




'''simple docstring'''





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							:   str      		= logging.get_logger("transformers.models.speecht5")


def      UpperCamelCase_(	snake_case							:       Union[str, Any]							,				snake_case							:       Optional[Any]							,				snake_case							:       Optional[Any]     ):

							'''simple docstring'''






							hf_model.apply_weight_norm()

							snake_case_						=      checkpoint["input_conv.weight_g"]
							snake_case_						=      checkpoint["input_conv.weight_v"]
							snake_case_						=      checkpoint["input_conv.bias"]

							for i in range(len(config.upsample_rates     )     ):
														snake_case_						=      checkpoint[f'upsamples.{i}.1.weight_g']
														snake_case_						=      checkpoint[f'upsamples.{i}.1.weight_v']
														snake_case_						=      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     )     ):
																					snake_case_						=      checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
																					snake_case_						=      checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
																					snake_case_						=      checkpoint[f'blocks.{i}.convs1.{j}.1.bias']

																					snake_case_						=      checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
																					snake_case_						=      checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
																					snake_case_						=      checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

							snake_case_						=      checkpoint["output_conv.1.weight_g"]
							snake_case_						=      checkpoint["output_conv.1.weight_v"]
							snake_case_						=      checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()


@torch.no_grad()
def      UpperCamelCase_(	snake_case							:       Optional[int]							,				snake_case							:       Optional[Any]							,				snake_case							:       Dict							,				snake_case							:       str=None							,				snake_case							:       Dict=None							,				):

							'''simple docstring'''






							if config_path is not None:
														snake_case_						=      SpeechTaHifiGanConfig.from_pretrained(__snake_case     )
							else:
														snake_case_						=      SpeechTaHifiGanConfig()

							snake_case_						=      SpeechTaHifiGan(__snake_case     )

							snake_case_						=      torch.load(__snake_case     )
							load_weights(orig_checkpoint["model"]["generator"]							,				__snake_case							,				__snake_case     )

							snake_case_						=      np.load(__snake_case     )
							snake_case_						=      stats[0].reshape(-1     )
							snake_case_						=      stats[1].reshape(-1     )
							snake_case_						=      torch.from_numpy(__snake_case     ).float()
							snake_case_						=      torch.from_numpy(__snake_case     ).float()

							model.save_pretrained(__snake_case     )

							if repo_id:
														print("Pushing to the hub..."     )
														model.push_to_hub(__snake_case     )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   int      		= 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."
		)

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











import numpy as np

from transformers import Pipeline





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		np.max(__snake_case       ,axis=-1       ,keepdims=__snake_case							)
	lowerCamelCase__								=		np.exp(outputs - maxes							)
	return shifted_exp / shifted_exp.sum(axis=-1       ,keepdims=__snake_case							)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{}
				if "second_text" in kwargs:
					lowerCamelCase__								=		kwargs['''second_text''']
				return preprocess_kwargs, {}, {}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







				return self.tokenizer(__lowerCAmelCase       ,  text_pair=__lowerCAmelCase       ,  return_tensors=self.framework							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.model(**__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		model_outputs.logits[0].numpy()
				lowerCamelCase__								=		softmax(__lowerCAmelCase							)

				lowerCamelCase__								=		np.argmax(__lowerCAmelCase							)
				lowerCamelCase__								=		self.model.config.idalabel[best_class]
				lowerCamelCase__								=		probabilities[best_class].item()
				lowerCamelCase__								=		logits.tolist()
				return {"label": label, "score": score, "logits": logits}



