





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCAmelCase__						:			List[Any]									=logging.get_logger('''transformers.models.speecht5''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Union[str, Any]:
				hf_model.apply_weight_norm()

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

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

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

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

				hf_model.remove_weight_norm()





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      )						->  Tuple:
				if config_path is not None:
								lowerCamelCase	 =SpeechTaHifiGanConfig.from_pretrained(_UpperCAmelCase				)
				else:
								lowerCamelCase	 =SpeechTaHifiGanConfig()

				lowerCamelCase	 =SpeechTaHifiGan(_UpperCAmelCase				)

				lowerCamelCase	 =torch.load(_UpperCAmelCase				)
				load_weights(orig_checkpoint["""model"""]["""generator"""]			,      _UpperCAmelCase			,      _UpperCAmelCase				)

				lowerCamelCase	 =np.load(_UpperCAmelCase				)
				lowerCamelCase	 =stats[0].reshape(-1				)
				lowerCamelCase	 =stats[1].reshape(-1				)
				lowerCamelCase	 =torch.from_numpy(_UpperCAmelCase				).float()
				lowerCamelCase	 =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__":
							UpperCAmelCase__						:			int									=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__						:			List[Any]									=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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





