



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

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





def 							A     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[str]   )    ->			List[str]:







			'''simple docstring'''
			for attribute in key.split('.'   ):
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   )

			if weight_type is not None:
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   ).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 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       int   )    ->			Optional[Any]:







			'''simple docstring'''
			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 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Any   )    ->			Optional[int]:







			'''simple docstring'''
			_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(_UpperCAmelCase							,       _UpperCAmelCase   ):
									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(_UpperCAmelCase   )[0].split('.'   )[-2]
															_UpperCAmelCase			=       mapped_key.replace('*'							,       _UpperCAmelCase   )
												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(_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 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]=None							,       _UpperCAmelCase    :       Union[str, Any]=None							,       )    ->			Any:







			'''simple docstring'''
			if config_path is not None:
						_UpperCAmelCase			=       EncodecConfig.from_pretrained(_UpperCAmelCase   )
			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			=       64
						_UpperCAmelCase			=       32_000
						_UpperCAmelCase			=       2_048
						_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			=       48_000
						_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(_UpperCAmelCase   )

			_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(_UpperCAmelCase   )

			_UpperCAmelCase			=       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
						_UpperCAmelCase			=       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__":
							UpperCAmelCase__       =	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."
							)

							UpperCAmelCase__       =	parser.parse_args()
							convert_checkpoint(
							    args.model,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       set()
			# keep track of all the paths to be checked
			_UpperCAmelCase			=       [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						_UpperCAmelCase			=       queue.pop(0   )
						# get the last node from the path
						_UpperCAmelCase			=       path[-1]
						if node not in explored:
									_UpperCAmelCase			=       graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												_UpperCAmelCase			=       list(_UpperCAmelCase   )
												new_path.append(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCAmelCase   )

    # in case there's no path between the 2 nodes
			return []





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# Keep tab on distances from `start` node.
			_UpperCAmelCase			=       {start: 0, target: -1}
			while queue:
						_UpperCAmelCase			=       queue.pop(0   )
						if node == target:
									_UpperCAmelCase			=       (
									    dist[node] if dist[target] == -1 else min(dist[target]							,       dist[node]   )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_UpperCAmelCase			=       dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4
