"""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


__A  =				(
    '''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    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Any      ) ->      str:



							'''simple docstring'''

							warnings.warn(_lowerCamelCase						,   _lowerCamelCase      )
							requires_backends(_lowerCamelCase						,   "sklearn"      )
							return (preds == labels).mean()


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Any      ) ->      Optional[Any]:



							'''simple docstring'''

							warnings.warn(_lowerCamelCase						,   _lowerCamelCase      )
							requires_backends(_lowerCamelCase						,   "sklearn"      )
							__lowerCamelCase					:		List[str]		   =     simple_accuracy(_lowerCamelCase						,   _lowerCamelCase      )
							__lowerCamelCase					:		Union[str, Any]		   =     fa_score(y_true=_lowerCamelCase						,   y_pred=_lowerCamelCase      )
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Any:



							'''simple docstring'''

							warnings.warn(_lowerCamelCase						,   _lowerCamelCase      )
							requires_backends(_lowerCamelCase						,   "sklearn"      )
							__lowerCamelCase					:		Optional[int]		   =     pearsonr(_lowerCamelCase						,   _lowerCamelCase      )[0]
							__lowerCamelCase					:		int		   =     spearmanr(_lowerCamelCase						,   _lowerCamelCase      )[0]
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							    "corr": (pearson_corr + spearman_corr) / 2,
							}


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	Optional[Any]      ) ->      Union[str, Any]:



							'''simple docstring'''

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







def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Any      ) ->      Optional[Any]:



							'''simple docstring'''

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





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__A  =				logging.get_logger('''transformers.models.speecht5''')


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Union[str, Any]:



							'''simple docstring'''

							hf_model.apply_weight_norm()

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["input_conv.weight_g"]
							__lowerCamelCase					:		int		   =     checkpoint["input_conv.weight_v"]
							__lowerCamelCase					:		Dict		   =     checkpoint["input_conv.bias"]

							for i in range(len(config.upsample_rates      )      ):
														__lowerCamelCase					:		Dict		   =     checkpoint[F"""upsamples.{i}.1.weight_g"""]
														__lowerCamelCase					:		Optional[int]		   =     checkpoint[F"""upsamples.{i}.1.weight_v"""]
														__lowerCamelCase					:		Any		   =     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      )      ):
																					__lowerCamelCase					:		List[str]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
																					__lowerCamelCase					:		Union[str, Any]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

							__lowerCamelCase					:		Any		   =     checkpoint["output_conv.1.weight_g"]
							__lowerCamelCase					:		Tuple		   =     checkpoint["output_conv.1.weight_v"]
							__lowerCamelCase					:		int		   =     checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Optional[int]=None						,   ) ->      Optional[int]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SpeechTaHifiGanConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Tuple		   =     SpeechTaHifiGanConfig()

							__lowerCamelCase					:		List[Any]		   =     SpeechTaHifiGan(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     torch.load(_lowerCamelCase      )
							load_weights(orig_checkpoint["model"]["generator"]						,   _lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     np.load(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     stats[0].reshape(-1      )
							__lowerCamelCase					:		Optional[int]		   =     stats[1].reshape(-1      )
							__lowerCamelCase					:		int		   =     torch.from_numpy(_lowerCamelCase      ).float()
							__lowerCamelCase					:		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__":
				__A  =				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.'''
				)

				__A  =				parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)