






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()
__UpperCAmelCase								=						logging.get_logger('transformers.models.encodec')

__UpperCAmelCase								=						{
    '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',
}
__UpperCAmelCase								=						{
    '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',
}
__UpperCAmelCase								=						{
    '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',
}
__UpperCAmelCase								=						{
    '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',
}
__UpperCAmelCase								=						{
    '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',
}
__UpperCAmelCase								=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__UpperCAmelCase								=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__UpperCAmelCase								=						[]
__UpperCAmelCase								=						[]




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							Tuple     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							List[Any]  ):


					'''simple docstring'''


					for attribute in key.split('.'  ):
										UpperCAmelCase_    :      Union[str, Any]            =						getattr(__snake_case     ,       __snake_case  )

					if weight_type is not None:
										UpperCAmelCase_    :      Optional[Any]            =						getattr(__snake_case     ,       __snake_case  ).shape
					else:
										UpperCAmelCase_    :      Optional[int]            =						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":
										UpperCAmelCase_    :      Dict            =						value
					elif weight_type == "weight_g":
										UpperCAmelCase_    :      List[str]            =						value
					elif weight_type == "weight_v":
										UpperCAmelCase_    :      List[str]            =						value
					elif weight_type == "bias":
										UpperCAmelCase_    :      Optional[Any]            =						value
					elif weight_type == "running_mean":
										UpperCAmelCase_    :      str            =						value
					elif weight_type == "running_var":
										UpperCAmelCase_    :      Optional[int]            =						value
					elif weight_type == "num_batches_tracked":
										UpperCAmelCase_    :      Union[str, Any]            =						value
					elif weight_type == "weight_ih_l0":
										UpperCAmelCase_    :      Tuple            =						value
					elif weight_type == "weight_hh_l0":
										UpperCAmelCase_    :      Any            =						value
					elif weight_type == "bias_ih_l0":
										UpperCAmelCase_    :      List[str]            =						value
					elif weight_type == "bias_hh_l0":
										UpperCAmelCase_    :      List[Any]            =						value
					elif weight_type == "weight_ih_l1":
										UpperCAmelCase_    :      Union[str, Any]            =						value
					elif weight_type == "weight_hh_l1":
										UpperCAmelCase_    :      List[str]            =						value
					elif weight_type == "bias_ih_l1":
										UpperCAmelCase_    :      Union[str, Any]            =						value
					elif weight_type == "bias_hh_l1":
										UpperCAmelCase_    :      Any            =						value
					else:
										UpperCAmelCase_    :      int            =						value

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




def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str  ):


					'''simple docstring'''


					for key in ignore_keys:
										if key.endswith('.*'  ):
															if name.startswith(key[:-1]  ):
																				return True
										elif ".*." in key:
															UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						key.split('.*.'  )
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False




def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							Any     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						[]

					if model_name == "encodec_24khz" or "encodec_32khz":
										UpperCAmelCase_    :      Optional[int]            =						MAPPING_24K
					elif model_name == "encodec_48khz":
										UpperCAmelCase_    :      Union[str, Any]            =						MAPPING_48K
					else:
										raise ValueError(F"Unsupported model: {model_name}"  )

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

										UpperCAmelCase_    :      Any            =						False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						key.split('.*.'  )
																				if prefix in name and suffix in name:
																									UpperCAmelCase_    :      str            =						suffix

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

																				UpperCAmelCase_    :      Union[str, Any]            =						True
																				if "*" in mapped_key:
																									UpperCAmelCase_    :      str            =						name.split(__snake_case  )[0].split('.'  )[-2]
																									UpperCAmelCase_    :      str            =						mapped_key.replace('*'     ,       __snake_case  )
																				if "weight_g" in name:
																									UpperCAmelCase_    :      Dict            =						'weight_g'
																				elif "weight_v" in name:
																									UpperCAmelCase_    :      str            =						'weight_v'
																				elif "weight_ih_l0" in name:
																									UpperCAmelCase_    :      List[str]            =						'weight_ih_l0'
																				elif "weight_hh_l0" in name:
																									UpperCAmelCase_    :      Dict            =						'weight_hh_l0'
																				elif "bias_ih_l0" in name:
																									UpperCAmelCase_    :      Union[str, Any]            =						'bias_ih_l0'
																				elif "bias_hh_l0" in name:
																									UpperCAmelCase_    :      List[str]            =						'bias_hh_l0'
																				elif "weight_ih_l1" in name:
																									UpperCAmelCase_    :      Tuple            =						'weight_ih_l1'
																				elif "weight_hh_l1" in name:
																									UpperCAmelCase_    :      int            =						'weight_hh_l1'
																				elif "bias_ih_l1" in name:
																									UpperCAmelCase_    :      int            =						'bias_ih_l1'
																				elif "bias_hh_l1" in name:
																									UpperCAmelCase_    :      List[str]            =						'bias_hh_l1'
																				elif "bias" in name:
																									UpperCAmelCase_    :      List[Any]            =						'bias'
																				elif "weight" in name:
																									UpperCAmelCase_    :      List[str]            =						'weight'
																				elif "running_mean" in name:
																									UpperCAmelCase_    :      Optional[int]            =						'running_mean'
																				elif "running_var" in name:
																									UpperCAmelCase_    :      Tuple            =						'running_var'
																				elif "num_batches_tracked" in name:
																									UpperCAmelCase_    :      int            =						'num_batches_tracked'
																				else:
																									UpperCAmelCase_    :      Any            =						None
																				set_recursively(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
															continue
										if not is_used:
															unused_weights.append(__snake_case  )

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




@torch.no_grad()
def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							int     ,       __snake_case    :							int     ,       __snake_case    :							str=None     ,       __snake_case    :							Any=None     ,       ):


					'''simple docstring'''


					if config_path is not None:
										UpperCAmelCase_    :      Tuple            =						EncodecConfig.from_pretrained(__snake_case  )
					else:
										UpperCAmelCase_    :      Optional[Any]            =						EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										UpperCAmelCase_    :      Dict            =						[8, 5, 4, 4]
										UpperCAmelCase_    :      Dict            =						[2.2]
										UpperCAmelCase_    :      Union[str, Any]            =						64
										UpperCAmelCase_    :      Tuple            =						32_000
										UpperCAmelCase_    :      List[Any]            =						2_048
										UpperCAmelCase_    :      List[str]            =						False
										UpperCAmelCase_    :      Tuple            =						False
										UpperCAmelCase_    :      Union[str, Any]            =						False
					elif model_name == "encodec_48khz":
										UpperCAmelCase_    :      Optional[int]            =						[8, 5, 4, 2]
										UpperCAmelCase_    :      Optional[Any]            =						[3.0, 6.0, 12.0, 24.0]
										UpperCAmelCase_    :      Optional[int]            =						48_000
										UpperCAmelCase_    :      Optional[Any]            =						2
										UpperCAmelCase_    :      int            =						False
										UpperCAmelCase_    :      Optional[Any]            =						'time_group_norm'
										UpperCAmelCase_    :      Optional[Any]            =						True
										UpperCAmelCase_    :      Optional[int]            =						1.0
										UpperCAmelCase_    :      Optional[int]            =						0.01
					else:
										raise ValueError(F"Unknown model name: {model_name}"  )

					UpperCAmelCase_    :      List[str]            =						EncodecModel(__snake_case  )

					UpperCAmelCase_    :      List[str]            =						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(__snake_case  )

					UpperCAmelCase_    :      Dict            =						torch.load(__snake_case  )
					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
										UpperCAmelCase_    :      List[str]            =						original_checkpoint['best_state']
					recursively_load_weights(__snake_case     ,       __snake_case     ,       __snake_case  )
					model.save_pretrained(__snake_case  )

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


if __name__ == "__main__":
		__UpperCAmelCase								=						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.'
		)

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














import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCAmelCase								=						logging.get_logger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							None:
							warnings.warn(
							    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use ImageGPTImageProcessor instead.'				,		_UpperCamelCase				,		)
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )






