



'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     :						str	=  logging.get_logger('''transformers.models.speecht5''')






def 				a  (     UpperCamelCase_    :    int      ,					UpperCamelCase_    :    List[Any]      ,					UpperCamelCase_    :    Tuple			)       ->	Optional[Any]:
		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 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    str      ,					UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    List[Any]=None      ,					UpperCamelCase_    :    Union[str, Any]=None      ,					)       ->	Optional[int]:


		if config_path is not None:
				snake_case__	     =SpeechTaHifiGanConfig.from_pretrained(UpperCamelCase_			)
		else:
				snake_case__	     =SpeechTaHifiGanConfig()

		snake_case__	     =SpeechTaHifiGan(UpperCamelCase_			)

		snake_case__	     =torch.load(UpperCamelCase_			)
		load_weights(orig_checkpoint['model']['generator']      ,					UpperCamelCase_      ,					UpperCamelCase_			)

		snake_case__	     =np.load(UpperCamelCase_			)
		snake_case__	     =stats[0].reshape(-1			)
		snake_case__	     =stats[1].reshape(-1			)
		snake_case__	     =torch.from_numpy(UpperCamelCase_			).float()
		snake_case__	     =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__":
			SCREAMING_SNAKE_CASE__     :						Optional[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__     :						Dict	=  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'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A__							:  int    ={
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  str    =[
          '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Wav2Vec2ForAudioFrameClassification''',
          '''Wav2Vec2ForCTC''',
          '''Wav2Vec2ForMaskedLM''',
          '''Wav2Vec2ForPreTraining''',
          '''Wav2Vec2ForSequenceClassification''',
          '''Wav2Vec2ForXVector''',
          '''Wav2Vec2Model''',
          '''Wav2Vec2PreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =[
          '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWav2Vec2ForCTC''',
          '''TFWav2Vec2Model''',
          '''TFWav2Vec2PreTrainedModel''',
          '''TFWav2Vec2ForSequenceClassification''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''FlaxWav2Vec2ForCTC''',
          '''FlaxWav2Vec2ForPreTraining''',
          '''FlaxWav2Vec2Model''',
          '''FlaxWav2Vec2PreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
      from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
      from .processing_wavaveca import WavaVecaProcessor
      from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca import (
                WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaForAudioFrameClassification,
                WavaVecaForCTC,
                WavaVecaForMaskedLM,
                WavaVecaForPreTraining,
                WavaVecaForSequenceClassification,
                WavaVecaForXVector,
                WavaVecaModel,
                WavaVecaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWavaVecaForCTC,
                TFWavaVecaForSequenceClassification,
                TFWavaVecaModel,
                TFWavaVecaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                FlaxWavaVecaForCTC,
                FlaxWavaVecaForPreTraining,
                FlaxWavaVecaModel,
                FlaxWavaVecaPreTrainedModel,
            )


else:
      import sys

      A__							:  Any    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






