




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							hf_model.apply_weight_norm()

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

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

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

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

							hf_model.remove_weight_norm()

@torch.no_grad()
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=None  ,    ):
							if config_path is not None:
														SCREAMING_SNAKE_CASE_   	=    SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase     )
							else:
														SCREAMING_SNAKE_CASE_   	=    SpeechTaHifiGanConfig()

							SCREAMING_SNAKE_CASE_   	=    SpeechTaHifiGan(_lowerCAmelCase     )

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

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

							model.save_pretrained(_lowerCAmelCase     )

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


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=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 						=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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					"swin2sr"

				SCREAMING_SNAKE_CASE__					:							str	      =					{
				    "hidden_size": "embed_dim",
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}


				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Dict=64		,					_lowerCamelCase:   str=1		,					_lowerCamelCase:   Optional[int]=3		,					_lowerCamelCase:   List[Any]=1_80		,					_lowerCamelCase:   Optional[int]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Union[str, Any]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Tuple=8		,					_lowerCamelCase:   List[Any]=2.0		,					_lowerCamelCase:   Dict=True		,					_lowerCamelCase:   Dict=0.0		,					_lowerCamelCase:   int=0.0		,					_lowerCamelCase:   Optional[int]=0.1		,					_lowerCamelCase:   Any="gelu"		,					_lowerCamelCase:   int=False		,					_lowerCamelCase:   Tuple=0.02		,					_lowerCamelCase:   List[str]=1E-5		,					_lowerCamelCase:   List[Any]=2		,					_lowerCamelCase:   str=1.0		,					_lowerCamelCase:   List[Any]="1conv"		,					_lowerCamelCase:   str="pixelshuffle"		,					**_lowerCamelCase:   List[str]		,					):
											super().__init__(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    patch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    embed_dim
											SCREAMING_SNAKE_CASE_   	=    depths
											SCREAMING_SNAKE_CASE_   	=    len(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    num_heads
											SCREAMING_SNAKE_CASE_   	=    window_size
											SCREAMING_SNAKE_CASE_   	=    mlp_ratio
											SCREAMING_SNAKE_CASE_   	=    qkv_bias
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    drop_path_rate
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    use_absolute_embeddings
											SCREAMING_SNAKE_CASE_   	=    layer_norm_eps
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    upscale
											SCREAMING_SNAKE_CASE_   	=    img_range
											SCREAMING_SNAKE_CASE_   	=    resi_connection
											SCREAMING_SNAKE_CASE_   	=    upsampler


