




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__				:      Any	   = logging.get_logger("transformers.models.speecht5")

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     hf_model.apply_weight_norm()

     a     =				checkpoint["input_conv.weight_g"]
     a     =				checkpoint["input_conv.weight_v"]
     a     =				checkpoint["input_conv.bias"]

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

               a     =				checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
               a     =				checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
               a     =				checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

     a     =				checkpoint["output_conv.1.weight_g"]
     a     =				checkpoint["output_conv.1.weight_v"]
     a     =				checkpoint["output_conv.1.bias"]

     hf_model.remove_weight_norm()

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=None		, __UpperCamelCase=None		, )      ->					Union[str, Any]:
     if config_path is not None:
          a     =				SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase)
     else:
          a     =				SpeechTaHifiGanConfig()

     a     =				SpeechTaHifiGan(__UpperCamelCase)

     a     =				torch.load(__UpperCamelCase)
     load_weights(orig_checkpoint["model"]["generator"]		, __UpperCamelCase		, __UpperCamelCase)

     a     =				np.load(__UpperCamelCase)
     a     =				stats[0].reshape(-1)
     a     =				stats[1].reshape(-1)
     a     =				torch.from_numpy(__UpperCamelCase).float()
     a     =				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__":
   lowercase__				:      int	   = 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__				:      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,
   )







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowercase__				:      str	   = TypeVar("T")




class       a__	( Generic[T]				):







     def __init__(     self  ,		A = True			)  ->   None:



          '''simple docstring'''



          a     =				{}  # dictionary of lists
          a     =				directed







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   GraphAdjacencyList[T]:



          '''simple docstring'''



          if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    self.adj_list[destination_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(A			)
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                    a     =				[destination_vertex]
                    a     =				[source_vertex]
          else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[]
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                    a     =				[destination_vertex]
                    a     =				[]

          return self







     def __repr__(     self			)  ->   str:



          '''simple docstring'''



          return pformat(self.adj_list			)

