


"""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     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						List[Any]:
						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     lowercase			(SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				Optional[int]=None ,			SCREAMING_SNAKE_CASE_		:				int=None ,			)   ->						int:
						if config_path is not None:
												SCREAMING_SNAKE_CASE													=					SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
						else:
												SCREAMING_SNAKE_CASE													=					SpeechTaHifiGanConfig()

						SCREAMING_SNAKE_CASE													=					SpeechTaHifiGan(SCREAMING_SNAKE_CASE_  )

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

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






"""simple docstring"""




__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # Text guided image variation with an image mask
        '''prompt''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # image variation with an image mask
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(
    [
        '''example_image''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''example_image''', '''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''audio_length_in_s''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])
__UpperCamelCase						=      frozenset(['''input_tokens'''])

__UpperCamelCase						=      frozenset(['''input_tokens'''])


