





"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__UpperCamelCase     :						Optional[Any]						       =  logging.get_logger('''transformers.models.speecht5''')

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    Dict			):
 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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Any=None  ,     _UpperCAmelCase					:    Union[str, Any]=None  ,     ):
 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     :						Dict						       =  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     :						Optional[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,
       )







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCamelCase__     :		Optional[int]        = [
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   True
  while ask_again:
    lowercase__	: Tuple     		=   input(lowercase_ )
    try:
      if default is not None and len(lowercase_ ) == 0:
        return default
      return convert_value(lowercase_ ) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_=[]			,			lowercase_=None			,			lowercase_=0 )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   BulletMenu(lowercase_			,			lowercase_ )
  lowercase__	: Any     		=   menu.run(default_choice=lowercase_ )
  return convert_value(lowercase_ ) if convert_value is not None else result

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   int(lowercase_ )
  return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   int(lowercase_ )
  return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: str     		=   int(lowercase_ )
  return DynamoBackend(DYNAMO_BACKENDS[value] ).value

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return {"yes": True, "no": False}[value.lower()]



class    _snake_case (						argparse.RawDescriptionHelpFormatter				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   super()._format_usage(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   usage.replace("""<command> [<args>] """       ,	"""""")
   return usage






