




'''simple docstring'''





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE				:  int 						=					logging.get_logger("transformers.models.speecht5")


def 	UpperCamelCase_(      snake_case				:			Tuple				,							snake_case				:			Optional[int]				,							snake_case				:			str	):

							'''simple docstring'''






							hf_model.apply_weight_norm()

							snake_case_         = checkpoint["input_conv.weight_g"]
							snake_case_         = checkpoint["input_conv.weight_v"]
							snake_case_         = checkpoint["input_conv.bias"]

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

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

							snake_case_         = checkpoint["output_conv.1.weight_g"]
							snake_case_         = checkpoint["output_conv.1.weight_v"]
							snake_case_         = checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()


@torch.no_grad()
def 	UpperCamelCase_(      snake_case				:			List[Any]				,							snake_case				:			List[str]				,							snake_case				:			str				,							snake_case				:			Optional[int]=None				,							snake_case				:			int=None				,							):

							'''simple docstring'''






							if config_path is not None:
														snake_case_         = SpeechTaHifiGanConfig.from_pretrained(lowercase_	)
							else:
														snake_case_         = SpeechTaHifiGanConfig()

							snake_case_         = SpeechTaHifiGan(lowercase_	)

							snake_case_         = torch.load(lowercase_	)
							load_weights(orig_checkpoint["model"]["generator"]				,							lowercase_				,							lowercase_	)

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

							model.save_pretrained(lowercase_	)

							if repo_id:
														print("Pushing to the hub..."	)
														model.push_to_hub(lowercase_	)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE				:  Any 						=					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."
		)

		_SCREAMING_SNAKE_CASE				:  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,
		)










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


_lowerCamelCase      :						str							=  logging.get_logger(__name__)

_lowerCamelCase      :						List[str]							=  {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''openai-gpt'''
    UpperCAmelCase__				    = {
        '''max_position_embeddings''': '''n_positions''',
        '''hidden_size''': '''n_embd''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }


    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Dict=40_478   ,					UpperCAmelCase__ :      str=512   ,					UpperCAmelCase__ :      Union[str, Any]=768   ,					UpperCAmelCase__ :      Optional[Any]=12   ,					UpperCAmelCase__ :      Any=12   ,					UpperCAmelCase__ :      Optional[Any]="gelu"   ,					UpperCAmelCase__ :      Any=0.1   ,					UpperCAmelCase__ :      List[str]=0.1   ,					UpperCAmelCase__ :      Tuple=0.1   ,					UpperCAmelCase__ :      List[str]=1e-5   ,					UpperCAmelCase__ :      int=0.02   ,					UpperCAmelCase__ :      Any="cls_index"   ,					UpperCAmelCase__ :      Optional[Any]=True   ,					UpperCAmelCase__ :      List[Any]=None   ,					UpperCAmelCase__ :      List[Any]=True   ,					UpperCAmelCase__ :      Optional[Any]=0.1   ,					**UpperCAmelCase__ :      Dict   ,					)		->Any:





           '''simple docstring'''

           A__ 					=       vocab_size
           A__ 					=       n_positions
           A__ 					=       n_embd
           A__ 					=       n_layer
           A__ 					=       n_head
           A__ 					=       afn
           A__ 					=       resid_pdrop
           A__ 					=       embd_pdrop
           A__ 					=       attn_pdrop
           A__ 					=       layer_norm_epsilon
           A__ 					=       initializer_range
           A__ 					=       summary_type
           A__ 					=       summary_use_proj
           A__ 					=       summary_activation
           A__ 					=       summary_first_dropout
           A__ 					=       summary_proj_to_labels
           super().__init__(**UpperCAmelCase__)


