





"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCamelCase					      =   logging.get_logger('transformers.models.speecht5')



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				Dict ):


							'''simple docstring'''







							hf_model.apply_weight_norm()

							__lowerCamelCase   :   List[str]       =checkpoint["input_conv.weight_g"]
							__lowerCamelCase   :   Optional[int]       =checkpoint["input_conv.weight_v"]
							__lowerCamelCase   :   Optional[Any]       =checkpoint["input_conv.bias"]

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

																					__lowerCamelCase   :   Dict       =checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
																					__lowerCamelCase   :   Optional[int]       =checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
																					__lowerCamelCase   :   List[Any]       =checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

							__lowerCamelCase   :   List[str]       =checkpoint["output_conv.1.weight_g"]
							__lowerCamelCase   :   Union[str, Any]       =checkpoint["output_conv.1.weight_v"]
							__lowerCamelCase   :   Union[str, Any]       =checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()



@torch.no_grad()
def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				List[str]=None     ,    SCREAMING_SNAKE_CASE							:				str=None     ,    ):


							'''simple docstring'''







							if config_path is not None:
														__lowerCamelCase   :   Dict       =SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =SpeechTaHifiGanConfig()

							__lowerCamelCase   :   List[Any]       =SpeechTaHifiGan(SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   List[Any]       =torch.load(SCREAMING_SNAKE_CASE )
							load_weights(orig_checkpoint['''model''']['''generator''']     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Optional[Any]       =np.load(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Optional[int]       =stats[0].reshape(-1 )
							__lowerCamelCase   :   Dict       =stats[1].reshape(-1 )
							__lowerCamelCase   :   Any       =torch.from_numpy(SCREAMING_SNAKE_CASE ).float()
							__lowerCamelCase   :   Union[str, Any]       =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__":
							_UpperCamelCase					      =   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.'
							)

							_UpperCamelCase					      =   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


a  :      List[Any]            =    logging.get_logger(__name__)

a  :      Union[str, Any]            =    {
    """bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[str]          =    'gpt_bigcode'
    SCREAMING_SNAKE_CASE:				Union[str, Any]          =    ['past_key_values']
    SCREAMING_SNAKE_CASE:				Tuple          =    {
        'hidden_size': 'n_embd',
        'max_position_embeddings': 'n_positions',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=50_257       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=768       ,						lowerCamelCase__=12       ,						lowerCamelCase__=12       ,						lowerCamelCase__=None       ,						lowerCamelCase__="gelu_pytorch_tanh"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=50_256       ,						lowerCamelCase__=50_256       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							List[Any]       =	vocab_size
       lowerCAmelCase_:							List[str]       =	n_positions
       lowerCAmelCase_:							Union[str, Any]       =	n_embd
       lowerCAmelCase_:							Union[str, Any]       =	n_layer
       lowerCAmelCase_:							Union[str, Any]       =	n_head
       lowerCAmelCase_:							List[Any]       =	n_inner
       lowerCAmelCase_:							List[Any]       =	activation_function
       lowerCAmelCase_:							Optional[int]       =	resid_pdrop
       lowerCAmelCase_:							Union[str, Any]       =	embd_pdrop
       lowerCAmelCase_:							Any       =	attn_pdrop
       lowerCAmelCase_:							Union[str, Any]       =	layer_norm_epsilon
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	scale_attn_weights
       lowerCAmelCase_:							int       =	use_cache
       lowerCAmelCase_:							int       =	attention_softmax_in_fpaa
       lowerCAmelCase_:							int       =	scale_attention_softmax_in_fpaa
       lowerCAmelCase_:							Union[str, Any]       =	multi_query

       lowerCAmelCase_:							Union[str, Any]       =	bos_token_id
       lowerCAmelCase_:							List[str]       =	eos_token_id

       super().__init__(bos_token_id=lowerCamelCase__       ,						eos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )