


import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch

class     UpperCAmelCase__ (   unittest.TestCase		):



     '''simple docstring'''
     UpperCAmelCase_       =	JukeboxTokenizer
     UpperCAmelCase_       =	{
         '''artist''': '''Zac Brown Band''',
         '''genres''': '''Country''',
         '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
     }




     @require_torch
     def 	lowerCAmelCase_ (			self							:							Dict		):





            """simple docstring"""



            import torch

            _lowercase       :					List[str]     							=      JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''		)
            _lowercase       :					Union[str, Any]     							=      tokenizer(**self.metas		)['''input_ids''']
            # fmt: off
            _lowercase       :					Optional[Any]     							=      [
                torch.tensor([[
                    0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
                    76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                    44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                    47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                    76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                    30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                    27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                    45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                    41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                    19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                    76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                    76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                    64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                    30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                    27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                    34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                    27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                    41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                    76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                    44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                    76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                    32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                    40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                    20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                    45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                    31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                    45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                    76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                    34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                    31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                    40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                    38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                    78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                    76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                    41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                    27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                    46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                    76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                    41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                    46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                    41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                    78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                    40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                    27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                    76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                    76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                    41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                    76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                    27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                    78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                    34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                    44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                    40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                    78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                    46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                    38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                    40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                    27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                    20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                    76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                    76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                    76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                    76, 76]]		),
                torch.tensor([[0, 0, 0, 10_69, 11]]		),
                torch.tensor([[0, 0, 0, 10_69, 11]]		),
            ]
            # fmt: on
            self.assertTrue(torch.allclose(tokens[0]						,   EXPECTED_OUTPUT[0]		)		)
            self.assertTrue(torch.allclose(tokens[1]						,   EXPECTED_OUTPUT[1]		)		)
            self.assertTrue(torch.allclose(tokens[2]						,   EXPECTED_OUTPUT[2]		)		)




     @require_torch
     def 	lowerCAmelCase_ (			self							:							List[str]		):





            """simple docstring"""



            import torch

            _lowercase       :					List[str]     							=      JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''		)
            _lowercase       :					List[Any]     							=      tokenizer(**self.metas		)['''input_ids''']
            # fmt: off
            _lowercase       :					Any     							=      [
                torch.tensor([[
                    0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
                    31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                    31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                    40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                    79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                    77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                    27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                    37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                    32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                    77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                    77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                    77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                    46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                    77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                    77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                    77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                    77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                    64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                    40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                    40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                    38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                    31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                    41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                    77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                    46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                    41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                    31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                    31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                    23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                    44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                    31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                    38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                    40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                    77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                    27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                    31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                    34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                    31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                    1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                    31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                    45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                    31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                    77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                    15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                    11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                    45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                    41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                    44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                    46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                    27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                    77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                    35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                    77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                    31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                    77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                    41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                    77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                    40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                    77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                    77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                    27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                    77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                    77, 77, 77, 77, 77, 77]]		),
                torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]		),
                torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]		),
            ]
            # fmt: on
            self.assertTrue(torch.allclose(tokens[0]						,   EXPECTED_OUTPUT[0]		)		)
            self.assertTrue(torch.allclose(tokens[1]						,   EXPECTED_OUTPUT[1]		)		)
            self.assertTrue(torch.allclose(tokens[2]						,   EXPECTED_OUTPUT[2]		)		)





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase      :						int							=  logging.get_logger("""transformers.models.speecht5""")







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

       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							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None					,							)  ->			str:



       """simple docstring"""

       if config_path is not None:
              A__ 					=       SpeechTaHifiGanConfig.from_pretrained(lowercase_		)
       else:
              A__ 					=       SpeechTaHifiGanConfig()

       A__ 					=       SpeechTaHifiGan(lowercase_		)

       A__ 					=       torch.load(lowercase_		)
       load_weights(orig_checkpoint['''model''']['''generator''']					,							lowercase_					,							lowercase_		)

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

       model.save_pretrained(lowercase_		)

       if repo_id:
              print('''Pushing to the hub...'''		)
              model.push_to_hub(lowercase_		)


if __name__ == "__main__":
  _lowerCamelCase      :						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."""
  )

  _lowerCamelCase      :						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,
  )


