






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

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

def    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A			):






						"""simple docstring"""







						for attribute in key.split("""."""			):
												__magic_name__      :       Any       						= getattr(_A,   _A			)

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

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

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__      :       int       						= []

						if model_name == "encodec_24khz" or "encodec_32khz":
												__magic_name__      :       str       						= MAPPING_24K
						elif model_name == "encodec_48khz":
												__magic_name__      :       Optional[Any]       						= 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

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

																								__magic_name__      :       Dict       						= True
																								if "*" in mapped_key:
																														__magic_name__      :       Union[str, Any]       						= name.split(_A			)[0].split("""."""			)[-2]
																														__magic_name__      :       List[Any]       						= mapped_key.replace("""*""",   _A			)
																								if "weight_g" in name:
																														__magic_name__      :       Optional[int]       						= """weight_g"""
																								elif "weight_v" in name:
																														__magic_name__      :       Optional[Any]       						= """weight_v"""
																								elif "weight_ih_l0" in name:
																														__magic_name__      :       List[Any]       						= """weight_ih_l0"""
																								elif "weight_hh_l0" in name:
																														__magic_name__      :       Union[str, Any]       						= """weight_hh_l0"""
																								elif "bias_ih_l0" in name:
																														__magic_name__      :       Tuple       						= """bias_ih_l0"""
																								elif "bias_hh_l0" in name:
																														__magic_name__      :       Dict       						= """bias_hh_l0"""
																								elif "weight_ih_l1" in name:
																														__magic_name__      :       str       						= """weight_ih_l1"""
																								elif "weight_hh_l1" in name:
																														__magic_name__      :       int       						= """weight_hh_l1"""
																								elif "bias_ih_l1" in name:
																														__magic_name__      :       Dict       						= """bias_ih_l1"""
																								elif "bias_hh_l1" in name:
																														__magic_name__      :       Optional[int]       						= """bias_hh_l1"""
																								elif "bias" in name:
																														__magic_name__      :       Tuple       						= """bias"""
																								elif "weight" in name:
																														__magic_name__      :       Optional[int]       						= """weight"""
																								elif "running_mean" in name:
																														__magic_name__      :       Union[str, Any]       						= """running_mean"""
																								elif "running_var" in name:
																														__magic_name__      :       List[Any]       						= """running_var"""
																								elif "num_batches_tracked" in name:
																														__magic_name__      :       Union[str, Any]       						= """num_batches_tracked"""
																								else:
																														__magic_name__      :       Optional[int]       						= 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    UpperCamelCase						(	_A,   _A,   _A,   _A=None,   _A=None,   ):






						"""simple docstring"""







						if config_path is not None:
												__magic_name__      :       List[Any]       						= EncodecConfig.from_pretrained(_A			)
						else:
												__magic_name__      :       List[Any]       						= EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												__magic_name__      :       Any       						= [8, 5, 4, 4]
												__magic_name__      :       Optional[Any]       						= [2.2]
												__magic_name__      :       Dict       						= 64
												__magic_name__      :       Tuple       						= 32000
												__magic_name__      :       List[Any]       						= 2048
												__magic_name__      :       List[Any]       						= False
												__magic_name__      :       Optional[Any]       						= False
												__magic_name__      :       Tuple       						= False
						elif model_name == "encodec_48khz":
												__magic_name__      :       Tuple       						= [8, 5, 4, 2]
												__magic_name__      :       List[Any]       						= [3.0, 6.0, 12.0, 24.0]
												__magic_name__      :       int       						= 48000
												__magic_name__      :       Union[str, Any]       						= 2
												__magic_name__      :       Any       						= False
												__magic_name__      :       Optional[int]       						= """time_group_norm"""
												__magic_name__      :       int       						= True
												__magic_name__      :       int       						= 1.0
												__magic_name__      :       Optional[Any]       						= 0.01
						else:
												raise ValueError(f'Unknown model name: {model_name}'			)

						__magic_name__      :       Union[str, Any]       						= EncodecModel(_A			)

						__magic_name__      :       Union[str, 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(_A			)

						__magic_name__      :       Tuple       						= 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
												__magic_name__      :       Union[str, Any]       						= 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__":
		__magic_name__:					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."
		)

		__magic_name__:					Optional[int]							=     parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)








from decimal import Decimal, getcontext
from math import ceil, factorial

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if not isinstance(_A,   _A			):
												raise TypeError("""Undefined for non-integers"""			)
						elif precision < 1:
												raise ValueError("""Undefined for non-natural numbers"""			)

						__magic_name__      :       Dict       						= precision
						__magic_name__      :       str       						= ceil(precision / 14			)
						__magic_name__      :       List[str]       						= 426880 * Decimal(10005			).sqrt()
						__magic_name__      :       List[Any]       						= 1
						__magic_name__      :       Dict       						= 13591409
						__magic_name__      :       Tuple       						= Decimal(_A			)
						for k in range(1,   _A			):
												__magic_name__      :       List[Any]       						= factorial(6 * k			) // (factorial(3 * k			) * factorial(_A			) ** 3)
												linear_term += 545140134
												exponential_term *= -262537412640768000
												partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
						return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
		__magic_name__:					Tuple							=     50
		print(F"""The first {n} digits of pi is: {pi(n)}""")
