
"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase_       :						Optional[int]     					=	logging.get_logger('''transformers.models.speecht5''')





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							Any,						lowerCamelCase__							:							List[Any]				)  ->    Union[str, Any]:
  hf_model.apply_weight_norm()

  _SCREAMING_SNAKE_CASE   :				str							    = checkpoint["input_conv.weight_g"]
  _SCREAMING_SNAKE_CASE   :				Any							    = checkpoint["input_conv.weight_v"]
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = checkpoint["input_conv.bias"]

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

      _SCREAMING_SNAKE_CASE   :				Any							    = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
      _SCREAMING_SNAKE_CASE   :				Dict							    = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
      _SCREAMING_SNAKE_CASE   :				int							    = checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

  _SCREAMING_SNAKE_CASE   :				str							    = checkpoint["output_conv.1.weight_g"]
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = checkpoint["output_conv.1.weight_v"]
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = checkpoint["output_conv.1.bias"]

  hf_model.remove_weight_norm()





@torch.no_grad()
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							Optional[Any]=None,						)  ->    List[Any]:
  if config_path is not None:
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase__				)
  else:
    _SCREAMING_SNAKE_CASE   :				int							    = SpeechTaHifiGanConfig()

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = SpeechTaHifiGan(lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = torch.load(lowerCamelCase__				)
  load_weights(orig_checkpoint["model"]["generator"],						lowerCamelCase__,						lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = np.load(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = stats[0].reshape(-1				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = stats[1].reshape(-1				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = torch.from_numpy(lowerCamelCase__				).float()
  _SCREAMING_SNAKE_CASE   :				str							    = torch.from_numpy(lowerCamelCase__				).float()

  model.save_pretrained(lowerCamelCase__				)

  if repo_id:
    print("Pushing to the hub..."				)
    model.push_to_hub(lowerCamelCase__				)


if __name__ == "__main__":
      lowercase_       :						Union[str, 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.'''
      )

      lowercase_       :						List[str]     					=	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"""
# 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


lowercase_       :						Tuple     					=	[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							int=None,						lowerCamelCase__							:							Any=None,						lowerCamelCase__							:							Any=None				)  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = True
  while ask_again:
    _SCREAMING_SNAKE_CASE   :				List[str]							    = input(lowerCamelCase__				)
    try:
      if default is not None and len(lowerCamelCase__				) == 0:
        return default
      return convert_value(lowerCamelCase__				) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Dict=[],						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							str=0				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				int							    = BulletMenu(lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = menu.run(default_choice=lowerCamelCase__				)
  return convert_value(lowerCamelCase__				) if convert_value is not None else result





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = int(lowerCamelCase__				)
  return DynamoBackend(DYNAMO_BACKENDS[value]				).value





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				int							    = int(lowerCamelCase__				)
  return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(lowerCamelCase__				)
  return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Optional[Any]:
  return {"yes": True, "no": False}[value.lower()]





class 			UpperCamelCase      (							argparse.RawDescriptionHelpFormatter  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = super()._format_usage(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = usage.replace("<command> [<args>] "  ,     ""				)
       return usage



