
"""simple docstring"""

import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      List[str]          =   r'\w+[.]\d+'
		lowerCamelCase					:      str          =   re.findall(a_,						a_      )
		for pat in pats:
				lowerCamelCase					:      List[Any]          =   key.replace(a_,						'_'.join(pat.split('.'      )      )      )
		return key
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   pt_tuple_key[:-1] + ('scale',)
		if (
		    any('norm' in str_ for str_ in pt_tuple_key      )
		    and (pt_tuple_key[-1] == "bias")
		    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
		    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
		):
				lowerCamelCase					:      Optional[Any]          =   pt_tuple_key[:-1] + ('scale',)
				return renamed_pt_tuple_key, pt_tensor
		elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
				lowerCamelCase					:      List[str]          =   pt_tuple_key[:-1] + ('scale',)
				return renamed_pt_tuple_key, pt_tensor

		# embedding
		if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
				lowerCamelCase					:      Optional[Any]          =   pt_tuple_key[:-1] + ('embedding',)
				return renamed_pt_tuple_key, pt_tensor

		# conv layer
		lowerCamelCase					:      Tuple          =   pt_tuple_key[:-1] + ('kernel',)
		if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
				lowerCamelCase					:      Any          =   pt_tensor.transpose(2,						3,						1,						0      )
				return renamed_pt_tuple_key, pt_tensor

		# linear layer
		lowerCamelCase					:      Dict          =   pt_tuple_key[:-1] + ('kernel',)
		if pt_tuple_key[-1] == "weight":
				lowerCamelCase					:      List[Any]          =   pt_tensor.T
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm weight
		lowerCamelCase					:      Any          =   pt_tuple_key[:-1] + ('weight',)
		if pt_tuple_key[-1] == "gamma":
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm bias
		lowerCamelCase					:      int          =   pt_tuple_key[:-1] + ('bias',)
		if pt_tuple_key[-1] == "beta":
				return renamed_pt_tuple_key, pt_tensor

		return pt_tuple_key, pt_tensor





def 		UpperCAmelCase (		a_,						a_,						a_=42      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   {k: v.numpy() for k, v in pt_state_dict.items()}

		# Step 2: Since the model is stateless, get random Flax params
		lowerCamelCase					:      str          =   flax_model.init_weights(PRNGKey(a_      )      )

		lowerCamelCase					:      Optional[Any]          =   flatten_dict(a_      )
		lowerCamelCase					:      int          =   {}

		# Need to change some parameters name to match Flax names
		for pt_key, pt_tensor in pt_state_dict.items():
				lowerCamelCase					:      Optional[Any]          =   rename_key(a_      )
				lowerCamelCase					:      Any          =   tuple(renamed_pt_key.split('.'      )      )

				# Correctly rename weight parameters
				lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   rename_key_and_reshape_tensor(a_,						a_,						a_      )

				if flax_key in random_flax_state_dict:
						if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
								raise ValueError(
								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""      )

        # also add unexpected weight so that warning is thrown
				lowerCamelCase					:      str          =   jnp.asarray(a_      )

		return unflatten_dict(a_      )








"""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,
					)






