


'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
A				:					Dict      						=			logging.get_logger('''transformers.models.speecht5''')






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Dict  ,lowerCamelCase					:      List[Any]       ):
       hf_model.apply_weight_norm()

       _A					:     str     = checkpoint['input_conv.weight_g']
       _A					:     List[Any]     = checkpoint['input_conv.weight_v']
       _A					:     int     = checkpoint['input_conv.bias']

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

                     _A					:     Any     = checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     _A					:     Union[str, Any]     = checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     _A					:     List[Any]     = checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       _A					:     List[Any]     = checkpoint['output_conv.1.weight_g']
       _A					:     str     = checkpoint['output_conv.1.weight_v']
       _A					:     List[str]     = checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      Any  ,lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      Tuple=None  ,lowerCamelCase					:      Tuple=None  ,):

       if config_path is not None:
              _A					:     List[Any]     = SpeechTaHifiGanConfig.from_pretrained(a__       )
       else:
              _A					:     int     = SpeechTaHifiGanConfig()

       _A					:     Any     = SpeechTaHifiGan(a__       )

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

       _A					:     str     = np.load(a__       )
       _A					:     Union[str, Any]     = stats[0].reshape(-1       )
       _A					:     int     = stats[1].reshape(-1       )
       _A					:     Dict     = torch.from_numpy(a__       ).float()
       _A					:     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				:					str      						=			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				:					List[str]      						=			parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )










'''simple docstring'''







import operator as op


A				:					List[str]    						=			'''scaler.pt'''
A				:					Optional[int]    						=			'''pytorch_model'''
A				:					Union[str, Any]    						=			'''random_states'''
A				:					List[Any]    						=			'''optimizer'''
A				:					Optional[Any]    						=			'''scheduler'''
A				:					Optional[int]    						=			'''pytorch_model.bin'''
A				:					List[str]    						=			'''pytorch_model.bin.index.json'''
A				:					List[Any]    						=			'''model.safetensors'''
A				:					int    						=			'''model.safetensors.index.json'''
A				:					List[str]    						=			'''1.10.2'''
A				:					str    						=			'''py38'''
A				:					int    						=			'''4.17.0'''
A				:					Tuple    						=			['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
A				:					Union[str, Any]    						=			['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
A				:					Optional[Any]    						=			['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
A				:					Dict    						=			['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
A				:					Any    						=			['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
A				:					Tuple    						=			'''2.0.1'''
A				:					List[str]    						=			['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
A				:					Union[str, Any]    						=			['''default''', '''reduce-overhead''', '''max-autotune''']

A				:					List[Any]    						=			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
A				:					Tuple    						=			[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

A				:					List[str]    						=			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A				:					Any    						=			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']






