




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    :						Optional[Any]						      =       logging.get_logger('''transformers.models.encodec''')

__a    :						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    :						Dict						      =       {
    '''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    :						Any						      =       {
    '''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    :						List[str]						      =       {
    '''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    :						Any						      =       {
    '''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    :						List[Any]						      =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__a    :						List[Any]						      =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__a    :						List[Any]						      =       []
__a    :						List[Any]						      =       []
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					List[str]:






							for attribute in key.split("."							):
														lowercase__    :			Optional[int]     =				getattr(snake_case__							,snake_case__							)

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

							logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}."""							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					Union[str, Any]:






							for key in ignore_keys:
														if key.endswith(".*"							):
																					if name.startswith(key[:-1]							):
																												return True
														elif ".*." in key:
																					lowercase__    :			Optional[int]     =				key.split(".*."							)
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					Dict:
							lowercase__    :			Any     =				[]

							if model_name == "encodec_24khz" or "encodec_32khz":
														lowercase__    :			Tuple     =				MAPPING_24K
							elif model_name == "encodec_48khz":
														lowercase__    :			Tuple     =				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

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

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






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

							if model_name == "encodec_24khz":
														pass  # config is already correct
							elif model_name == "encodec_32khz":
														lowercase__    :			Optional[int]     =				[8, 5, 4, 4]
														lowercase__    :			Optional[Any]     =				[2.2]
														lowercase__    :			int     =				64
														lowercase__    :			List[Any]     =				3_20_00
														lowercase__    :			str     =				20_48
														lowercase__    :			Union[str, Any]     =				False
														lowercase__    :			Any     =				False
														lowercase__    :			Tuple     =				False
							elif model_name == "encodec_48khz":
														lowercase__    :			Tuple     =				[8, 5, 4, 2]
														lowercase__    :			Any     =				[3.0, 6.0, 12.0, 24.0]
														lowercase__    :			Union[str, Any]     =				4_80_00
														lowercase__    :			Any     =				2
														lowercase__    :			Any     =				False
														lowercase__    :			Dict     =				"""time_group_norm"""
														lowercase__    :			Optional[Any]     =				True
														lowercase__    :			Optional[int]     =				1.0
														lowercase__    :			Union[str, Any]     =				0.01
							else:
														raise ValueError(F"""Unknown model name: {model_name}"""							)

							lowercase__    :			str     =				EncodecModel(snake_case__							)

							lowercase__    :			List[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(snake_case__							)

							lowercase__    :			Dict     =				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
														lowercase__    :			Optional[Any]     =				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    :						Optional[int]						      =       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,
						)







def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     if digit_amount > 0:
          return round(number - int(snake_case__	)						,snake_case__	)
     return number - int(snake_case__	)


if __name__ == "__main__":
     print(decimal_isolate(1.5_3, 0))
     print(decimal_isolate(3_5.3_4_5, 1))
     print(decimal_isolate(3_5.3_4_5, 2))
     print(decimal_isolate(3_5.3_4_5, 3))
     print(decimal_isolate(-1_4.7_8_9, 3))
     print(decimal_isolate(0, 2))
     print(decimal_isolate(-1_4.1_2_3, 1))
     print(decimal_isolate(-1_4.1_2_3, 2))
     print(decimal_isolate(-1_4.1_2_3, 3))


