





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

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



def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Optional[int]							,      _lowercase       :		List[str]							,      _lowercase       :		List[str]							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					for attribute in key.split("."	):
										UpperCAmelCase			:   Dict      =				getattr(_lowercase							,      _lowercase	)

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




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[int]      =				[]

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

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

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

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



@torch.no_grad()
def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Any							,      _lowercase       :		Optional[int]							,      _lowercase       :		Tuple=None							,      _lowercase       :		Tuple=None							,      ):




					'''simple docstring'''






					if config_path is not None:
										UpperCAmelCase			:   Union[str, Any]      =				EncodecConfig.from_pretrained(_lowercase	)
					else:
										UpperCAmelCase			:   Any      =				EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										UpperCAmelCase			:   str      =				[8, 5, 4, 4]
										UpperCAmelCase			:   Optional[int]      =				[2.2]
										UpperCAmelCase			:   Any      =				64
										UpperCAmelCase			:   int      =				3_20_00
										UpperCAmelCase			:   Optional[int]      =				20_48
										UpperCAmelCase			:   Any      =				False
										UpperCAmelCase			:   int      =				False
										UpperCAmelCase			:   Optional[int]      =				False
					elif model_name == "encodec_48khz":
										UpperCAmelCase			:   Optional[int]      =				[8, 5, 4, 2]
										UpperCAmelCase			:   Dict      =				[3.0, 6.0, 12.0, 24.0]
										UpperCAmelCase			:   List[str]      =				4_80_00
										UpperCAmelCase			:   Union[str, Any]      =				2
										UpperCAmelCase			:   Optional[int]      =				False
										UpperCAmelCase			:   Union[str, Any]      =				"time_group_norm"
										UpperCAmelCase			:   List[Any]      =				True
										UpperCAmelCase			:   Any      =				1.0
										UpperCAmelCase			:   List[str]      =				0.0_1
					else:
										raise ValueError(F"""Unknown model name: {model_name}"""	)

					UpperCAmelCase			:   Optional[int]      =				EncodecModel(_lowercase	)

					UpperCAmelCase			:   Optional[int]      =				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(_lowercase	)

					UpperCAmelCase			:   List[Any]      =				torch.load(_lowercase	)
					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			:   str      =				original_checkpoint["best_state"]
					recursively_load_weights(_lowercase							,      _lowercase							,      _lowercase	)
					model.save_pretrained(_lowercase	)

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


if __name__ == "__main__":
	snake_case_   :   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_   :   Tuple        =       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 math



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					return math.sqrt(_lowercase	) * math.sqrt(_lowercase	) == num



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				0
					UpperCAmelCase			:   Tuple      =				n
					while left <= right:
										UpperCAmelCase			:   Tuple      =				(left + right) // 2
										if mid**2 == n:
															return True
										elif mid**2 > n:
															UpperCAmelCase			:   List[str]      =				mid - 1
										else:
															UpperCAmelCase			:   Any      =				mid + 1
					return False


if __name__ == "__main__":
	import doctest

	doctest.testmod()


