


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()
_snake_case     :		Optional[int]						=				logging.get_logger("transformers.models.encodec")

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







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase	):




							for attribute in key.split("."	):
														__snake_case		:			List[str]							 =   getattr(_UpperCamelCase							,	_UpperCamelCase	)

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

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







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):




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







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			int							 =   []

							if model_name == "encodec_24khz" or "encodec_32khz":
														__snake_case		:			Any							 =   MAPPING_24K
							elif model_name == "encodec_48khz":
														__snake_case		:			Dict							 =   MAPPING_48K
							else:
														raise ValueError(F'Unsupported model: {model_name}'	)

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

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

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

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

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







@torch.no_grad()
def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase=None							,	__lowerCamelCase=None							,	):




							if config_path is not None:
														__snake_case		:			Union[str, Any]							 =   EncodecConfig.from_pretrained(_UpperCamelCase	)
							else:
														__snake_case		:			Tuple							 =   EncodecConfig()

							if model_name == "encodec_24khz":
														pass  # config is already correct
							elif model_name == "encodec_32khz":
														__snake_case		:			Union[str, Any]							 =   [8, 5, 4, 4]
														__snake_case		:			str							 =   [2.2]
														__snake_case		:			Optional[Any]							 =   6_4
														__snake_case		:			Tuple							 =   3_2_0_0_0
														__snake_case		:			Optional[Any]							 =   2_0_4_8
														__snake_case		:			List[Any]							 =   False
														__snake_case		:			Optional[Any]							 =   False
														__snake_case		:			Any							 =   False
							elif model_name == "encodec_48khz":
														__snake_case		:			Any							 =   [8, 5, 4, 2]
														__snake_case		:			Optional[Any]							 =   [3.0, 6.0, 1_2.0, 2_4.0]
														__snake_case		:			Optional[Any]							 =   4_8_0_0_0
														__snake_case		:			Dict							 =   2
														__snake_case		:			Optional[Any]							 =   False
														__snake_case		:			str							 =   "time_group_norm"
														__snake_case		:			str							 =   True
														__snake_case		:			Any							 =   1.0
														__snake_case		:			str							 =   0.0_1
							else:
														raise ValueError(F'Unknown model name: {model_name}'	)

							__snake_case		:			List[str]							 =   EncodecModel(_UpperCamelCase	)

							__snake_case		:			Optional[Any]							 =   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(_UpperCamelCase	)

							__snake_case		:			Tuple							 =   torch.load(_UpperCamelCase	)
							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
														__snake_case		:			Tuple							 =   original_checkpoint["best_state"]
							recursively_load_weights(_UpperCamelCase							,	_UpperCamelCase							,	_UpperCamelCase	)
							model.save_pretrained(_UpperCamelCase	)

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


if __name__ == "__main__":
			_snake_case     :		Optional[int]						=				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."
			)

			_snake_case     :		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"""


from typing import TYPE_CHECKING

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


A     :   Optional[int]			    =			{
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Tuple			    =			[
	    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "AltCLIPPreTrainedModel",
	    "AltCLIPModel",
	    "AltCLIPTextModel",
	    "AltCLIPVisionModel",
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	A     :   int			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




