




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

a													=			{
    """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													=			{
    """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													=			{
    """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													=			{
    """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													=			{
    """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													=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a													=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a													=			[]
a													=			[]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Union[str, Any]:

						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

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

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				int:

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Any:
						_UpperCAmelCase      =			[]

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

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

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

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




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     )						->				str:

						if config_path is not None:
												_UpperCAmelCase      =			EncodecConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												_UpperCAmelCase      =			[8, 5, 4, 4]
												_UpperCAmelCase      =			[2.2]
												_UpperCAmelCase      =			6_4
												_UpperCAmelCase      =			3_2_0_0_0
												_UpperCAmelCase      =			2_0_4_8
												_UpperCAmelCase      =			False
												_UpperCAmelCase      =			False
												_UpperCAmelCase      =			False
						elif model_name == "encodec_48khz":
												_UpperCAmelCase      =			[8, 5, 4, 2]
												_UpperCAmelCase      =			[3.0, 6.0, 12.0, 24.0]
												_UpperCAmelCase      =			4_8_0_0_0
												_UpperCAmelCase      =			2
												_UpperCAmelCase      =			False
												_UpperCAmelCase      =			"""time_group_norm"""
												_UpperCAmelCase      =			True
												_UpperCAmelCase      =			1.0
												_UpperCAmelCase      =			0.01
						else:
												raise ValueError(f"Unknown model name: {model_name}"       )

						_UpperCAmelCase      =			EncodecModel(snake_case       )

						_UpperCAmelCase      =			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(snake_case       )

						_UpperCAmelCase      =			torch.load(snake_case       )
						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      =			original_checkpoint["""best_state"""]
						recursively_load_weights(snake_case			,     snake_case			,     snake_case       )
						model.save_pretrained(snake_case       )

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


if __name__ == "__main__":
				a													=			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													=			parser.parse_args()
				convert_checkpoint(
				    args.model,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)





from __future__ import annotations




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[int]:
						if len(snake_case       ) == 0:
												return array

						_UpperCAmelCase    ,					_UpperCAmelCase      =			min(snake_case       ), max(snake_case       )

						# Compute the variables
						_UpperCAmelCase      =			_max - _min + 1
						_UpperCAmelCase    ,					_UpperCAmelCase      =			[0] * holes_range, [0] * holes_range

						# Make the sorting.
						for i in array:
												_UpperCAmelCase      =			i - _min
												_UpperCAmelCase      =			i
												holes_repeat[index] += 1

						# Makes the array back by replacing the numbers.
						_UpperCAmelCase      =			0
						for i in range(snake_case       ):
												while holes_repeat[i] > 0:
																		_UpperCAmelCase      =			holes[i]
																		index += 1
																		holes_repeat[i] -= 1

    # Returns the sorted array.
						return array


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				a													=			input("Enter numbers separated by comma:\n")
				a													=			[int(x) for x in user_input.split(",")]
				print(pigeon_sort(unsorted))