






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


lowerCAmelCase_           = (
    '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      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[int]   ):
				warnings.warn(UpperCAmelCase,						UpperCAmelCase   )
				requires_backends(UpperCAmelCase,						'sklearn'   )
				return (preds == labels).mean()







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Tuple   ):
				warnings.warn(UpperCAmelCase,						UpperCAmelCase   )
				requires_backends(UpperCAmelCase,						'sklearn'   )
				A	 =      simple_accuracy(UpperCAmelCase,						UpperCAmelCase   )
				A	 =      fa_score(y_true=UpperCAmelCase,						y_pred=UpperCAmelCase   )
				return {
				    "acc": acc,
				    "f1": fa,
				    "acc_and_f1": (acc + fa) / 2,
				}







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Dict   ):
				warnings.warn(UpperCAmelCase,						UpperCAmelCase   )
				requires_backends(UpperCAmelCase,						'sklearn'   )
				A	 =      pearsonr(UpperCAmelCase,						UpperCAmelCase   )[0]
				A	 =      spearmanr(UpperCAmelCase,						UpperCAmelCase   )[0]
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				    "corr": (pearson_corr + spearman_corr) / 2,
				}







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







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Optional[Any]   ):
				warnings.warn(UpperCAmelCase,						UpperCAmelCase   )
				requires_backends(UpperCAmelCase,						'sklearn'   )
				if len(UpperCAmelCase   ) != len(UpperCAmelCase   ):
								raise ValueError(f'Predictions and labels have mismatched lengths {len(UpperCAmelCase   )} and {len(UpperCAmelCase   )}'   )
				if task_name == "xnli":
								return {"acc": simple_accuracy(UpperCAmelCase,						UpperCAmelCase   )}
				else:
								raise KeyError(UpperCAmelCase   )













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





