





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase			       =      logging.get_logger('transformers.models.speecht5')




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       hf_model.apply_weight_norm()

       _lowerCamelCase							:     int	=		checkpoint['''input_conv.weight_g''']
       _lowerCamelCase							:     List[str]	=		checkpoint['''input_conv.weight_v''']
       _lowerCamelCase							:     List[Any]	=		checkpoint['''input_conv.bias''']

       for i in range(len(config.upsample_rates      )      ):
              _lowerCamelCase							:     List[str]	=		checkpoint[F"""upsamples.{i}.1.weight_g"""]
              _lowerCamelCase							:     List[str]	=		checkpoint[F"""upsamples.{i}.1.weight_v"""]
              _lowerCamelCase							:     List[str]	=		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							:     Any	=		checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
                     _lowerCamelCase							:     int	=		checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
                     _lowerCamelCase							:     Dict	=		checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

                     _lowerCamelCase							:     Tuple	=		checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     _lowerCamelCase							:     str	=		checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     _lowerCamelCase							:     Optional[Any]	=		checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

       _lowerCamelCase							:     Optional[Any]	=		checkpoint['''output_conv.1.weight_g''']
       _lowerCamelCase							:     Dict	=		checkpoint['''output_conv.1.weight_v''']
       _lowerCamelCase							:     List[Any]	=		checkpoint['''output_conv.1.bias''']

       hf_model.remove_weight_norm()




@torch.no_grad()
def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_=None ,    lowercase_=None ,    ):
       if config_path is not None:
              _lowerCamelCase							:     Tuple	=		SpeechTaHifiGanConfig.from_pretrained(lowercase_      )
       else:
              _lowerCamelCase							:     str	=		SpeechTaHifiGanConfig()

       _lowerCamelCase							:     Dict	=		SpeechTaHifiGan(lowercase_      )

       _lowerCamelCase							:     str	=		torch.load(lowercase_      )
       load_weights(orig_checkpoint['''model''']['''generator'''] ,    lowercase_ ,    lowercase_      )

       _lowerCamelCase							:     Tuple	=		np.load(lowercase_      )
       _lowerCamelCase							:     Optional[Any]	=		stats[0].reshape(-1      )
       _lowerCamelCase							:     Dict	=		stats[1].reshape(-1      )
       _lowerCamelCase							:     List[str]	=		torch.from_numpy(lowercase_      ).float()
       _lowerCamelCase							:     List[str]	=		torch.from_numpy(lowercase_      ).float()

       model.save_pretrained(lowercase_      )

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


if __name__ == "__main__":
       _lowerCamelCase			       =      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.'
       )

       _lowerCamelCase			       =      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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

