





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase							:			Tuple        =							logging.get_logger("""transformers.models.speecht5""")


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Tuple:
    hf_model.apply_weight_norm()

    lowerCamelCase__       : Optional[Any]   					=						checkpoint['input_conv.weight_g']
    lowerCamelCase__       : Tuple   					=						checkpoint['input_conv.weight_v']
    lowerCamelCase__       : Any   					=						checkpoint['input_conv.bias']

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

            lowerCamelCase__       : int   					=						checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
            lowerCamelCase__       : Any   					=						checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
            lowerCamelCase__       : List[str]   					=						checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

    lowerCamelCase__       : Tuple   					=						checkpoint['output_conv.1.weight_g']
    lowerCamelCase__       : Union[str, Any]   					=						checkpoint['output_conv.1.weight_v']
    lowerCamelCase__       : str   					=						checkpoint['output_conv.1.bias']

    hf_model.remove_weight_norm()


@torch.no_grad()
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      )     ->	Optional[Any]:
    if config_path is not None:
        lowerCamelCase__       : Optional[Any]   					=						SpeechTaHifiGanConfig.from_pretrained(_UpperCAmelCase					)
    else:
        lowerCamelCase__       : Optional[Any]   					=						SpeechTaHifiGanConfig()

    lowerCamelCase__       : int   					=						SpeechTaHifiGan(_UpperCAmelCase					)

    lowerCamelCase__       : str   					=						torch.load(_UpperCAmelCase					)
    load_weights(orig_checkpoint['model']['generator']		,      _UpperCAmelCase		,      _UpperCAmelCase					)

    lowerCamelCase__       : Union[str, Any]   					=						np.load(_UpperCAmelCase					)
    lowerCamelCase__       : Tuple   					=						stats[0].reshape(-1					)
    lowerCamelCase__       : List[str]   					=						stats[1].reshape(-1					)
    lowerCamelCase__       : List[str]   					=						torch.from_numpy(_UpperCAmelCase					).float()
    lowerCamelCase__       : Optional[int]   					=						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							:			Dict        =							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							:			List[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,
    )












from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase				(   unittest.TestCase     ):
 @slow
 def 	A_      ( self    : Union[str, Any]      )     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

     lowerCamelCase__       : Any   					=						tf.convert_to_tensor(
         [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]     ,	dtype=tf.intaa     ,	)  # J'aime le camembert !"

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )['last_hidden_state']
     lowerCamelCase__       : Tuple   					=						tf.TensorShape((1, 10, 768)      )
     self.assertEqual(output.shape     ,	UpperCAmelCase      )
     # compare the actual values for a slice.
     lowerCamelCase__       : Optional[Any]   					=						tf.convert_to_tensor(
         [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]     ,	dtype=tf.floataa     ,	)
     # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
     # camembert.eval()
     # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

     self.assertTrue(np.allclose(output[:, :3, :3].numpy()     ,	expected_slice.numpy()     ,	atol=1e-4      )      )





