





'''simple docstring'''




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
    raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
    raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
_A:					List[str]							=				logging.get_logger(__name__)

_A:					List[Any]							=				"""Hello, World!"""
_A:					Optional[Any]							=				"""en_XX"""







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[Any]:
 __UpperCAmelCase					=   Path('data_bin'    )
 __UpperCAmelCase					=   FairseqXmodModel.from_pretrained(
     model_name_or_path=str(Path(_lowerCAmelCase    ).parent    )       ,    checkpoint_file=Path(_lowerCAmelCase    ).name       ,    _name='xmod_base'       ,    arch='xmod_base'       ,    task='multilingual_masked_lm'       ,    data_name_or_path=str(_lowerCAmelCase    )       ,    bpe='sentencepiece'       ,    sentencepiece_model=str(Path(_lowerCAmelCase    ).parent / 'sentencepiece.bpe.model'    )       ,    src_dict=str(data_dir / 'dict.txt'    )       ,    )
 xmod.eval()  # disable dropout
 print(_lowerCAmelCase    )

 __UpperCAmelCase					=   xmod.model.encoder.sentence_encoder
 __UpperCAmelCase					=   XmodConfig(
     vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings       ,    hidden_size=xmod.cfg.model.encoder_embed_dim       ,    num_hidden_layers=xmod.cfg.model.encoder_layers       ,    num_attention_heads=xmod.cfg.model.encoder_attention_heads       ,    intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim       ,    max_position_embeddings=5_14       ,    type_vocab_size=1       ,    layer_norm_eps=1e-5       ,    pre_norm=xmod.cfg.model.encoder_normalize_before       ,    adapter_reduction_factor=getattr(xmod.cfg.model       ,    'bottleneck'       ,    2    )       ,    adapter_layer_norm=xmod.cfg.model.adapter_layer_norm       ,    adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm       ,    ln_before_adapter=xmod.cfg.model.ln_before_adapter       ,    languages=xmod.cfg.model.languages       ,    )
 if classification_head:
  __UpperCAmelCase					=   xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

 print('Our X-MOD config:'       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   XmodForSequenceClassification(_lowerCAmelCase    ) if classification_head else XmodForMaskedLM(_lowerCAmelCase    )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 __UpperCAmelCase					=   xmod_sent_encoder.embed_tokens.weight
 __UpperCAmelCase					=   xmod_sent_encoder.embed_positions.weight
 __UpperCAmelCase					=   torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c xmod doesn't use them.

 __UpperCAmelCase					=   xmod_sent_encoder.layernorm_embedding.weight
 __UpperCAmelCase					=   xmod_sent_encoder.layernorm_embedding.bias

 for i in range(config.num_hidden_layers    ):
  # Encoder: start of layer
  __UpperCAmelCase					=   model.roberta.encoder.layer[i]
  __UpperCAmelCase					=   xmod_sent_encoder.layers[i]

  # self attention
  __UpperCAmelCase					=   layer.attention.self
  if not (
      xmod_layer.self_attn.k_proj.weight.data.shape
      == xmod_layer.self_attn.q_proj.weight.data.shape
      == xmod_layer.self_attn.v_proj.weight.data.shape
      == torch.Size((config.hidden_size, config.hidden_size)    )
  ):
   raise AssertionError('Dimensions of self-attention weights do not match.'    )

  __UpperCAmelCase					=   xmod_layer.self_attn.q_proj.weight
  __UpperCAmelCase					=   xmod_layer.self_attn.q_proj.bias
  __UpperCAmelCase					=   xmod_layer.self_attn.k_proj.weight
  __UpperCAmelCase					=   xmod_layer.self_attn.k_proj.bias
  __UpperCAmelCase					=   xmod_layer.self_attn.v_proj.weight
  __UpperCAmelCase					=   xmod_layer.self_attn.v_proj.bias

  # self-attention output
  __UpperCAmelCase					=   layer.attention.output
  if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
   raise AssertionError('Dimensions of self-attention output weights do not match.'    )
  __UpperCAmelCase					=   xmod_layer.self_attn.out_proj.weight
  __UpperCAmelCase					=   xmod_layer.self_attn.out_proj.bias
  __UpperCAmelCase					=   xmod_layer.self_attn_layer_norm.weight
  __UpperCAmelCase					=   xmod_layer.self_attn_layer_norm.bias

  # intermediate
  __UpperCAmelCase					=   layer.intermediate
  if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('Dimensions of intermediate weights do not match.'    )
  __UpperCAmelCase					=   xmod_layer.fca.weight
  __UpperCAmelCase					=   xmod_layer.fca.bias

  # output
  __UpperCAmelCase					=   layer.output
  if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('Dimensions of feed-forward weights do not match.'    )
  __UpperCAmelCase					=   xmod_layer.fca.weight
  __UpperCAmelCase					=   xmod_layer.fca.bias
  __UpperCAmelCase					=   xmod_layer.final_layer_norm.weight
  __UpperCAmelCase					=   xmod_layer.final_layer_norm.bias
  if bert_output.adapter_layer_norm is not None:
   __UpperCAmelCase					=   xmod_layer.adapter_layer_norm.weight
   __UpperCAmelCase					=   xmod_layer.adapter_layer_norm.bias

  if sorted(bert_output.adapter_modules.keys()    ) != sorted(xmod_layer.adapter_modules.keys()    ):
   raise AssertionError('Lists of language adapters do not match.'    )
  for lang_code, adapter in xmod_layer.adapter_modules.items():
   __UpperCAmelCase					=   bert_output.adapter_modules[lang_code]
   __UpperCAmelCase					=   xmod_layer.adapter_modules[lang_code]
   __UpperCAmelCase					=   from_adapter.fca.weight
   __UpperCAmelCase					=   from_adapter.fca.bias
   __UpperCAmelCase					=   from_adapter.fca.weight
   __UpperCAmelCase					=   from_adapter.fca.bias

  # end of layer

 if xmod_sent_encoder.layer_norm is not None:
  __UpperCAmelCase					=   xmod_sent_encoder.layer_norm.weight
  __UpperCAmelCase					=   xmod_sent_encoder.layer_norm.bias

 if classification_head:
  __UpperCAmelCase					=   xmod.model.classification_heads['mnli'].dense.weight
  __UpperCAmelCase					=   xmod.model.classification_heads['mnli'].dense.bias
  __UpperCAmelCase					=   xmod.model.classification_heads['mnli'].out_proj.weight
  __UpperCAmelCase					=   xmod.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  __UpperCAmelCase					=   xmod.model.encoder.lm_head.dense.weight
  __UpperCAmelCase					=   xmod.model.encoder.lm_head.dense.bias
  __UpperCAmelCase					=   xmod.model.encoder.lm_head.layer_norm.weight
  __UpperCAmelCase					=   xmod.model.encoder.lm_head.layer_norm.bias
  __UpperCAmelCase					=   xmod.model.encoder.lm_head.weight
  __UpperCAmelCase					=   xmod.model.encoder.lm_head.bias

 # Let's check that we get the same results.
 __UpperCAmelCase					=   xmod.encode(_lowerCAmelCase    ).unsqueeze(0    )  # batch of size 1
 model.roberta.set_default_language(_lowerCAmelCase    )

 __UpperCAmelCase					=   model(_lowerCAmelCase    )[0]
 if classification_head:
  __UpperCAmelCase					=   xmod.model.classification_heads['mnli'](xmod.extract_features(_lowerCAmelCase    )    )
 else:
  __UpperCAmelCase					=   xmod.model(_lowerCAmelCase       ,    lang_id=[SAMPLE_LANGUAGE]    )[0]
 print(our_output.shape       ,    their_output.shape    )
 __UpperCAmelCase					=   torch.max(torch.abs(our_output - their_output    )    ).item()
 print(F'max_absolute_diff = {max_absolute_diff}'    )  # ~ 1e-7
 __UpperCAmelCase					=   torch.allclose(_lowerCAmelCase       ,    _lowerCAmelCase       ,    atol=1e-3    )
 print('Do both models output the same tensors?'       ,    '🔥' if success else '💩'    )
 if not success:
  raise Exception('Something went wRoNg'    )

 Path(_lowerCAmelCase    ).mkdir(parents=_lowerCAmelCase       ,    exist_ok=_lowerCAmelCase    )
 print(F'Saving model to {pytorch_dump_folder_path}'    )
 model.save_pretrained(_lowerCAmelCase    )


if __name__ == "__main__":
    _A:					Any							=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
    )
    _A:					List[Any]							=				parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )












'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_A:					Tuple							=				"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

_A:					List[Any]							=				"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

_A:					List[Any]							=				"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 return float((preds == labels).mean()    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase="binary"    )->     Tuple:
 __UpperCAmelCase					=   simple_accuracy(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   float(fa_score(y_true=_lowerCAmelCase       ,    y_pred=_lowerCAmelCase       ,    average=_lowerCAmelCase    )    )
 return {
     "accuracy": acc,
     "f1": fa,
 }







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   {}
 for id_pred, label in zip(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  __UpperCAmelCase					=   F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
  __UpperCAmelCase					=   id_pred['prediction']
  if question_id in question_map:
   question_map[question_id].append((pred, label)    )
  else:
   __UpperCAmelCase					=   [(pred, label)]
 __UpperCAmelCase					,    __UpperCAmelCase					=   [], []
 for question, preds_labels in question_map.items():
  __UpperCAmelCase					,    __UpperCAmelCase					=   zip(*_lowerCAmelCase    )
  __UpperCAmelCase					=   fa_score(y_true=_lowerCAmelCase       ,    y_pred=_lowerCAmelCase       ,    average='macro'    )
  fas.append(_lowerCAmelCase    )
  __UpperCAmelCase					=   int(sum(pred == label for pred, label in preds_labels    ) == len(_lowerCAmelCase    )    )
  ems.append(_lowerCAmelCase    )
 __UpperCAmelCase					=   float(sum(_lowerCAmelCase    ) / len(_lowerCAmelCase    )    )
 __UpperCAmelCase					=   sum(_lowerCAmelCase    ) / len(_lowerCAmelCase    )
 __UpperCAmelCase					=   float(fa_score(y_true=_lowerCAmelCase       ,    y_pred=[id_pred['prediction'] for id_pred in ids_preds]    )    )
 return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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



    def 					__lowerCamelCase						(  self					):
     if self.config_name not in [
         "boolq",
         "cb",
         "copa",
         "multirc",
         "record",
         "rte",
         "wic",
         "wsc",
         "wsc.fixed",
         "axb",
         "axg",
     ]:
      raise KeyError(
          'You should supply a configuration name selected in '
          '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'					)
     return datasets.MetricInfo(
         description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(self._get_feature_types()					)     ,			codebase_urls=[]     ,			reference_urls=[]     ,			format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None     ,			)



    def 					__lowerCamelCase						(  self					):
     if self.config_name == "record":
      return {
          "predictions": {
              "idx": {
                  "passage": datasets.Value('int64'					),
                  "query": datasets.Value('int64'					),
              },
              "prediction_text": datasets.Value('string'					),
          },
          "references": {
              "idx": {
                  "passage": datasets.Value('int64'					),
                  "query": datasets.Value('int64'					),
              },
              "answers": datasets.Sequence(datasets.Value('string'					)					),
          },
      }
     elif self.config_name == "multirc":
      return {
          "predictions": {
              "idx": {
                  "answer": datasets.Value('int64'					),
                  "paragraph": datasets.Value('int64'					),
                  "question": datasets.Value('int64'					),
              },
              "prediction": datasets.Value('int64'					),
          },
          "references": datasets.Value('int64'					),
      }
     else:
      return {
          "predictions": datasets.Value('int64'					),
          "references": datasets.Value('int64'					),
      }




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     if self.config_name == "axb":
      return {"matthews_correlation": matthews_corrcoef(__A     ,			__A					)}
     elif self.config_name == "cb":
      return acc_and_fa(__A     ,			__A     ,			fa_avg='macro'					)
     elif self.config_name == "record":
      __UpperCAmelCase					=   [
          {
              'qas': [
                  {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                  for ref in references
              ]
          }
      ]
      __UpperCAmelCase					=   {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
      return evaluate_record(__A     ,			__A					)[0]
     elif self.config_name == "multirc":
      return evaluate_multirc(__A     ,			__A					)
     elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
      return {"accuracy": simple_accuracy(__A     ,			__A					)}
     else:
      raise KeyError(
          'You should supply a configuration name selected in '
          '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'					)





