import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
     import librosa  # noqa: E402

     snake_case__						      =			True
     snake_case__						      =			''
except Exception as e:
     snake_case__						      =			False
     snake_case__						      =			(
         f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
     )


from PIL import Image  # noqa: E402



class 			UpperCamelCase					(			__lowercase ,	__lowercase			):

     '''simple docstring'''

     A_					    =	'mel_config.json'



     @register_to_config
     def __init__(       self					,					A_ = 2_56					,					A_ = 2_56					,					A_ = 2_20_50					,					A_ = 20_48					,					A_ = 5_12					,					A_ = 80					,					A_ = 32					,					)			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					hop_length
           _lowerCamelCase           =					sample_rate
           _lowerCamelCase           =					n_fft
           _lowerCamelCase           =					top_db
           _lowerCamelCase           =					n_iter
           self.set_resolution(A_					,					A_    )
           _lowerCamelCase           =					None

           if not _librosa_can_be_imported:
                 raise ValueError(_import_error    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					x_res
           _lowerCamelCase           =					y_res
           _lowerCamelCase           =					self.y_res
           _lowerCamelCase           =					self.x_res * self.hop_length - 1



     def 		UpperCamelCase_    (       self					,					A_ = None					,					A_ = None    )			->					str:






           """simple docstring"""
           if audio_file is not None:
                 _lowerCamelCase     ,       _lowerCamelCase           =					librosa.load(A_					,					mono=A_					,					sr=self.sr    )
           else:
                 _lowerCamelCase           =					raw_audio

           # Pad with silence if necessary.
           if len(self.audio    ) < self.x_res * self.hop_length:
                 _lowerCamelCase           =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio    ),)    )]    )



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           return len(self.audio    ) // self.slice_size



     def 		UpperCamelCase_    (       self					,					A_ = 0    )			->					np.ndarray:






           """simple docstring"""
           return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           return self.sr



     def 		UpperCamelCase_    (       self					,					A_    )			->					Image.Image:






           """simple docstring"""
           _lowerCamelCase           =					librosa.feature.melspectrogram(
               y=self.get_audio_slice(A_    )					,					sr=self.sr					,					n_fft=self.n_fft					,					hop_length=self.hop_length					,					n_mels=self.n_mels    )
           _lowerCamelCase           =					librosa.power_to_db(A_					,					ref=np.max					,					top_db=self.top_db    )
           _lowerCamelCase           =					(((log_S + self.top_db) * 2_55 / self.top_db).clip(0					,					2_55    ) + 0.5).astype(np.uinta    )
           _lowerCamelCase           =					Image.fromarray(A_    )
           return image





     def 		UpperCamelCase_    (       self					,					A_    )			->					np.ndarray:






           """simple docstring"""
           _lowerCamelCase           =					np.frombuffer(image.tobytes()					,					dtype='''uint8'''    ).reshape((image.height, image.width)    )
           _lowerCamelCase           =					bytedata.astype('''float'''    ) * self.top_db / 2_55 - self.top_db
           _lowerCamelCase           =					librosa.db_to_power(A_    )
           _lowerCamelCase           =					librosa.feature.inverse.mel_to_audio(
               A_					,					sr=self.sr					,					n_fft=self.n_fft					,					hop_length=self.hop_length					,					n_iter=self.n_iter    )
           return audio
import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

      print(F'Building PyTorch model from configuration: {config}'		)

      # Load weights from tf checkpoint
      load_tf_weights_in_canine(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)

      # Save pytorch-model (weights and configuration)
      print(F'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(__UpperCAmelCase		)

      # Save tokenizer files
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)