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

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):




			for attribute in key.split("."    ):
						A							:					Optional[Any]              =						getattr(_A ,    _A    )

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

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):




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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Dict              =						[]

			if model_name == "encodec_24khz" or "encodec_32khz":
						A							:					str              =						MAPPING_24K
			elif model_name == "encodec_48khz":
						A							:					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

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

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

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




			if config_path is not None:
						A							:					str              =						EncodecConfig.from_pretrained(_A    )
			else:
						A							:					Optional[int]              =						EncodecConfig()

			if model_name == "encodec_24khz":
						pass  # config is already correct
			elif model_name == "encodec_32khz":
						A							:					Union[str, Any]              =						[8, 5, 4, 4]
						A							:					Tuple              =						[2.2]
						A							:					int              =						64
						A							:					List[Any]              =						3_2000
						A							:					Optional[int]              =						2048
						A							:					Optional[Any]              =						False
						A							:					int              =						False
						A							:					int              =						False
			elif model_name == "encodec_48khz":
						A							:					Any              =						[8, 5, 4, 2]
						A							:					str              =						[3.0, 6.0, 12.0, 24.0]
						A							:					Dict              =						4_8000
						A							:					Optional[int]              =						2
						A							:					Union[str, Any]              =						False
						A							:					str              =						"time_group_norm"
						A							:					int              =						True
						A							:					Optional[Any]              =						1.0
						A							:					Union[str, Any]              =						0.01
			else:
						raise ValueError(f"""Unknown model name: {model_name}"""    )

			A							:					Dict              =						EncodecModel(_A    )

			A							:					Dict              =						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    )

			A							:					Dict              =						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
						A							:					List[str]              =						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__":
							__SCREAMING_SNAKE_CASE           =  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."""
							)

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




from collections.abc import Sequence




def 	UpperCAmelCase   (						_lowerCamelCase = None    ):
			if nums is None or not nums:
						raise ValueError("Input sequence should not be empty"    )

			A							:					Dict              =						nums[0]
			for i in range(1 ,    len(_lowerCamelCase    )    ):
						A							:					Tuple              =						nums[i]
						A							:					List[Any]              =						max(_lowerCamelCase ,    ans + num ,    _lowerCamelCase    )

			return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							__SCREAMING_SNAKE_CASE           =  int(input("""Enter number of elements : """).strip())
							__SCREAMING_SNAKE_CASE           =  list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
							print(max_subsequence_sum(array))