




'''simple docstring'''





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      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   List[str] ,						_UpperCamelCase:   List[Any]   )						->			str:

     """simple docstring"""




     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:   Optional[Any] ,						_UpperCamelCase:   str ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   List[Any]=None ,						_UpperCamelCase:   int=None ,						)						->			List[Any]:

     """simple docstring"""




     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__":
   UpperCamelCase_						:      str       =						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_						:      Union[str, 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,
   )








'''simple docstring'''





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Any       =						["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""BridgeTowerImageProcessor"""
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      BatchEncoding:
          _snake_case							=				self.tokenizer(
              text=_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE ,return_attention_mask=_SCREAMING_SNAKE_CASE ,return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,return_offsets_mapping=_SCREAMING_SNAKE_CASE ,return_length=_SCREAMING_SNAKE_CASE ,verbose=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
          # add pixel_values + pixel_mask
          _snake_case							=				self.image_processor(
              _SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
          encoding.update(_SCREAMING_SNAKE_CASE   )

          return encoding






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer.model_input_names
          _snake_case							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


