
"""simple docstring"""

import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encodec'




	def __init__(			self		,			UpperCAmelCase_=[1.5, 3.0, 6.0, 12.0, 24.0]		,			UpperCAmelCase_=24000		,			UpperCAmelCase_=1		,			UpperCAmelCase_=False		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=128		,			UpperCAmelCase_=32		,			UpperCAmelCase_=1		,			UpperCAmelCase_=[8, 5, 4, 2]		,			UpperCAmelCase_="weight_norm"		,			UpperCAmelCase_=7		,			UpperCAmelCase_=7		,			UpperCAmelCase_=3		,			UpperCAmelCase_=2		,			UpperCAmelCase_=True		,			UpperCAmelCase_="reflect"		,			UpperCAmelCase_=2		,			UpperCAmelCase_=2		,			UpperCAmelCase_=1.0		,			UpperCAmelCase_=1024		,			UpperCAmelCase_=None		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							Tuple:
			lowerCamelCase					:      Any          =   target_bandwidths
			lowerCamelCase					:      Any          =   sampling_rate
			lowerCamelCase					:      Any          =   audio_channels
			lowerCamelCase					:      int          =   normalize
			lowerCamelCase					:      Any          =   chunk_length_s
			lowerCamelCase					:      Any          =   overlap
			lowerCamelCase					:      Dict          =   hidden_size
			lowerCamelCase					:      str          =   num_filters
			lowerCamelCase					:      Optional[Any]          =   num_residual_layers
			lowerCamelCase					:      Optional[int]          =   upsampling_ratios
			lowerCamelCase					:      int          =   norm_type
			lowerCamelCase					:      Any          =   kernel_size
			lowerCamelCase					:      List[Any]          =   last_kernel_size
			lowerCamelCase					:      Optional[Any]          =   residual_kernel_size
			lowerCamelCase					:      Optional[Any]          =   dilation_growth_rate
			lowerCamelCase					:      Optional[Any]          =   use_causal_conv
			lowerCamelCase					:      Dict          =   pad_mode
			lowerCamelCase					:      Any          =   compress
			lowerCamelCase					:      Dict          =   num_lstm_layers
			lowerCamelCase					:      Any          =   trim_right_ratio
			lowerCamelCase					:      Any          =   codebook_size
			lowerCamelCase					:      Tuple          =   codebook_dim if codebook_dim is not None else hidden_size
			lowerCamelCase					:      Any          =   use_conv_shortcut

			if self.norm_type not in ["weight_norm", "time_group_norm"]:
					raise ValueError(
					    F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )

			super().__init__(**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[int]:
			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[int]:
			if self.chunk_length_s is None or self.overlap is None:
					return None
			else:
					return max(1		,			int((1.0 - self.overlap) * self.chunk_length ) )




	@property
	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Any          =   np.prod(self.upsampling_ratios )
			return math.ceil(self.sampling_rate / hop_length )




	@property
	def       _UpperCamelCase   (			self )		->							int:
			return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )








"""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 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		hf_model.apply_weight_norm()

		lowerCamelCase					:      str          =   checkpoint['input_conv.weight_g']
		lowerCamelCase					:      int          =   checkpoint['input_conv.weight_v']
		lowerCamelCase					:      Optional[Any]          =   checkpoint['input_conv.bias']

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

						lowerCamelCase					:      Dict          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
						lowerCamelCase					:      str          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
						lowerCamelCase					:      Optional[Any]          =   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 		UpperCAmelCase (		a_,						a_,						a_,						a_=None,						a_=None,						):





		'''simple docstring'''


		if config_path is not None:
				lowerCamelCase					:      str          =   SpeechTaHifiGanConfig.from_pretrained(a_      )
		else:
				lowerCamelCase					:      Dict          =   SpeechTaHifiGanConfig()

		lowerCamelCase					:      int          =   SpeechTaHifiGan(a_      )

		lowerCamelCase					:      Optional[Any]          =   torch.load(a_      )
		load_weights(orig_checkpoint['model']['generator'],						a_,						a_      )

		lowerCamelCase					:      Tuple          =   np.load(a_      )
		lowerCamelCase					:      str          =   stats[0].reshape(-1      )
		lowerCamelCase					:      Optional[int]          =   stats[1].reshape(-1      )
		lowerCamelCase					:      Dict          =   torch.from_numpy(a_      ).float()
		lowerCamelCase					:      Optional[int]          =   torch.from_numpy(a_      ).float()

		model.save_pretrained(a_      )

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


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,
					)






