


import os
import sys
import unittest


a			:			Optional[Any]												=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a			:			str												=					os.path.join(git_repo_path, '''src''', '''transformers''')

a			:			Optional[Any]												=					'''\n{0} = None\n'''

a			:			Optional[int]												=					'''\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'''


a			:			Union[str, Any]												=					'''\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'''
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''    )
								self.assertIsNone(snake_case_    )

								__lowercase			       = find_backend('''    if not is_tokenizers_available():'''    )
								self.assertEqual(snake_case_			,							'''tokenizers'''    )

								__lowercase			       = find_backend('''    if not is_tensorflow_text_available():'''    )
								self.assertEqual(snake_case_			,							'''tensorflow_text'''    )

								__lowercase			       = find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''    )
								self.assertEqual(snake_case_			,							'''sentencepiece_and_tokenizers'''    )

								__lowercase			       = find_backend(
								    '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''    )
								self.assertEqual(snake_case_			,							'''sentencepiece_and_tensorflow_text'''    )

								__lowercase			       = find_backend(
								    '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''    )
								self.assertEqual(snake_case_			,							'''sentencepiece_and_tokenizers_and_vision'''    )






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = read_init()
								# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
								self.assertIn('''torch'''			,							snake_case_    )
								self.assertIn('''tensorflow_text'''			,							snake_case_    )
								self.assertIn('''sentencepiece_and_tokenizers'''			,							snake_case_    )

								# Likewise, we can't assert on the exact content of a key
								self.assertIn('''BertModel'''			,							objects['''torch''']    )
								self.assertIn('''TFBertModel'''			,							objects['''tf''']    )
								self.assertIn('''FlaxBertModel'''			,							objects['''flax''']    )
								self.assertIn('''BertModel'''			,							objects['''torch''']    )
								self.assertIn('''TFBertTokenizer'''			,							objects['''tensorflow_text''']    )
								self.assertIn('''convert_slow_tokenizer'''			,							objects['''sentencepiece_and_tokenizers''']    )






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = create_dummy_object('''CONSTANT'''			,							'''\'torch\''''    )
								self.assertEqual(snake_case_			,							'''\nCONSTANT = None\n'''    )

								__lowercase			       = create_dummy_object('''function'''			,							'''\'torch\''''    )
								self.assertEqual(
								    snake_case_			,							'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''    )

								__lowercase			       = '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
								__lowercase			       = create_dummy_object('''FakeClass'''			,							'''\'torch\''''    )
								self.assertEqual(snake_case_			,							snake_case_    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
								__lowercase			       = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}    )
								self.assertEqual(dummy_files['''torch''']			,							snake_case_    )








'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase_    	=				logging.get_logger("transformers.models.speecht5")




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      hf_model.apply_weight_norm()

      SCREAMING_SNAKE_CASE   :      Any    					=						checkpoint['input_conv.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['input_conv.weight_v']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['input_conv.bias']

      for i in range(len(config.upsample_rates    )    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[f"upsamples.{i}.1.weight_g"]
            SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"upsamples.{i}.1.weight_v"]
            SCREAMING_SNAKE_CASE   :      Union[str, 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   :      int    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      str    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]

                  SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						checkpoint['output_conv.1.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						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   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str=None      ,__UpperCamelCase:					Tuple=None      ,):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						SpeechTaHifiGanConfig()

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaHifiGan(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.load(__UpperCamelCase    )
      load_weights(orig_checkpoint['model']['generator']      ,__UpperCamelCase      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      int    					=						np.load(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						stats[0].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						stats[1].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.from_numpy(__UpperCamelCase    ).float()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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__":
      UpperCamelCase_    	=				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."
      )

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


