





'''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()
lowerCAmelCase					:     List[str]												=logging.get_logger(__name__)

lowerCAmelCase					:     Any												='''Hello, World!'''
lowerCAmelCase					:     Any												='''en_XX'''
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					str ):
   lowercase_			:Tuple    			= Path("data_bin" )
   lowercase_			:str    			= FairseqXmodModel.from_pretrained(
       model_name_or_path=str(Path(__snake_case ).parent )	,checkpoint_file=Path(__snake_case ).name	,_name="xmod_base"	,arch="xmod_base"	,task="multilingual_masked_lm"	,data_name_or_path=str(__snake_case )	,bpe="sentencepiece"	,sentencepiece_model=str(Path(__snake_case ).parent / "sentencepiece.bpe.model" )	,src_dict=str(data_dir / "dict.txt" )	,)
   xmod.eval()  # disable dropout
   print(__snake_case )

   lowercase_			:List[str]    			= xmod.model.encoder.sentence_encoder
   lowercase_			:int    			= 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:
      lowercase_			:Any    			= xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

   print("Our X-MOD config:"	,__snake_case )

   lowercase_			:Optional[int]    			= XmodForSequenceClassification(__snake_case ) if classification_head else XmodForMaskedLM(__snake_case )
   model.eval()

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

   lowercase_			:List[str]    			= xmod_sent_encoder.layernorm_embedding.weight
   lowercase_			:List[str]    			= xmod_sent_encoder.layernorm_embedding.bias

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

      # self attention
      lowercase_			:Optional[int]    			= 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." )

      lowercase_			:Any    			= xmod_layer.self_attn.q_proj.weight
      lowercase_			:Optional[int]    			= xmod_layer.self_attn.q_proj.bias
      lowercase_			:Optional[int]    			= xmod_layer.self_attn.k_proj.weight
      lowercase_			:int    			= xmod_layer.self_attn.k_proj.bias
      lowercase_			:Union[str, Any]    			= xmod_layer.self_attn.v_proj.weight
      lowercase_			:Optional[Any]    			= xmod_layer.self_attn.v_proj.bias

      # self-attention output
      lowercase_			:Tuple    			= 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." )
      lowercase_			:Union[str, Any]    			= xmod_layer.self_attn.out_proj.weight
      lowercase_			:Union[str, Any]    			= xmod_layer.self_attn.out_proj.bias
      lowercase_			:Any    			= xmod_layer.self_attn_layer_norm.weight
      lowercase_			:str    			= xmod_layer.self_attn_layer_norm.bias

      # intermediate
      lowercase_			:Union[str, Any]    			= layer.intermediate
      if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
         raise AssertionError("Dimensions of intermediate weights do not match." )
      lowercase_			:int    			= xmod_layer.fca.weight
      lowercase_			:List[str]    			= xmod_layer.fca.bias

      # output
      lowercase_			:Optional[Any]    			= layer.output
      if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
         raise AssertionError("Dimensions of feed-forward weights do not match." )
      lowercase_			:Any    			= xmod_layer.fca.weight
      lowercase_			:Optional[Any]    			= xmod_layer.fca.bias
      lowercase_			:Dict    			= xmod_layer.final_layer_norm.weight
      lowercase_			:Union[str, Any]    			= xmod_layer.final_layer_norm.bias
      if bert_output.adapter_layer_norm is not None:
         lowercase_			:List[str]    			= xmod_layer.adapter_layer_norm.weight
         lowercase_			:List[Any]    			= 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():
         lowercase_			:Dict    			= bert_output.adapter_modules[lang_code]
         lowercase_			:List[str]    			= xmod_layer.adapter_modules[lang_code]
         lowercase_			:List[Any]    			= from_adapter.fca.weight
         lowercase_			:Optional[Any]    			= from_adapter.fca.bias
         lowercase_			:Any    			= from_adapter.fca.weight
         lowercase_			:int    			= from_adapter.fca.bias

      # end of layer

   if xmod_sent_encoder.layer_norm is not None:
      lowercase_			:Dict    			= xmod_sent_encoder.layer_norm.weight
      lowercase_			:str    			= xmod_sent_encoder.layer_norm.bias

   if classification_head:
      lowercase_			:Union[str, Any]    			= xmod.model.classification_heads["mnli"].dense.weight
      lowercase_			:List[str]    			= xmod.model.classification_heads["mnli"].dense.bias
      lowercase_			:Optional[Any]    			= xmod.model.classification_heads["mnli"].out_proj.weight
      lowercase_			:str    			= xmod.model.classification_heads["mnli"].out_proj.bias
   else:
      # LM Head
      lowercase_			:List[Any]    			= xmod.model.encoder.lm_head.dense.weight
      lowercase_			:Dict    			= xmod.model.encoder.lm_head.dense.bias
      lowercase_			:Optional[int]    			= xmod.model.encoder.lm_head.layer_norm.weight
      lowercase_			:int    			= xmod.model.encoder.lm_head.layer_norm.bias
      lowercase_			:int    			= xmod.model.encoder.lm_head.weight
      lowercase_			:Any    			= xmod.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   lowercase_			:Optional[Any]    			= xmod.encode(__snake_case ).unsqueeze(0 )  # batch of size 1
   model.roberta.set_default_language(__snake_case )

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

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


if __name__ == "__main__":
 lowerCAmelCase					:     Optional[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.'''
 )
 lowerCAmelCase					:     List[str]												=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 matthews_corrcoef

import datasets


_a														=		"""
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

_a														=		"""
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

_a														=		"""\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""






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



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int32'''),
							            '''references''': datasets.Value('''int32'''),
							        })				,		reference_urls=[
							        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
							    ]				,		)




		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a=None)       ->       Dict:






							'''simple docstring'''
							return {
							    "matthews_correlation": float(matthews_corrcoef(__a				,		__a				,		sample_weight=__a)),
							}






