


"""simple docstring"""
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							:      int						=      logging.get_logger('transformers.models.encodec')

__SCREAMING_SNAKE_CASE							:      str						=      {
    '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							:      Union[str, 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',
}
__SCREAMING_SNAKE_CASE							:      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',
}
__SCREAMING_SNAKE_CASE							:      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',
}
__SCREAMING_SNAKE_CASE							:      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',
}
__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__SCREAMING_SNAKE_CASE							:      Tuple						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      []
__SCREAMING_SNAKE_CASE							:      str						=      []







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							for attribute in key.split("""."""			):
														snake_case_          =						getattr(lowercase__			,	lowercase__			)

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

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







def   _a			( _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:
																					snake_case_    ,  snake_case_          =						key.split(""".*."""			)
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							snake_case_          =						[]

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

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

														snake_case_          =						False
														for key, mapped_key in MAPPING.items():
																					if "*" in key:
																												snake_case_    ,  snake_case_          =						key.split(""".*."""			)
																												if prefix in name and suffix in name:
																																			snake_case_          =						suffix

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

																												snake_case_          =						True
																												if "*" in mapped_key:
																																			snake_case_          =						name.split(lowercase__			)[0].split("""."""			)[-2]
																																			snake_case_          =						mapped_key.replace("""*"""			,	lowercase__			)
																												if "weight_g" in name:
																																			snake_case_          =						"""weight_g"""
																												elif "weight_v" in name:
																																			snake_case_          =						"""weight_v"""
																												elif "weight_ih_l0" in name:
																																			snake_case_          =						"""weight_ih_l0"""
																												elif "weight_hh_l0" in name:
																																			snake_case_          =						"""weight_hh_l0"""
																												elif "bias_ih_l0" in name:
																																			snake_case_          =						"""bias_ih_l0"""
																												elif "bias_hh_l0" in name:
																																			snake_case_          =						"""bias_hh_l0"""
																												elif "weight_ih_l1" in name:
																																			snake_case_          =						"""weight_ih_l1"""
																												elif "weight_hh_l1" in name:
																																			snake_case_          =						"""weight_hh_l1"""
																												elif "bias_ih_l1" in name:
																																			snake_case_          =						"""bias_ih_l1"""
																												elif "bias_hh_l1" in name:
																																			snake_case_          =						"""bias_hh_l1"""
																												elif "bias" in name:
																																			snake_case_          =						"""bias"""
																												elif "weight" in name:
																																			snake_case_          =						"""weight"""
																												elif "running_mean" in name:
																																			snake_case_          =						"""running_mean"""
																												elif "running_var" in name:
																																			snake_case_          =						"""running_var"""
																												elif "num_batches_tracked" in name:
																																			snake_case_          =						"""num_batches_tracked"""
																												else:
																																			snake_case_          =						None
																												set_recursively(lowercase__			,	lowercase__			,	lowercase__			,	lowercase__			,	lowercase__			)
																					continue
														if not is_used:
																					unused_weights.append(lowercase__			)

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







@torch.no_grad()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=None			,	_SCREAMING_SNAKE_CASE=None			,	)      ->			int:
							if config_path is not None:
														snake_case_          =						EncodecConfig.from_pretrained(lowercase__			)
							else:
														snake_case_          =						EncodecConfig()

							if model_name == "encodec_24khz":
														pass  # config is already correct
							elif model_name == "encodec_32khz":
														snake_case_          =						[8, 5, 4, 4]
														snake_case_          =						[2.2]
														snake_case_          =						64
														snake_case_          =						32_000
														snake_case_          =						2_048
														snake_case_          =						False
														snake_case_          =						False
														snake_case_          =						False
							elif model_name == "encodec_48khz":
														snake_case_          =						[8, 5, 4, 2]
														snake_case_          =						[3.0, 6.0, 12.0, 24.0]
														snake_case_          =						48_000
														snake_case_          =						2
														snake_case_          =						False
														snake_case_          =						"""time_group_norm"""
														snake_case_          =						True
														snake_case_          =						1.0
														snake_case_          =						0.01
							else:
														raise ValueError(f"""Unknown model name: {model_name}"""			)

							snake_case_          =						EncodecModel(lowercase__			)

							snake_case_          =						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(lowercase__			)

							snake_case_          =						torch.load(lowercase__			)
							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
														snake_case_          =						original_checkpoint["""best_state"""]
							recursively_load_weights(lowercase__			,	lowercase__			,	lowercase__			)
							model.save_pretrained(lowercase__			)

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


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







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





print(fmt.format(*covid_stats()))



