





import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
    import jax



@require_flax
class  A__       (	_SCREAMING_SNAKE_CASE	,       unittest.TestCase      ):
      _UpperCAmelCase     :int						   =       FlaxAutoencoderKL





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int		=       4
       UpperCamelCase     : Tuple		=       3
       UpperCamelCase     : Union[str, Any]		=       (32, 32)

       UpperCamelCase     : List[Any]		=       jax.random.PRNGKey(0       )
       UpperCamelCase     : Union[str, Any]		=       jax.random.uniform(A_	,							((batch_size, num_channels) + sizes)       )

       return {"sample": image, "prng_key": prng_key}





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Any		=       {
           """block_out_channels""": [32, 64],
           """in_channels""": 3,
           """out_channels""": 3,
           """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
           """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
           """latent_channels""": 4,
       }
       UpperCamelCase     : Dict		=       self.dummy_input
       return init_dict, inputs_dict







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

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

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

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

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

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : 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	:					Union[str, 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	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
