



'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase__:   List[str]								=			logging.get_logger("transformers.models.speecht5")


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   List[Any]:
							hf_model.apply_weight_norm()

							UpperCAmelCase :       List[Any] 							=		checkpoint['''input_conv.weight_g''']
							UpperCAmelCase :       Optional[int] 							=		checkpoint['''input_conv.weight_v''']
							UpperCAmelCase :       Any 							=		checkpoint['''input_conv.bias''']

							for i in range(len(config.upsample_rates  )  ):
														UpperCAmelCase :       str 							=		checkpoint[f"""upsamples.{i}.1.weight_g"""]
														UpperCAmelCase :       Optional[int] 							=		checkpoint[f"""upsamples.{i}.1.weight_v"""]
														UpperCAmelCase :       str 							=		checkpoint[f"""upsamples.{i}.1.bias"""]

							for i in range(len(config.upsample_rates  ) * len(config.resblock_kernel_sizes  )  ):
														for j in range(len(config.resblock_dilation_sizes  )  ):
																					UpperCAmelCase :       str 							=		checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
																					UpperCAmelCase :       List[str] 							=		checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
																					UpperCAmelCase :       Optional[int] 							=		checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

																					UpperCAmelCase :       List[Any] 							=		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					UpperCAmelCase :       Union[str, Any] 							=		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					UpperCAmelCase :       str 							=		checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

							UpperCAmelCase :       Tuple 							=		checkpoint['''output_conv.1.weight_g''']
							UpperCAmelCase :       Union[str, Any] 							=		checkpoint['''output_conv.1.weight_v''']
							UpperCAmelCase :       Dict 							=		checkpoint['''output_conv.1.bias''']

							hf_model.remove_weight_norm()


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Union[str, Any]=None   ,      _lowerCAmelCase  :						List[str]=None   ,      )      ->   List[Any]:
							if config_path is not None:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig()

							UpperCAmelCase :       Dict 							=		SpeechTaHifiGan(_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		torch.load(_lowerCAmelCase  )
							load_weights(orig_checkpoint['''model''']['''generator''']   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Any 							=		np.load(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		stats[0].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		stats[1].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		torch.from_numpy(_lowerCAmelCase  ).float()
							UpperCAmelCase :       List[str] 							=		torch.from_numpy(_lowerCAmelCase  ).float()

							model.save_pretrained(_lowerCAmelCase  )

							if repo_id:
														print('''Pushing to the hub...'''  )
														model.push_to_hub(_lowerCAmelCase  )


if __name__ == "__main__":
						UpperCamelCase__:   List[Any]								=			argparse.ArgumentParser()
						parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
						parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						parser.add_argument(
						    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
						)

						UpperCamelCase__:   Optional[int]								=			parser.parse_args()
						convert_hifigan_checkpoint(
						    args.checkpoint_path,
						    args.stats_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.push_to_hub,
						)












'''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       )




