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  :     int       =  logging.get_logger("""transformers.models.encodec""")

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





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				for attribute in key.split('''.'''							):
								__lowercase 			=							getattr(__lowerCamelCase							,			__lowerCamelCase							)

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

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





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]

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

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

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

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

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

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





@torch.no_grad()
def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):







				"""simple docstring"""



				if config_path is not None:
								__lowercase 			=							EncodecConfig.from_pretrained(__lowerCamelCase							)
				else:
								__lowercase 			=							EncodecConfig()

				if model_name == "encodec_24khz":
								pass  # config is already correct
				elif model_name == "encodec_32khz":
								__lowercase 			=							[8, 5, 4, 4]
								__lowercase 			=							[2.2]
								__lowercase 			=							64
								__lowercase 			=							32000
								__lowercase 			=							2048
								__lowercase 			=							False
								__lowercase 			=							False
								__lowercase 			=							False
				elif model_name == "encodec_48khz":
								__lowercase 			=							[8, 5, 4, 2]
								__lowercase 			=							[3.0, 6.0, 12.0, 24.0]
								__lowercase 			=							48000
								__lowercase 			=							2
								__lowercase 			=							False
								__lowercase 			=							'''time_group_norm'''
								__lowercase 			=							True
								__lowercase 			=							1.0
								__lowercase 			=							0.01
				else:
								raise ValueError(F"Unknown model name: {model_name}"							)

				__lowercase 			=							EncodecModel(__lowerCamelCase							)

				__lowercase 			=							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(__lowerCamelCase							)

				__lowercase 			=							torch.load(__lowerCamelCase							)
				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
								__lowercase 			=							original_checkpoint['''best_state''']
				recursively_load_weights(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							)
				model.save_pretrained(__lowerCamelCase							)

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


if __name__ == "__main__":
		__a  :     List[Any]       =  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  :     List[str]       =  parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)





from math import pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

