





'''simple docstring'''




import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__SCREAMING_SNAKE_CASE     :List[Any]       =				['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
__SCREAMING_SNAKE_CASE     :int       =				{'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
  raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :str       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Dict       =				''' Hello world! cécé herlolip'''

__SCREAMING_SNAKE_CASE     :Tuple       =				[
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   [
         "encoder.version",
         "decoder.version",
         "model.encoder.version",
         "model.decoder.version",
         "_float_tensor",
     ]
     for k in ignore_keys:
          state_dict.pop(__lowercase			,     __lowercase       )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      Optional[int]			,     __lowercase							:      Dict       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   dct.pop(__lowercase       )
     _UpperCAmelCase     							=   val
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   torch.load(__lowercase			,     map_location="cpu"       )
     _UpperCAmelCase     							=   torch.hub.load("pytorch/fairseq"			,     "bart.large.cnn"       ).eval()
     hub_interface.model.load_state_dict(sd["model"]       )
     return hub_interface
def 	UpperCAmelCase_							(					__lowercase							:      Tuple       )		->       Union[str, Any]:


     '''simple docstring'''



     _UpperCAmelCase							,  _UpperCAmelCase     							=   emb.weight.shape
     _UpperCAmelCase     							=   nn.Linear(__lowercase			,     __lowercase			,     bias=__lowercase       )
     _UpperCAmelCase     							=   emb.weight.data
     return lin_layer
@torch.no_grad()
def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      int			,     __lowercase							:      Tuple=None       )		->       Tuple:


     '''simple docstring'''



     if not os.path.exists(__lowercase       ):
          _UpperCAmelCase     							=   torch.hub.load("pytorch/fairseq"			,     __lowercase       ).eval()
     else:
          _UpperCAmelCase     							=   load_xsum_checkpoint(__lowercase       )

     bart.model.upgrade_state_dict(bart.model.state_dict()       )
     if hf_checkpoint_name is None:
          _UpperCAmelCase     							=   checkpoint_path.replace("."			,     "-"       )
     _UpperCAmelCase     							=   BartConfig.from_pretrained(__lowercase       )
     _UpperCAmelCase     							=   bart.encode(__lowercase       ).unsqueeze(0       )
     _UpperCAmelCase     							=   BartTokenizer.from_pretrained(__lowercase       ).encode(__lowercase			,     return_tensors="pt"       ).unsqueeze(0       )
     if not torch.eq(__lowercase			,     __lowercase       ).all():
          raise ValueError(
              f'converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'       )

     if checkpoint_path == "bart.large.mnli":
          _UpperCAmelCase     							=   bart.state_dict()
          remove_ignore_keys_(__lowercase       )
          _UpperCAmelCase     							=   state_dict["model.decoder.embed_tokens.weight"]
          for src, dest in mnli_rename_keys:
               rename_key(__lowercase			,     __lowercase			,     __lowercase       )
          _UpperCAmelCase     							=   BartForSequenceClassification(__lowercase       ).eval()
          model.load_state_dict(__lowercase       )
          _UpperCAmelCase     							=   bart.predict("mnli"			,     __lowercase			,     return_logits=__lowercase       )
          _UpperCAmelCase     							=   model(__lowercase       )[0]  # logits
     else:  # no classification heads to worry about
          _UpperCAmelCase     							=   bart.model.state_dict()
          remove_ignore_keys_(__lowercase       )
          _UpperCAmelCase     							=   state_dict["decoder.embed_tokens.weight"]
          _UpperCAmelCase     							=   bart.extract_features(__lowercase       )
          if hf_checkpoint_name == "facebook/bart-large":
               _UpperCAmelCase     							=   BartModel(__lowercase       ).eval()
               model.load_state_dict(__lowercase       )
               _UpperCAmelCase     							=   model(__lowercase       ).model[0]
          else:
               _UpperCAmelCase     							=   BartForConditionalGeneration(__lowercase       ).eval()  # an existing summarization ckpt
               model.model.load_state_dict(__lowercase       )
               if hasattr(__lowercase			,     "lm_head"       ):
                    _UpperCAmelCase     							=   make_linear_from_emb(model.model.shared       )
               _UpperCAmelCase     							=   model.model(__lowercase       )[0]

    # Check results
     if fairseq_output.shape != new_model_outputs.shape:
          raise ValueError(
              f'`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'       )
     if (fairseq_output != new_model_outputs).any().item():
          raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"       )
     Path(__lowercase       ).mkdir(exist_ok=__lowercase       )
     model.save_pretrained(__lowercase       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :Optional[Any]       =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
  )
  parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument(
      '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
  )
  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)











'''simple docstring'''




import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__SCREAMING_SNAKE_CASE     :List[str]       =				(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     return (preds == labels).mean()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   simple_accuracy(__lowercase			,     __lowercase       )
     _UpperCAmelCase     							=   fa_score(y_true=__lowercase			,     y_pred=__lowercase       )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      List[str]       )		->       List[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   pearsonr(__lowercase			,     __lowercase       )[0]
     _UpperCAmelCase     							=   spearmanr(__lowercase			,     __lowercase       )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      str			,     __lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     assert len(__lowercase       ) == len(__lowercase       ), f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'
     if task_name == "cola":
          return {"mcc": matthews_corrcoef(__lowercase			,     __lowercase       )}
     elif task_name == "sst-2":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mrpc":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "sts-b":
          return pearson_and_spearman(__lowercase			,     __lowercase       )
     elif task_name == "qqp":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "mnli":
          return {"mnli/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mnli-mm":
          return {"mnli-mm/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "qnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "rte":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "wnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "hans":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )






def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Dict			,     __lowercase							:      str       )		->       Union[str, Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     if len(__lowercase       ) != len(__lowercase       ):
          raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'       )
     if task_name == "xnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )




