





'''simple docstring'''







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

	_a					: str      =    True
	_a					: Union[str, Any]      =    ""
except Exception as e:
	_a					: Optional[Any]      =    False
	_a					: Union[str, Any]      =    (
	    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							(						lowerCAmelCase__	,					lowerCAmelCase__   ):
		a   :   List[Any]     ='mel_config.json'
		@register_to_config
		def __init__(  self,__SCREAMING_SNAKE_CASE = 2_56,__SCREAMING_SNAKE_CASE = 2_56,__SCREAMING_SNAKE_CASE = 2_20_50,__SCREAMING_SNAKE_CASE = 20_48,__SCREAMING_SNAKE_CASE = 5_12,__SCREAMING_SNAKE_CASE = 80,__SCREAMING_SNAKE_CASE = 32,):



					'''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   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				x_res
					__lowerCAmelCase											=				y_res
					__lowerCAmelCase											=				self.y_res
					__lowerCAmelCase											=				self.x_res * self.hop_length - 1
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None			):



					'''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   lowerCamelCase__			(  self			):



					'''simple docstring'''

					return len(self.audio			) // self.slice_size
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE = 0			):



					'''simple docstring'''

					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					return self.sr
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''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   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''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
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoConfig(
        hidden_size=config_json['n_embd']       ,      num_layers=config_json['n_layer']       ,      num_heads=config_json['n_head']       ,      attention_types=config_json['attention_types']       ,      max_position_embeddings=config_json['n_positions']       ,      resid_dropout=config_json['res_dropout']       ,      embed_dropout=config_json['embed_dropout']       ,      attention_dropout=config_json['attn_dropout']       ,      )
    print(f"""Building PyTorch model from configuration: {config}"""				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoForCausalLM(_SCREAMING_SNAKE_CASE				)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
    model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
       UpperCamelCase__     :					Tuple								=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained mesh-tf model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


