
'''simple docstring'''



import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_lowercase          =						logging.get_logger("""transformers.models.encodec""")

_lowercase          =						{
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
_lowercase          =						{
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
_lowercase          =						{
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
_lowercase          =						{
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
_lowercase          =						{
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
_lowercase          =						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowercase          =						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowercase          =						[]
_lowercase          =						[]


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any					,      __lowerCamelCase :str					,      __lowerCamelCase :Optional[int]						):
						for attribute in key.split("""."""						):
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						)

						if weight_type is not None:
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						).shape
						else:
												_lowerCAmelCase    		=					hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
												    f' {value.shape} for {full_name}'						)

						if weight_type == "weight":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_g":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_v":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias":
												_lowerCAmelCase    		=					value
						elif weight_type == "running_mean":
												_lowerCAmelCase    		=					value
						elif weight_type == "running_var":
												_lowerCAmelCase    		=					value
						elif weight_type == "num_batches_tracked":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_ih_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_hh_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_ih_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_hh_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_ih_l1":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_hh_l1":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_ih_l1":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_hh_l1":
												_lowerCAmelCase    		=					value
						else:
												_lowerCAmelCase    		=					value

						logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'						)


def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :str						):
						for key in ignore_keys:
												if key.endswith(""".*"""						):
																		if name.startswith(key[:-1]						):
																								return True
												elif ".*." in key:
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					key.split(""".*."""						)
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False


def A (__lowerCamelCase :Any					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict						):
						_lowerCAmelCase    		=					[]

						if model_name == "encodec_24khz" or "encodec_32khz":
												_lowerCAmelCase    		=					MAPPING_24K
						elif model_name == "encodec_48khz":
												_lowerCAmelCase    		=					MAPPING_48K
						else:
												raise ValueError(f'Unsupported model: {model_name}'						)

						for name, value in orig_dict.items():
												if should_ignore(__lowerCamelCase					,      __lowerCamelCase						):
																		logger.info(f'{name} was ignored'						)
																		continue

												_lowerCAmelCase    		=					False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					key.split(""".*."""						)
																								if prefix in name and suffix in name:
																														_lowerCAmelCase    		=					suffix

																		if key in name:
																								# HACK otherwise .embed gets initialized with .embed_avg too
																								if key.endswith("""embed"""						) and name.endswith("""embed_avg"""						):
																														continue

																								_lowerCAmelCase    		=					True
																								if "*" in mapped_key:
																														_lowerCAmelCase    		=					name.split(__lowerCamelCase						)[0].split("""."""						)[-2]
																														_lowerCAmelCase    		=					mapped_key.replace("""*"""					,      __lowerCamelCase						)
																								if "weight_g" in name:
																														_lowerCAmelCase    		=					"""weight_g"""
																								elif "weight_v" in name:
																														_lowerCAmelCase    		=					"""weight_v"""
																								elif "weight_ih_l0" in name:
																														_lowerCAmelCase    		=					"""weight_ih_l0"""
																								elif "weight_hh_l0" in name:
																														_lowerCAmelCase    		=					"""weight_hh_l0"""
																								elif "bias_ih_l0" in name:
																														_lowerCAmelCase    		=					"""bias_ih_l0"""
																								elif "bias_hh_l0" in name:
																														_lowerCAmelCase    		=					"""bias_hh_l0"""
																								elif "weight_ih_l1" in name:
																														_lowerCAmelCase    		=					"""weight_ih_l1"""
																								elif "weight_hh_l1" in name:
																														_lowerCAmelCase    		=					"""weight_hh_l1"""
																								elif "bias_ih_l1" in name:
																														_lowerCAmelCase    		=					"""bias_ih_l1"""
																								elif "bias_hh_l1" in name:
																														_lowerCAmelCase    		=					"""bias_hh_l1"""
																								elif "bias" in name:
																														_lowerCAmelCase    		=					"""bias"""
																								elif "weight" in name:
																														_lowerCAmelCase    		=					"""weight"""
																								elif "running_mean" in name:
																														_lowerCAmelCase    		=					"""running_mean"""
																								elif "running_var" in name:
																														_lowerCAmelCase    		=					"""running_var"""
																								elif "num_batches_tracked" in name:
																														_lowerCAmelCase    		=					"""num_batches_tracked"""
																								else:
																														_lowerCAmelCase    		=					None
																								set_recursively(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		continue
												if not is_used:
																		unused_weights.append(__lowerCamelCase						)

						logger.warning(f'Unused weights: {unused_weights}'						)


@torch.no_grad()
def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :str					,      __lowerCamelCase :int=None					,      __lowerCamelCase :str=None					,      ):
						if config_path is not None:
												_lowerCAmelCase    		=					EncodecConfig.from_pretrained(__lowerCamelCase						)
						else:
												_lowerCAmelCase    		=					EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												_lowerCAmelCase    		=					[8, 5, 4, 4]
												_lowerCAmelCase    		=					[2.2]
												_lowerCAmelCase    		=					64
												_lowerCAmelCase    		=					32000
												_lowerCAmelCase    		=					2048
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					False
						elif model_name == "encodec_48khz":
												_lowerCAmelCase    		=					[8, 5, 4, 2]
												_lowerCAmelCase    		=					[3.0, 6.0, 12.0, 24.0]
												_lowerCAmelCase    		=					48000
												_lowerCAmelCase    		=					2
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					"""time_group_norm"""
												_lowerCAmelCase    		=					True
												_lowerCAmelCase    		=					1.0
												_lowerCAmelCase    		=					0.01
						else:
												raise ValueError(f'Unknown model name: {model_name}'						)

						_lowerCAmelCase    		=					EncodecModel(__lowerCamelCase						)

						_lowerCAmelCase    		=					EncodecFeatureExtractor(
						    feature_size=config.audio_channels					,      sampling_rate=config.sampling_rate					,      chunk_length_s=config.chunk_length_s					,      overlap=config.overlap					,      )
						feature_extractor.save_pretrained(__lowerCamelCase						)

						_lowerCAmelCase    		=					torch.load(__lowerCamelCase						)
						if "best_state" in original_checkpoint:
												# we might have a training state saved, in which case discard the yaml results and just retain the weights
												_lowerCAmelCase    		=					original_checkpoint["""best_state"""]
						recursively_load_weights(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
						model.save_pretrained(__lowerCamelCase						)

						if repo_id:
												print("""Pushing to the hub..."""						)
												feature_extractor.push_to_hub(__lowerCamelCase						)
												model.push_to_hub(__lowerCamelCase						)


if __name__ == "__main__":
						_lowercase          =						argparse.ArgumentParser()
						parser.add_argument(
						    """--model""",
						    default="""encodec_24khz""",
						    type=str,
						    help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
						)
						parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
						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."""
						)

						_lowercase          =						parser.parse_args()
						convert_checkpoint(
						    args.model,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.push_to_hub,
						)


'''simple docstring'''



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  ['''input_values''', '''padding_mask''']




						def __init__(   self       ,  _lowercase = 1       ,  _lowercase = 24_000       ,  _lowercase = 0.0       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					chunk_length_s
												_lowerCAmelCase    		=					overlap




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1       ,  int((1.0 - self.overlap) * self.chunk_length ) )




						def __call__(   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																								    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
																								    F' {self.sampling_rate} and not {sampling_rate}.' )
												else:
																		logger.warning(
																		    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
																		    """Failing to do so can result in silent errors that might be hard to debug.""" )

												if padding and truncation:
																		raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
												elif padding is None:
																		# by default let's pad the inputs
																		_lowerCAmelCase    		=					True

												_lowerCAmelCase    		=					bool(
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_audio[0]       ,  (np.ndarray, tuple, list) )) )

												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ).T for audio in raw_audio]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_audio.astype(np.floataa )

												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase ).T]

												# verify inputs are valid
												for idx, example in enumerate(_lowercase ):
																		if example.ndim > 2:
																								raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
																		if self.feature_size == 1 and example.ndim != 1:
																								raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
																		if self.feature_size == 2 and example.shape[-1] != 2:
																								raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )

												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					BatchFeature({"""input_values""": raw_audio} )
												if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																		if truncation:
																								_lowerCAmelCase    		=					min(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.floor(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																								_lowerCAmelCase    		=					max(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.ceil(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																								_lowerCAmelCase    		=					"""max_length"""
																		else:
																								_lowerCAmelCase    		=					input_values

        # normal padding on batch
												if padded_inputs is None:
																		_lowerCAmelCase    		=					self.pad(
																		    _lowercase       ,  max_length=_lowercase       ,  truncation=_lowercase       ,  padding=_lowercase       ,  return_attention_mask=_lowercase       ,  )
																		if padding:
																								_lowerCAmelCase    		=					padded_inputs.pop("""attention_mask""" )

												_lowerCAmelCase    		=					[]
												for example in padded_inputs.pop("""input_values""" ):
																		if self.feature_size == 1:
																								_lowerCAmelCase    		=					example[..., None]
																		input_values.append(example.T )

												_lowerCAmelCase    		=					input_values
												if return_tensors is not None:
																		_lowerCAmelCase    		=					padded_inputs.convert_to_tensors(_lowercase )

												return padded_inputs
