

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									=  logging.get_logger('transformers.models.encodec')

A									=  {
    '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									=  {
    '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									=  {
    '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									=  {
    '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									=  {
    '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									=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
A									=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
A									=  []
A									=  []





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	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(lowercase__      ,			lowercase__						):





	'''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 a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''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(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(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__=None      ,			lowercase__=None      ,			):





	'''simple docstring'''
	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_   		=			32000
		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_   		=			48000
		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__":
		A									=  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									=  parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)



import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script


@require_cpu
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				debug_launcher(test_script.main				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				debug_launcher(test_ops.main				)
