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('''.''' ):
										_A								= getattr(a__					,			a__ )

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







					_A								= []

					if model_name == "encodec_24khz" or "encodec_32khz":
										_A								= MAPPING_24K
					elif model_name == "encodec_48khz":
										_A								= 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								= False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				_A							,_A								= key.split('''.*.''' )
																				if prefix in name and suffix in name:
																									_A								= 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								= True
																				if "*" in mapped_key:
																									_A								= name.split(a__ )[0].split('''.''' )[-2]
																									_A								= mapped_key.replace('''*'''					,			a__ )
																				if "weight_g" in name:
																									_A								= '''weight_g'''
																				elif "weight_v" in name:
																									_A								= '''weight_v'''
																				elif "weight_ih_l0" in name:
																									_A								= '''weight_ih_l0'''
																				elif "weight_hh_l0" in name:
																									_A								= '''weight_hh_l0'''
																				elif "bias_ih_l0" in name:
																									_A								= '''bias_ih_l0'''
																				elif "bias_hh_l0" in name:
																									_A								= '''bias_hh_l0'''
																				elif "weight_ih_l1" in name:
																									_A								= '''weight_ih_l1'''
																				elif "weight_hh_l1" in name:
																									_A								= '''weight_hh_l1'''
																				elif "bias_ih_l1" in name:
																									_A								= '''bias_ih_l1'''
																				elif "bias_hh_l1" in name:
																									_A								= '''bias_hh_l1'''
																				elif "bias" in name:
																									_A								= '''bias'''
																				elif "weight" in name:
																									_A								= '''weight'''
																				elif "running_mean" in name:
																									_A								= '''running_mean'''
																				elif "running_var" in name:
																									_A								= '''running_var'''
																				elif "num_batches_tracked" in name:
																									_A								= '''num_batches_tracked'''
																				else:
																									_A								= 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      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase=None					,			_lowercase=None					,			):

					'''simple docstring'''







					if config_path is not None:
										_A								= EncodecConfig.from_pretrained(a__ )
					else:
										_A								= EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										_A								= [8, 5, 4, 4]
										_A								= [2.2]
										_A								= 64
										_A								= 3_20_00
										_A								= 20_48
										_A								= False
										_A								= False
										_A								= False
					elif model_name == "encodec_48khz":
										_A								= [8, 5, 4, 2]
										_A								= [3.0, 6.0, 12.0, 24.0]
										_A								= 4_80_00
										_A								= 2
										_A								= False
										_A								= '''time_group_norm'''
										_A								= True
										_A								= 1.0
										_A								= 0.01
					else:
										raise ValueError(f"""Unknown model name: {model_name}""" )

					_A								= EncodecModel(a__ )

					_A								= 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								= 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								= 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__":
				__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 os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__A      =				'\\n    Text data.\n    Second line of data.'

__A      =				'file'




@pytest.fixture(scope='''session''' )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
					_A								= bytes(_lowercase					,			'''utf-8''' )
					with zstd.open(_lowercase					,			'''wb''' ) as f:
										f.write(_lowercase )
					return path




@pytest.fixture
def      __A					(     _lowercase ):

					'''simple docstring'''







					with open(os.path.join(tmpfs.local_root_dir					,			_lowercase )					,			'''w''' ) as f:
										f.write(_lowercase )
					return FILE_PATH




@pytest.mark.parametrize('''compression_format'''					,			['''gzip''', '''xz''', '''zstd'''] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
					_A								= input_paths[compression_format]
					_A								= tmp_path / '''cache'''
					_A								= DownloadConfig(cache_dir=_lowercase					,			extract_compressed_file=_lowercase )
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					with open(_lowercase ) as f:
										_A								= f.read()
					with open(_lowercase ) as f:
										_A								= f.read()
					assert extracted_file_content == expected_file_content




@pytest.mark.parametrize('''default_extracted'''					,			[True, False] )
@pytest.mark.parametrize('''default_cache_dir'''					,			[True, False] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= '''custom_cache'''
					_A								= '''custom_extracted_dir'''
					_A								= tmp_path / '''custom_extracted_path'''
					if default_extracted:
										_A								= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
					else:
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''					,			_lowercase )
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''					,			str(_lowercase ) )
										_A								= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

					_A								= xz_file
					_A								= (
					    DownloadConfig(extract_compressed_file=_lowercase )
					    if default_cache_dir
					    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir					,			extract_compressed_file=_lowercase )
					)
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					assert Path(_lowercase ).parent.parts[-2:] == expected




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(Path(_lowercase ).resolve() )
					assert cached_path(_lowercase ) == text_file
					# relative path
					_A								= str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
					assert cached_path(_lowercase ) == text_file




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(tmp_path.resolve() / '''__missing_file__.txt''' )
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )
					# relative path
					_A								= '''./__missing_file__.txt'''
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= get_from_cache(f"""tmp://{tmpfs_file}""" )
					with open(_lowercase ) as f:
										_A								= f.read()
					assert output_file_content == FILE_CONTENT




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     ):

					'''simple docstring'''







					with pytest.raises(_lowercase ):
										cached_path('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										http_get('''https://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										http_head('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										ftp_get('''ftp://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										ftp_head('''ftp://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										fsspec_get('''s3://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										fsspec_head('''s3://huggingface.co''' )



