



'''simple docstring'''





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase_		       =  logging.get_logger("transformers.models.speecht5")




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     hf_model.apply_weight_norm()

     _a      =  checkpoint['input_conv.weight_g']
     _a      =  checkpoint['input_conv.weight_v']
     _a      =  checkpoint['input_conv.bias']

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

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

     _a      =  checkpoint['output_conv.1.weight_g']
     _a      =  checkpoint['output_conv.1.weight_v']
     _a      =  checkpoint['output_conv.1.bias']

     hf_model.remove_weight_norm()




@torch.no_grad()
def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A=None  ,	__A=None  ,	):



     """simple docstring"""



     if config_path is not None:
          _a      =  SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_)
     else:
          _a      =  SpeechTaHifiGanConfig()

     _a      =  SpeechTaHifiGan(SCREAMING_SNAKE_CASE_)

     _a      =  torch.load(SCREAMING_SNAKE_CASE_)
     load_weights(orig_checkpoint['''model''']['''generator''']  ,	SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_)

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

     model.save_pretrained(SCREAMING_SNAKE_CASE_)

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


if __name__ == "__main__":
  lowercase_		       =  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."
  )

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






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


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       int														=     """switch_transformers"""
    __lowerCamelCase						:       Optional[Any]														=     ["""past_key_values"""]
    __lowerCamelCase						:       Any														=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





    def __init__(      self						,   a=3_2128						,   a=768						,   a=64						,   a=2048						,   a=64						,   a=12						,   a=3						,   a=12						,   a=3						,   a=12						,   a=8						,   a=False						,   a=0.01						,   a="float32"						,   a=False						,   a=32						,   a=128						,   a=0.1						,   a=1e-6						,   a=0.001						,   a=0.001						,   a=1.0						,   a="relu"						,   a=True						,   a=False						,   a=True						,   a=0						,   a=1						,   **a						,   ):
         lowercase__    :   Optional[int]      =       vocab_size
         lowercase__    :   List[Any]      =       d_model
         lowercase__    :   List[Any]      =       d_kv
         lowercase__    :   Any      =       d_ff

         lowercase__    :   Optional[int]      =       num_sparse_encoder_layers

         lowercase__    :   Tuple      =       num_layers
         lowercase__    :   Optional[Any]      =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__    :   str      =       num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
              lowercase__    :   Optional[Any]      =       self.num_layers // self.num_sparse_encoder_layers
         else:
              lowercase__    :   Optional[int]      =       self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
              lowercase__    :   Union[str, Any]      =       self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              lowercase__    :   int      =       self.num_decoder_layers  # HACK: this will create 0 sparse layers

         lowercase__    :   List[Any]      =       num_heads
         lowercase__    :   Union[str, Any]      =       num_experts
         lowercase__    :   str      =       expert_capacity
         lowercase__    :   List[Any]      =       router_bias
         lowercase__    :   Optional[int]      =       router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
         lowercase__    :   str      =       router_dtype

         lowercase__    :   Optional[int]      =       router_ignore_padding_tokens
         lowercase__    :   int      =       relative_attention_num_buckets
         lowercase__    :   Optional[Any]      =       relative_attention_max_distance

         lowercase__    :   List[str]      =       dropout_rate
         lowercase__    :   str      =       layer_norm_epsilon
         lowercase__    :   int      =       initializer_factor
         lowercase__    :   int      =       feed_forward_proj
         lowercase__    :   Dict      =       use_cache
         lowercase__    :   int      =       add_router_probs

         lowercase__    :   int      =       router_z_loss_coef
         lowercase__    :   List[Any]      =       router_aux_loss_coef

         lowercase__    :   int      =       self.feed_forward_proj.split('-')
         lowercase__    :   Optional[int]      =       act_info[-1]
         lowercase__    :   Dict      =       act_info[0] == 'gated'

         if len(a) > 1 and act_info[0] != "gated" or len(a) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                  '\'gated-gelu\' or \'relu\'')

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              lowercase__    :   Optional[int]      =       'gelu_new'

         super().__init__(
             pad_token_id=a						,   eos_token_id=a						,   is_encoder_decoder=a						,   **a						,   )

