



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 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Tuple     ):



 """simple docstring"""





 hf_model.apply_weight_norm()

 __SCREAMING_SNAKE_CASE       :    List[str]       =	checkpoint["input_conv.weight_g"]
 __SCREAMING_SNAKE_CASE       :    List[str]       =	checkpoint["input_conv.weight_v"]
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates     )     ):
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	checkpoint[F"""upsamples.{i}.1.weight_g"""]
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	checkpoint[F"""upsamples.{i}.1.weight_v"""]
  __SCREAMING_SNAKE_CASE       :    Any       =	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       :    Any       =	checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   __SCREAMING_SNAKE_CASE       :    Tuple       =	checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   __SCREAMING_SNAKE_CASE       :    int       =	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       :    Union[str, Any]       =	checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

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

 hf_model.remove_weight_norm()






@torch.no_grad()
def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     int=None		,	__lowerCamelCase   :     str=None		,	):



 """simple docstring"""





 if config_path is not None:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase     )
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	SpeechTaHifiGanConfig()

 __SCREAMING_SNAKE_CASE       :    str       =	SpeechTaHifiGan(_lowerCAmelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	torch.load(_lowerCAmelCase     )
 load_weights(orig_checkpoint["model"]["generator"]		,	_lowerCAmelCase		,	_lowerCAmelCase     )

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	np.load(_lowerCAmelCase     )
 __SCREAMING_SNAKE_CASE       :    int       =	stats[0].reshape(-1     )
 __SCREAMING_SNAKE_CASE       :    Any       =	stats[1].reshape(-1     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	torch.from_numpy(_lowerCAmelCase     ).float()
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	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__":
    _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,
    )











# Copyright 2023 The HuggingFace Inc. 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""philschmid/bart-large-cnn-samsum"""
     lowerCAmelCase					=			(
         """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
         """and returns a summary of the text."""
     )
     lowerCAmelCase					=			"""summarizer"""
     lowerCAmelCase					=			AutoTokenizer
     lowerCAmelCase					=			AutoModelForSeqaSeqLM

     lowerCAmelCase					=			["""text"""]
     lowerCAmelCase					=			["""text"""]





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Tuple:
      return self.pre_processor(UpperCamelCase     ,  return_tensors="pt"     ,  truncation=UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Any]				)->Optional[Any]:
      return self.model.generate(**UpperCamelCase				)[0]






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict				)->Optional[int]:
      return self.pre_processor.decode(UpperCamelCase     ,  skip_special_tokens=UpperCamelCase     ,  clean_up_tokenization_spaces=UpperCamelCase				)






