
import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
							import jax.numpy as jnp

if is_tf_available():
							import tensorflow as tf

if is_torch_available():
							import torch

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Dict	=						{
							    """task_specific_params""": {
							        """summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
							        """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
							        """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
							    }
							}
							UpperCamelCase_:							List[Any]	=						{
							    """task_specific_params.summarization.length_penalty""": 1.0,
							    """task_specific_params.summarization.max_length""": 128,
							    """task_specific_params.summarization.min_length""": 12,
							    """task_specific_params.summarization.num_beams""": 4,
							    """task_specific_params.summarization_cnn.length_penalty""": 2.0,
							    """task_specific_params.summarization_cnn.max_length""": 142,
							    """task_specific_params.summarization_cnn.min_length""": 56,
							    """task_specific_params.summarization_cnn.num_beams""": 4,
							    """task_specific_params.summarization_xsum.length_penalty""": 1.0,
							    """task_specific_params.summarization_xsum.max_length""": 62,
							    """task_specific_params.summarization_xsum.min_length""": 11,
							    """task_specific_params.summarization_xsum.num_beams""": 6,
							}

							self.assertEqual(flatten_dict(snake_case_      )						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							int	=						np.random.randn(3						,	4      )
							self.assertTrue(np.allclose(transpose(snake_case_      )						,	x.transpose()      )      )

							UpperCamelCase_:							Dict	=						np.random.randn(3						,	4						,	5      )
							self.assertTrue(np.allclose(transpose(snake_case_						,	axes=(1, 2, 0)      )						,	x.transpose((1, 2, 0)      )      )      )

			@require_torch
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Optional[int]	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							str	=						torch.tensor(snake_case_      )
							self.assertTrue(np.allclose(transpose(snake_case_      )						,	transpose(snake_case_      ).numpy()      )      )

							UpperCamelCase_:							int	=						np.random.randn(3						,	4						,	5      )
							UpperCamelCase_:							str	=						torch.tensor(snake_case_      )
							self.assertTrue(np.allclose(transpose(snake_case_						,	axes=(1, 2, 0)      )						,	transpose(snake_case_						,	axes=(1, 2, 0)      ).numpy()      )      )

			@require_tf
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							Optional[Any]	=						tf.constant(snake_case_      )
							self.assertTrue(np.allclose(transpose(snake_case_      )						,	transpose(snake_case_      ).numpy()      )      )

							UpperCamelCase_:							int	=						np.random.randn(3						,	4						,	5      )
							UpperCamelCase_:							List[str]	=						tf.constant(snake_case_      )
							self.assertTrue(np.allclose(transpose(snake_case_						,	axes=(1, 2, 0)      )						,	transpose(snake_case_						,	axes=(1, 2, 0)      ).numpy()      )      )

			@require_flax
			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Tuple	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							str	=						jnp.array(snake_case_      )
							self.assertTrue(np.allclose(transpose(snake_case_      )						,	np.asarray(transpose(snake_case_      )      )      )      )

							UpperCamelCase_:							Tuple	=						np.random.randn(3						,	4						,	5      )
							UpperCamelCase_:							Optional[int]	=						jnp.array(snake_case_      )
							self.assertTrue(np.allclose(transpose(snake_case_						,	axes=(1, 2, 0)      )						,	np.asarray(transpose(snake_case_						,	axes=(1, 2, 0)      )      )      )      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							str	=						np.random.randn(3						,	4      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(4, 3)      )						,	np.reshape(snake_case_						,	(4, 3)      )      )      )

							UpperCamelCase_:							Optional[int]	=						np.random.randn(3						,	4						,	5      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(12, 5)      )						,	np.reshape(snake_case_						,	(12, 5)      )      )      )

			@require_torch
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							List[Any]	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							Optional[int]	=						torch.tensor(snake_case_      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(4, 3)      )						,	reshape(snake_case_						,	(4, 3)      ).numpy()      )      )

							UpperCamelCase_:							Tuple	=						np.random.randn(3						,	4						,	5      )
							UpperCamelCase_:							List[Any]	=						torch.tensor(snake_case_      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(12, 5)      )						,	reshape(snake_case_						,	(12, 5)      ).numpy()      )      )

			@require_tf
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Tuple	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							Optional[int]	=						tf.constant(snake_case_      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(4, 3)      )						,	reshape(snake_case_						,	(4, 3)      ).numpy()      )      )

							UpperCamelCase_:							str	=						np.random.randn(3						,	4						,	5      )
							UpperCamelCase_:							str	=						tf.constant(snake_case_      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(12, 5)      )						,	reshape(snake_case_						,	(12, 5)      ).numpy()      )      )

			@require_flax
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							Optional[int]	=						jnp.array(snake_case_      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(4, 3)      )						,	np.asarray(reshape(snake_case_						,	(4, 3)      )      )      )      )

							UpperCamelCase_:							List[str]	=						np.random.randn(3						,	4						,	5      )
							UpperCamelCase_:							int	=						jnp.array(snake_case_      )
							self.assertTrue(np.allclose(reshape(snake_case_						,	(12, 5)      )						,	np.asarray(reshape(snake_case_						,	(12, 5)      )      )      )      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Any	=						np.random.randn(1						,	3						,	4      )
							self.assertTrue(np.allclose(squeeze(snake_case_      )						,	np.squeeze(snake_case_      )      )      )

							UpperCamelCase_:							str	=						np.random.randn(1						,	4						,	1						,	5      )
							self.assertTrue(np.allclose(squeeze(snake_case_						,	axis=2      )						,	np.squeeze(snake_case_						,	axis=2      )      )      )

			@require_torch
			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[Any]	=						np.random.randn(1						,	3						,	4      )
							UpperCamelCase_:							Tuple	=						torch.tensor(snake_case_      )
							self.assertTrue(np.allclose(squeeze(snake_case_      )						,	squeeze(snake_case_      ).numpy()      )      )

							UpperCamelCase_:							int	=						np.random.randn(1						,	4						,	1						,	5      )
							UpperCamelCase_:							Tuple	=						torch.tensor(snake_case_      )
							self.assertTrue(np.allclose(squeeze(snake_case_						,	axis=2      )						,	squeeze(snake_case_						,	axis=2      ).numpy()      )      )

			@require_tf
			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							str	=						np.random.randn(1						,	3						,	4      )
							UpperCamelCase_:							Optional[int]	=						tf.constant(snake_case_      )
							self.assertTrue(np.allclose(squeeze(snake_case_      )						,	squeeze(snake_case_      ).numpy()      )      )

							UpperCamelCase_:							Optional[int]	=						np.random.randn(1						,	4						,	1						,	5      )
							UpperCamelCase_:							List[Any]	=						tf.constant(snake_case_      )
							self.assertTrue(np.allclose(squeeze(snake_case_						,	axis=2      )						,	squeeze(snake_case_						,	axis=2      ).numpy()      )      )

			@require_flax
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[str]	=						np.random.randn(1						,	3						,	4      )
							UpperCamelCase_:							List[Any]	=						jnp.array(snake_case_      )
							self.assertTrue(np.allclose(squeeze(snake_case_      )						,	np.asarray(squeeze(snake_case_      )      )      )      )

							UpperCamelCase_:							Dict	=						np.random.randn(1						,	4						,	1						,	5      )
							UpperCamelCase_:							Optional[int]	=						jnp.array(snake_case_      )
							self.assertTrue(np.allclose(squeeze(snake_case_						,	axis=2      )						,	np.asarray(squeeze(snake_case_						,	axis=2      )      )      )      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							List[str]	=						np.random.randn(3						,	4      )
							self.assertTrue(np.allclose(expand_dims(snake_case_						,	axis=1      )						,	np.expand_dims(snake_case_						,	axis=1      )      )      )

			@require_torch
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Any	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							Optional[int]	=						torch.tensor(snake_case_      )
							self.assertTrue(np.allclose(expand_dims(snake_case_						,	axis=1      )						,	expand_dims(snake_case_						,	axis=1      ).numpy()      )      )

			@require_tf
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							List[Any]	=						tf.constant(snake_case_      )
							self.assertTrue(np.allclose(expand_dims(snake_case_						,	axis=1      )						,	expand_dims(snake_case_						,	axis=1      ).numpy()      )      )




			@require_flax
			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Dict	=						np.random.randn(3						,	4      )
							UpperCamelCase_:							Dict	=						jnp.array(snake_case_      )
							self.assertTrue(np.allclose(expand_dims(snake_case_						,	axis=1      )						,	np.asarray(expand_dims(snake_case_						,	axis=1      )      )      )      )








import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_						: Optional[int]         =  logging.get_logger("""transformers.models.speecht5""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				hf_model.apply_weight_norm()

				UpperCamelCase_:							Union[str, Any]	=						checkpoint["""input_conv.weight_g"""]
				UpperCamelCase_:							Optional[int]	=						checkpoint["""input_conv.weight_v"""]
				UpperCamelCase_:							List[Any]	=						checkpoint["""input_conv.bias"""]

				for i in range(len(config.upsample_rates						)						):
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.weight_g''']
								UpperCamelCase_:							Dict	=						checkpoint[F'''upsamples.{i}.1.weight_v''']
								UpperCamelCase_:							List[str]	=						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_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

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

				UpperCamelCase_:							int	=						checkpoint["""output_conv.1.weight_g"""]
				UpperCamelCase_:							Tuple	=						checkpoint["""output_conv.1.weight_v"""]
				UpperCamelCase_:							List[str]	=						checkpoint["""output_conv.1.bias"""]

				hf_model.remove_weight_norm()







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Optional[int]:
				if config_path is not None:
								UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							str	=						SpeechTaHifiGanConfig()

				UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGan(lowerCamelCase						)

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

				UpperCamelCase_:							Union[str, Any]	=						np.load(lowerCamelCase						)
				UpperCamelCase_:							int	=						stats[0].reshape(-1						)
				UpperCamelCase_:							Union[str, Any]	=						stats[1].reshape(-1						)
				UpperCamelCase_:							Dict	=						torch.from_numpy(lowerCamelCase						).float()
				UpperCamelCase_:							Optional[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_						: Optional[int]         =  parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)






