






'''simple docstring'''

import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
     from transformers import WhisperFeatureExtractor

if is_torch_available():
     import torch

SCREAMING_SNAKE_CASE__     =       random.Random()







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase=1.0		,						__UpperCamelCase=None		,						__UpperCamelCase=None   )->			List[str]:
      if rng is None:
            UpperCamelCase      = global_rng

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

      return values






@require_torch
@require_torchaudio
class     a_						(      unittest.TestCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=2000      ,		_SCREAMING_SNAKE_CASE=10      ,		_SCREAMING_SNAKE_CASE=160      ,		_SCREAMING_SNAKE_CASE=8      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=4000      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=True      ,		)  ->				str:



        """simple docstring"""






        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = min_seq_length
        UpperCamelCase      = max_seq_length
        UpperCamelCase      = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        UpperCamelCase      = padding_value
        UpperCamelCase      = sampling_rate
        UpperCamelCase      = return_attention_mask
        UpperCamelCase      = do_normalize
        UpperCamelCase      = feature_size
        UpperCamelCase      = chunk_length
        UpperCamelCase      = hop_length

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Dict:



        """simple docstring"""






        def _flatten(_SCREAMING_SNAKE_CASE				):
              return list(itertools.chain(*_SCREAMING_SNAKE_CASE				)				)

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






@require_torch
@require_torchaudio
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				WhisperFeatureExtractor if is_speech_available() else None

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = WhisperFeatureExtractionTester(self				)

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.feature_extraction_class(**self.feat_extract_dict				)

        with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCamelCase      = feat_extract_first.save_pretrained(_SCREAMING_SNAKE_CASE				)[0]
              check_json_file_has_correct_format(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = self.feature_extraction_class.from_pretrained(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = feat_extract_first.to_dict()
        UpperCamelCase      = feat_extract_second.to_dict()
        UpperCamelCase      = feat_extract_first.mel_filters
        UpperCamelCase      = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.feature_extraction_class(**self.feat_extract_dict				)

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

        UpperCamelCase      = feat_extract_first.to_dict()
        UpperCamelCase      = feat_extract_second.to_dict()
        UpperCamelCase      = feat_extract_first.mel_filters
        UpperCamelCase      = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        # create three inputs of length 800, 1000, and 1200
        UpperCamelCase      = [floats_list((1, x)				)[0] for x in range(800      ,		1400      ,		200				)]
        UpperCamelCase      = [np.asarray(_SCREAMING_SNAKE_CASE				) for speech_input in speech_inputs]

        # Test feature size
        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		padding="""max_length"""      ,		return_tensors="""np"""				).input_features
        self.assertTrue(input_features.ndim == 3				)
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames				)
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size				)

        # Test not batched input
        UpperCamelCase      = feature_extractor(speech_inputs[0]      ,		return_tensors="""np"""				).input_features
        UpperCamelCase      = feature_extractor(np_speech_inputs[0]      ,		return_tensors="""np"""				).input_features
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

        # Test batched
        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				).input_features
        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				).input_features
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

        # Test 2-D numpy arrays are batched.
        UpperCamelCase      = [floats_list((1, x)				)[0] for x in (800, 800, 800)]
        UpperCamelCase      = np.asarray(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				).input_features
        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				).input_features
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

        # Test truncation required
        UpperCamelCase      = [floats_list((1, x)				)[0] for x in range(200      ,		(feature_extractor.n_samples + 500)      ,		200				)]
        UpperCamelCase      = [np.asarray(_SCREAMING_SNAKE_CASE				) for speech_input in speech_inputs]

        UpperCamelCase      = [x[: feature_extractor.n_samples] for x in speech_inputs]
        UpperCamelCase      = [np.asarray(_SCREAMING_SNAKE_CASE				) for speech_input in speech_inputs_truncated]

        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				).input_features
        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				).input_features
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        import torch

        UpperCamelCase      = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        UpperCamelCase      = np.random.rand(100      ,		32				).astype(np.floataa				)
        UpperCamelCase      = np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
              UpperCamelCase      = feature_extractor.pad([{"""input_features""": inputs}]      ,		return_tensors="""np"""				)
              self.assertTrue(np_processed.input_features.dtype == np.floataa				)
              UpperCamelCase      = feature_extractor.pad([{"""input_features""": inputs}]      ,		return_tensors="""pt"""				)
              self.assertTrue(pt_processed.input_features.dtype == torch.floataa				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,		"""clean"""      ,		split="""validation"""				)
        # automatic decoding with librispeech
        UpperCamelCase      = ds.sort("""id"""				).select(range(_SCREAMING_SNAKE_CASE				)				)[:num_samples]["""audio"""]

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

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = torch.tensor(
            [
                0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
                0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
                0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
                -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
            ]				)
        # fmt: on

        UpperCamelCase      = self._load_datasamples(1				)
        UpperCamelCase      = WhisperFeatureExtractor()
        UpperCamelCase      = feature_extractor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).input_features
        self.assertEqual(input_features.shape      ,		(1, 80, 3000)				)
        self.assertTrue(torch.allclose(input_features[0, 0, :30]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)


  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        UpperCamelCase      = self._load_datasamples(1				)[0]
        UpperCamelCase      = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
        UpperCamelCase      = feat_extract.zero_mean_unit_var_norm([audio]      ,		attention_mask=_SCREAMING_SNAKE_CASE				)[0]

        self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE				) < 1e-3				)				)
        self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE				) - 1				) < 1e-3				)				)








'''simple docstring'''

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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      hf_model.apply_weight_norm()

      UpperCamelCase      = checkpoint["""input_conv.weight_g"""]
      UpperCamelCase      = checkpoint["""input_conv.weight_v"""]
      UpperCamelCase      = checkpoint["""input_conv.bias"""]

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

                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

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

      hf_model.remove_weight_norm()







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			List[Any]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaHifiGanConfig()

      UpperCamelCase      = SpeechTaHifiGan(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      load_weights(orig_checkpoint["""model"""]["""generator"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

      UpperCamelCase      = np.load(__UpperCamelCase   )
      UpperCamelCase      = stats[0].reshape(-1   )
      UpperCamelCase      = stats[1].reshape(-1   )
      UpperCamelCase      = torch.from_numpy(__UpperCamelCase   ).float()
      UpperCamelCase      = 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__":
     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,
     )
