ,      ignore_mask_value=0     )
      SCREAMING_SNAKE_CASE	: List[Any]					       =				hf_mlm(_A  ,      _A     )[0]

      # print shapes and diff
      print(F"original output {original_output.shape}"     )
      print(F"hf output {hf_output.shape}"     )
      print(F"max diff: {(original_output - hf_output).max()}"     )  # 0.0
      SCREAMING_SNAKE_CASE	: Any					       =				torch.allclose(_A  ,      _A  ,      atol=1e-3     )

      if success:
            print("""Yay!"""     )
            hf_mlm.save_pretrained(_A     )
      else:
            raise RuntimeError(F"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"     )

      if includes_tokenizer:
            print("""Transferring tokenizer"""     )
            SCREAMING_SNAKE_CASE	: Dict					       =				AutoTokenizer.from_pretrained(_A     )
            tokenizer.save_pretrained(_A     )


if __name__ == "__main__":
  UpperCAmelCase__	:       List[Any]			      =      argparse.ArgumentParser()

  parser.add_argument(
      """--pretrained_checkpoint_path""",
      default=None,
      type=str,
      required=True,
      help="""Point to the directory containing your model weights using the official Mega repo""",
  )

  parser.add_argument(
      """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
  )

  parser.add_argument(
      """--includes_tokenizer""",
      action="""store_true""",
      help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
  )

  UpperCAmelCase__	:       str			      =      parser.parse_args()

  convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)







import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:       Tuple			      =      """\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
"""

UpperCAmelCase__	:       Union[str, Any]			      =      """\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
"""


UpperCAmelCase__	:       str			      =      """
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION    )
class     a__    (				datasets.Metric    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Any						)   ->Optional[int]:
        """simple docstring"""







        return datasets.MetricInfo(
            description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""						),
                    """references""": datasets.Value("""string"""						),
                }						)			,			homepage="""https://github.com/moussaKam/FrugalScore"""			,			)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->Any:
        """simple docstring"""







        if self.config_name == "default":
              SCREAMING_SNAKE_CASE	: int					       =				"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
        else:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.config_name
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoTokenizer.from_pretrained(UpperCAmelCase__						)




  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	str=3_2			,			UpperCAmelCase__      :	Optional[int]=1_2_8			,			UpperCAmelCase__      :	Optional[int]=None			,			)   ->Any:
        """simple docstring"""







        assert len(UpperCAmelCase__						) == len(
            UpperCAmelCase__						), "predictions and references should have the same number of sentences."
        if device is not None:
              assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
        else:
              SCREAMING_SNAKE_CASE	: List[str]					       =				"""gpu""" if torch.cuda.is_available() else """cpu"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				TrainingArguments(
            """trainer"""			,			fpaa=(device == """gpu""")			,			per_device_eval_batch_size=UpperCAmelCase__			,			report_to="""all"""			,			no_cuda=(device == """cpu""")			,			log_level="""warning"""			,			)
        SCREAMING_SNAKE_CASE	: int					       =				{"""sentence1""": predictions, """sentence2""": references}
        SCREAMING_SNAKE_CASE	: List[str]					       =				datasets.Dataset.from_dict(UpperCAmelCase__						)

        def tokenize_function(UpperCAmelCase__      :	Any						):
              return self.tokenizer(
                  data["""sentence1"""]			,			data["""sentence2"""]			,			max_length=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			padding=UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Dict					       =				raw_datasets.map(UpperCAmelCase__			,			batched=UpperCAmelCase__						)
        tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				Trainer(self.model			,			UpperCAmelCase__			,			tokenizer=self.tokenizer						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				trainer.predict(UpperCAmelCase__						)
        return {"scores": list(predictions.predictions.squeeze(-1						)						)}




