


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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			hf_model.apply_weight_norm()

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

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

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

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

			hf_model.remove_weight_norm()


@torch.no_grad()
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase=None       ,			_UpperCamelCase=None       ,			):


			'''simple docstring'''







			if config_path is not None:
						__lowercase 					=   SpeechTaHifiGanConfig.from_pretrained(_UpperCamelCase  )
			else:
						__lowercase 					=   SpeechTaHifiGanConfig()

			__lowercase 					=   SpeechTaHifiGan(_UpperCamelCase  )

			__lowercase 					=   torch.load(_UpperCamelCase  )
			load_weights(orig_checkpoint['''model''']['''generator''']       ,			_UpperCamelCase       ,			_UpperCamelCase  )

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

			model.save_pretrained(_UpperCamelCase  )

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


if __name__ == "__main__":
	a : Dict		=				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,
	)






from typing import List

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


a : Any		=				logging.get_logger(__name__)

a : int		=				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"efficientformer"






					def __init__(				self ,       snake_case_ = [3, 2, 6, 4] ,       snake_case_ = [4_8, 9_6, 2_2_4, 4_4_8] ,       snake_case_ = [True, True, True, True] ,       snake_case_ = 4_4_8 ,       snake_case_ = 3_2 ,       snake_case_ = 4 ,       snake_case_ = 7 ,       snake_case_ = 5 ,       snake_case_ = 8 ,       snake_case_ = 4 ,       snake_case_ = 0.0 ,       snake_case_ = 1_6 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 2 ,       snake_case_ = 1 ,       snake_case_ = 0.0 ,       snake_case_ = 1 ,       snake_case_ = True ,       snake_case_ = True ,       snake_case_ = 1e-5 ,       snake_case_ = "gelu" ,       snake_case_ = 0.0_2 ,       snake_case_ = 1e-1_2 ,       snake_case_ = 2_2_4 ,       snake_case_ = 1e-0_5 ,       **snake_case_ ,       )   ->			None:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								__lowercase 					=   hidden_act
								__lowercase 					=   hidden_dropout_prob
								__lowercase 					=   hidden_sizes
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   initializer_range
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   patch_size
								__lowercase 					=   num_channels
								__lowercase 					=   depths
								__lowercase 					=   mlp_expansion_ratio
								__lowercase 					=   downsamples
								__lowercase 					=   dim
								__lowercase 					=   key_dim
								__lowercase 					=   attention_ratio
								__lowercase 					=   resolution
								__lowercase 					=   pool_size
								__lowercase 					=   downsample_patch_size
								__lowercase 					=   downsample_stride
								__lowercase 					=   downsample_pad
								__lowercase 					=   drop_path_rate
								__lowercase 					=   num_metaad_blocks
								__lowercase 					=   distillation
								__lowercase 					=   use_layer_scale
								__lowercase 					=   layer_scale_init_value
								__lowercase 					=   image_size
								__lowercase 					=   batch_norm_eps


