import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE										=						logging.get_logger('transformers.models.speecht5')






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Dict   ,	snake_case__			:List[Any])						->   int:
       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  snake_case  (       snake_case__			:str   ,	snake_case__			:Tuple   ,	snake_case__			:Tuple   ,	snake_case__			:Tuple=None   ,	snake_case__			:Any=None   ,	)						->   Optional[int]:
       if config_path is not None:
              _A        =  SpeechTaHifiGanConfig.from_pretrained(snake_case__)
       else:
              _A        =  SpeechTaHifiGanConfig()

       _A        =  SpeechTaHifiGan(snake_case__)

       _A        =  torch.load(snake_case__)
       load_weights(orig_checkpoint["""model"""]["""generator"""]   ,	snake_case__   ,	snake_case__)

       _A        =  np.load(snake_case__)
       _A        =  stats[0].reshape(-1)
       _A        =  stats[1].reshape(-1)
       _A        =  torch.from_numpy(snake_case__).float()
       _A        =  torch.from_numpy(snake_case__).float()

       model.save_pretrained(snake_case__)

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


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						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.'
    )

    _SCREAMING_SNAKE_CASE										=						parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




