



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Tuple:
		hf_model.apply_weight_norm()

		a_     :      int 						=     checkpoint["input_conv.weight_g"]
		a_     :      Tuple 						=     checkpoint["input_conv.weight_v"]
		a_     :      str 						=     checkpoint["input_conv.bias"]

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

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

		a_     :      List[Any] 						=     checkpoint["output_conv.1.weight_g"]
		a_     :      Union[str, Any] 						=     checkpoint["output_conv.1.weight_v"]
		a_     :      int 						=     checkpoint["output_conv.1.bias"]

		hf_model.remove_weight_norm()



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None, )			->      Union[str, Any]:


		if config_path is not None:
				a_     :      Dict 						=     SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase__	)
		else:
				a_     :      Optional[int] 						=     SpeechTaHifiGanConfig()

		a_     :      Optional[int] 						=     SpeechTaHifiGan(lowerCamelCase__	)

		a_     :      int 						=     torch.load(lowerCamelCase__	)
		load_weights(orig_checkpoint["model"]["generator"], lowerCamelCase__, lowerCamelCase__	)

		a_     :      str 						=     np.load(lowerCamelCase__	)
		a_     :      Optional[Any] 						=     stats[0].reshape(-1	)
		a_     :      Union[str, Any] 						=     stats[1].reshape(-1	)
		a_     :      Optional[int] 						=     torch.from_numpy(lowerCamelCase__	).float()
		a_     :      int 						=     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__":
					SCREAMING_SNAKE_CASE_				= 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."""
					)

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




"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

SCREAMING_SNAKE_CASE_				= random.Random()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=1.0, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      Tuple:
		if rng is None:
				a_     :      Any 						=     global_rng

		a_     :      Dict 						=     []
		for batch_idx in range(shape[0]	):
				values.append([]	)
				for _ in range(shape[1]	):
						values[-1].append(rng.random() * scale	)

		return values




class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=7			,     a_=4_0_0			,     a_=2_0_0_0			,     a_=2_0_4_8			,     a_=1_2_8			,     a_=1			,     a_=5_1_2			,     a_=3_0			,     a_=4_4_1_0_0			,     ):
									a_     :      Optional[Any] 						=     parent
									a_     :      Tuple 						=     batch_size
									a_     :      Union[str, Any] 						=     min_seq_length
									a_     :      Dict 						=     max_seq_length
									a_     :      Union[str, Any] 						=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a_     :      int 						=     spectrogram_length
									a_     :      Optional[Any] 						=     feature_size
									a_     :      Optional[int] 						=     num_audio_channels
									a_     :      List[str] 						=     hop_length
									a_     :      Optional[int] 						=     chunk_length
									a_     :      List[str] 						=     sampling_rate






							def snake_case_  (   self      ):
									return {
									    "spectrogram_length": self.spectrogram_length,
									    "feature_size": self.feature_size,
									    "num_audio_channels": self.num_audio_channels,
									    "hop_length": self.hop_length,
									    "chunk_length": self.chunk_length,
									    "sampling_rate": self.sampling_rate,
									}






							def snake_case_  (   self			,     a_=False			,     a_=False      ):
									def _flatten(a_      ):
											return list(itertools.chain(*a_      )      )

									if equal_length:
											a_     :      Tuple 						=     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
									else:
											# make sure that inputs increase in size
											a_     :      Optional[Any] 						=     [
											    floats_list((x, self.feature_size)      )
											    for x in range(self.min_seq_length			,     self.max_seq_length			,     self.seq_length_diff      )
											]
									if numpify:
											a_     :      Tuple 						=     [np.asarray(a_      ) for x in speech_inputs]
									return speech_inputs




@require_torch
@require_torchaudio
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					TvltFeatureExtractor






							def snake_case_  (   self      ):
									a_     :      Dict 						=     TvltFeatureExtractionTester(self      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )
									self.assertTrue(hasattr(a_			,     "spectrogram_length"      )      )
									self.assertTrue(hasattr(a_			,     "feature_size"      )      )
									self.assertTrue(hasattr(a_			,     "num_audio_channels"      )      )
									self.assertTrue(hasattr(a_			,     "hop_length"      )      )
									self.assertTrue(hasattr(a_			,     "chunk_length"      )      )
									self.assertTrue(hasattr(a_			,     "sampling_rate"      )      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      List[Any] 						=     feat_extract_first.save_pretrained(a_      )[0]
											check_json_file_has_correct_format(a_      )
											a_     :      Tuple 						=     self.feature_extraction_class.from_pretrained(a_      )

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      List[str] 						=     feat_extract_second.to_dict()
									a_     :      List[Any] 						=     dict_first.pop("mel_filters"      )
									a_     :      str 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      Dict 						=     os.path.join(a_			,     "feat_extract.json"      )
											feat_extract_first.to_json_file(a_      )
											a_     :      str 						=     self.feature_extraction_class.from_json_file(a_      )

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      str 						=     feat_extract_second.to_dict()
									a_     :      int 						=     dict_first.pop("mel_filters"      )
									a_     :      List[Any] 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									# Initialize feature_extractor
									a_     :      str 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									# create three inputs of length 800, 1000, and 1200
									a_     :      Optional[Any] 						=     [floats_list((1, x)      )[0] for x in range(8_0_0			,     1_4_0_0			,     2_0_0      )]
									a_     :      Union[str, Any] 						=     [np.asarray(a_      ) for speech_input in speech_inputs]

									# Test not batched input
									a_     :      Dict 						=     feature_extractor(np_speech_inputs[0]			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test batched
									a_     :      int 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test audio masking
									a_     :      Optional[int] 						=     feature_extractor(
									    a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0			,     mask_audio=a_      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test 2-D numpy arrays are batched.
									a_     :      List[str] 						=     [floats_list((1, x)      )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
									a_     :      Union[str, Any] 						=     np.asarray(a_      )
									a_     :      Dict 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values
									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Optional[int] 						=     load_dataset("hf-internal-testing/librispeech_asr_dummy"			,     "clean"			,     split="validation"      )
									# automatic decoding with librispeech
									a_     :      List[str] 						=     ds.sort("id"      ).select(range(a_      )      )[:num_samples]["audio"]

									return [x["array"] for x in speech_samples]






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self._load_datasamples(1      )
									a_     :      Any 						=     TvltFeatureExtractor()
									a_     :      Union[str, Any] 						=     feature_extractor(a_			,     return_tensors="pt"      ).audio_values

									self.assertEquals(audio_values.shape			,     (1, 1, 1_9_2, 1_2_8)      )

									a_     :      Dict 						=     torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]      )
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,     a_			,     atol=1e-4      )      )