


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

A				:       Optional[Any]      =  {
    '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				:       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',
}
A				:       List[str]      =  {
    '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				:       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				:       int      =  {
    '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				:       Any      =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
A				:       Optional[Any]      =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
A				:       int      =  []
A				:       int      =  []





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Optional[int]:


						'''simple docstring'''






						for attribute in key.split("."			):
												__snake_case				=		getattr(_lowerCAmelCase   ,						_lowerCAmelCase			)

						if weight_type is not None:
												__snake_case				=		getattr(_lowerCAmelCase   ,						_lowerCAmelCase			).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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[Any]:


						'''simple docstring'''






						__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(_lowerCAmelCase   ,						_lowerCAmelCase			):
																		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(_lowerCAmelCase			)[0].split("."			)[-2]
																														__snake_case				=		mapped_key.replace("*"   ,						_lowerCAmelCase			)
																								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(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
																		continue
												if not is_used:
																		unused_weights.append(_lowerCAmelCase			)

						logger.warning(F'''Unused weights: {unused_weights}'''			)





@torch.no_grad()
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=None   ,						_lowerCAmelCase=None   ,						)    ->						Any:


						'''simple docstring'''






						if config_path is not None:
												__snake_case				=		EncodecConfig.from_pretrained(_lowerCAmelCase			)
						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				=		3_2000
												__snake_case				=		2048
												__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				=		4_8000
												__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(_lowerCAmelCase			)

						__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(_lowerCAmelCase			)

						__snake_case				=		torch.load(_lowerCAmelCase			)
						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(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						model.save_pretrained(_lowerCAmelCase			)

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


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









import requests
from bsa import BeautifulSoup





def 						_lowerCAmelCase							(     _lowerCAmelCase = "AAPL"			)    ->						str:


						'''simple docstring'''






						__snake_case				=		F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
						__snake_case				=		BeautifulSoup(requests.get(_lowerCAmelCase			).text   ,						"html.parser"			)
						__snake_case				=		"My(6px) Pos(r) smartphone_Mt(6px)"
						return soup.find("div"   ,						class_=class_			).find("span"			).text


if __name__ == "__main__":
	for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
		print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')





