
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()
A     :	Optional[Any]    = logging.get_logger('transformers.models.encodec')

A     :	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',
}
A     :	Any    = {
    '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',
}
A     :	List[Any]    = {
    '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',
}
A     :	List[Any]    = {
    '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',
}
A     :	Union[str, Any]    = {
    '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',
}
A     :	Any    = {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
A     :	List[Any]    = {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
A     :	List[Any]    = []
A     :	str    = []


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__  )       ->  List[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     __lowerCAmelCase			(			a__						,    a__  )       ->  Optional[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     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Union[str, Any]:
				__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     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__=None						,    a__=None						,    )       ->  Union[str, Any]:
				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__":
					A     :	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.'
					)

					A     :	Union[str, Any]    = parser.parse_args()
					convert_checkpoint(
					    args.model,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _snake_case  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

