


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()
_lowerCAmelCase:						Union[str, Any]   				=       logging.get_logger('transformers.models.encodec')

_lowerCAmelCase:						List[Any]   				=       {
    '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',
}
_lowerCAmelCase:						str   				=       {
    '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',
}
_lowerCAmelCase:						Optional[int]   				=       {
    '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',
}
_lowerCAmelCase:						Dict   				=       {
    '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',
}
_lowerCAmelCase:						Tuple   				=       {
    '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',
}
_lowerCAmelCase:						str   				=       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowerCAmelCase:						Dict   				=       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowerCAmelCase:						Tuple   				=       []
_lowerCAmelCase:						str   				=       []






def   _lowercase(					__a				:						Any    ,     __a				:						List[str]    ,     __a				:						Tuple    ,     __a				:						Union[str, Any]    ,     __a				:						Optional[Any]     ):
							for attribute in key.split('.'     ):
														a__      =getattr(__a    ,     __a     )

							if weight_type is not None:
														a__      =getattr(__a    ,     __a     ).shape
							else:
														a__      =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":
														a__      =value
							elif weight_type == "weight_g":
														a__      =value
							elif weight_type == "weight_v":
														a__      =value
							elif weight_type == "bias":
														a__      =value
							elif weight_type == "running_mean":
														a__      =value
							elif weight_type == "running_var":
														a__      =value
							elif weight_type == "num_batches_tracked":
														a__      =value
							elif weight_type == "weight_ih_l0":
														a__      =value
							elif weight_type == "weight_hh_l0":
														a__      =value
							elif weight_type == "bias_ih_l0":
														a__      =value
							elif weight_type == "bias_hh_l0":
														a__      =value
							elif weight_type == "weight_ih_l1":
														a__      =value
							elif weight_type == "weight_hh_l1":
														a__      =value
							elif weight_type == "bias_ih_l1":
														a__      =value
							elif weight_type == "bias_hh_l1":
														a__      =value
							else:
														a__      =value

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






def   _lowercase(					__a				:						Optional[int]    ,     __a				:						Union[str, Any]     ):
							for key in ignore_keys:
														if key.endswith('.*'     ):
																					if name.startswith(key[:-1]     ):
																												return True
														elif ".*." in key:
																					a__    ,     a__      =key.split('.*.'     )
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False






def   _lowercase(					__a				:						str    ,     __a				:						int    ,     __a				:						Tuple     ):
							a__      =[]

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

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

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

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

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

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






@torch.no_grad()
def   _lowercase(					__a				:						str    ,     __a				:						int    ,     __a				:						str    ,     __a				:						Tuple=None    ,     __a				:						Optional[int]=None    ,     ):
							if config_path is not None:
														a__      =EncodecConfig.from_pretrained(__a     )
							else:
														a__      =EncodecConfig()

							if model_name == "encodec_24khz":
														pass  # config is already correct
							elif model_name == "encodec_32khz":
														a__      =[8, 5, 4, 4]
														a__      =[2.2]
														a__      =64
														a__      =3_2000
														a__      =2048
														a__      =False
														a__      =False
														a__      =False
							elif model_name == "encodec_48khz":
														a__      =[8, 5, 4, 2]
														a__      =[3.0, 6.0, 12.0, 24.0]
														a__      =4_8000
														a__      =2
														a__      =False
														a__      ='time_group_norm'
														a__      =True
														a__      =1.0
														a__      =0.01
							else:
														raise ValueError(f"""Unknown model name: {model_name}"""     )

							a__      =EncodecModel(__a     )

							a__      =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(__a     )

							a__      =torch.load(__a     )
							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
														a__      =original_checkpoint['best_state']
							recursively_load_weights(__a    ,     __a    ,     __a     )
							model.save_pretrained(__a     )

							if repo_id:
														print('Pushing to the hub...'     )
														feature_extractor.push_to_hub(__a     )
														model.push_to_hub(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						str   				=       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.'
	)

	_lowerCAmelCase:						str   				=       parser.parse_args()
	convert_checkpoint(
	    args.model,
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.push_to_hub,
	)








'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d