






import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

__snake_case    :    Dict         =     """bert-base-cased"""
__snake_case    :    List[str]         =     """fp16"""
__snake_case    :    Dict         =     """bf16"""
__snake_case    :    List[Any]         =     [FPaa, BFaa]


@require_fsdp
@require_cuda
class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().setUp()

												lowerCAmelCase__    						=						dict(
												    ACCELERATE_USE_FSDP='true'     ,						MASTER_ADDR='localhost'     ,						MASTER_PORT='10999'     ,						RANK='0'     ,						LOCAL_RANK='0'     ,						WORLD_SIZE='1'     ,						)






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

												for i, strategy in enumerate(_UpperCamelCase   ):
																		lowerCAmelCase__    						=						self.dist_env.copy()
																		lowerCAmelCase__    						=						F"{i + 1}"
																		lowerCAmelCase__    						=						strategy
																		with mockenv_context(**_UpperCamelCase   ):
																								lowerCAmelCase__    						=						FullyShardedDataParallelPlugin()
																								self.assertEqual(fsdp_plugin.sharding_strategy     ,						ShardingStrategy(i + 1   )   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

												for i, prefetch_policy in enumerate(_UpperCamelCase   ):
																		lowerCAmelCase__    						=						self.dist_env.copy()
																		lowerCAmelCase__    						=						prefetch_policy
																		with mockenv_context(**_UpperCamelCase   ):
																								lowerCAmelCase__    						=						FullyShardedDataParallelPlugin()
																								if prefetch_policy == "NO_PREFETCH":
																														self.assertIsNone(fsdp_plugin.backward_prefetch   )
																								else:
																														self.assertEqual(fsdp_plugin.backward_prefetch     ,						BackwardPrefetch(i + 1   )   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

												for i, state_dict_type in enumerate(_UpperCamelCase   ):
																		lowerCAmelCase__    						=						self.dist_env.copy()
																		lowerCAmelCase__    						=						state_dict_type
																		with mockenv_context(**_UpperCamelCase   ):
																								lowerCAmelCase__    						=						FullyShardedDataParallelPlugin()
																								self.assertEqual(fsdp_plugin.state_dict_type     ,						StateDictType(i + 1   )   )
																								if state_dict_type == "FULL_STATE_DICT":
																														self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu   )
																														self.assertTrue(fsdp_plugin.state_dict_config.ranka_only   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						AutoModel.from_pretrained(_UpperCamelCase   )
												for policy in FSDP_AUTO_WRAP_POLICY:
																		lowerCAmelCase__    						=						self.dist_env.copy()
																		lowerCAmelCase__    						=						policy
																		if policy == "TRANSFORMER_BASED_WRAP":
																								lowerCAmelCase__    						=						'BertLayer'
																		elif policy == "SIZE_BASED_WRAP":
																								lowerCAmelCase__    						=						'2000'
																		with mockenv_context(**_UpperCamelCase   ):
																								lowerCAmelCase__    						=						FullyShardedDataParallelPlugin()
																								fsdp_plugin.set_auto_wrap_policy(_UpperCamelCase   )
																								if policy == "NO_WRAP":
																														self.assertIsNone(fsdp_plugin.auto_wrap_policy   )
																								else:
																														self.assertIsNotNone(fsdp_plugin.auto_wrap_policy   )

												lowerCAmelCase__    						=						self.dist_env.copy()
												lowerCAmelCase__    						=						'TRANSFORMER_BASED_WRAP'
												lowerCAmelCase__    						=						'T5Layer'
												with mockenv_context(**_UpperCamelCase   ):
																		lowerCAmelCase__    						=						FullyShardedDataParallelPlugin()
																		with self.assertRaises(_UpperCamelCase   ) as cm:
																								fsdp_plugin.set_auto_wrap_policy(_UpperCamelCase   )
																		self.assertTrue('Could not find the transformer layer class to wrap in the model.' in str(cm.exception   )   )

												lowerCAmelCase__    						=						self.dist_env.copy()
												lowerCAmelCase__    						=						'SIZE_BASED_WRAP'
												lowerCAmelCase__    						=						'0'
												with mockenv_context(**_UpperCamelCase   ):
																		lowerCAmelCase__    						=						FullyShardedDataParallelPlugin()
																		fsdp_plugin.set_auto_wrap_policy(_UpperCamelCase   )
																		self.assertIsNone(fsdp_plugin.auto_wrap_policy   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
												from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

												for mp_dtype in dtypes:
																		lowerCAmelCase__    						=						self.dist_env.copy()
																		lowerCAmelCase__    						=						mp_dtype
																		with mockenv_context(**_UpperCamelCase   ):
																								lowerCAmelCase__    						=						Accelerator()
																								if mp_dtype == "fp16":
																														lowerCAmelCase__    						=						torch.floataa
																								elif mp_dtype == "bf16":
																														lowerCAmelCase__    						=						torch.bfloataa
																								lowerCAmelCase__    						=						MixedPrecision(param_dtype=_UpperCamelCase     ,						reduce_dtype=_UpperCamelCase     ,						buffer_dtype=_UpperCamelCase   )
																								self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy     ,						_UpperCamelCase   )
																								if mp_dtype == FPaa:
																														self.assertTrue(isinstance(accelerator.scaler     ,						_UpperCamelCase   )   )
																								elif mp_dtype == BFaa:
																														self.assertIsNone(accelerator.scaler   )
																								AcceleratorState._reset_state(_UpperCamelCase   )







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

												for flag in [True, False]:
																		lowerCAmelCase__    						=						self.dist_env.copy()
																		lowerCAmelCase__    						=						str(_UpperCamelCase   ).lower()
																		with mockenv_context(**_UpperCamelCase   ):
																								lowerCAmelCase__    						=						FullyShardedDataParallelPlugin()
																								self.assertEqual(fsdp_plugin.cpu_offload     ,						CPUOffload(offload_params=_UpperCamelCase   )   )




@require_fsdp
@require_multi_gpu
@slow
class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().setUp()
												lowerCAmelCase__    						=						0.82
												lowerCAmelCase__    						=						[
												    'fsdp_shard_grad_op_transformer_based_wrap',
												    'fsdp_full_shard_transformer_based_wrap',
												]
												lowerCAmelCase__    						=						{
												    'multi_gpu_fp16': 32_00,
												    'fsdp_shard_grad_op_transformer_based_wrap_fp16': 20_00,
												    'fsdp_full_shard_transformer_based_wrap_fp16': 19_00,
												    # Disabling below test as it overwhelms the RAM memory usage
												    # on CI self-hosted runner leading to tests getting killed.
												    # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
												}
												lowerCAmelCase__    						=						1_60
												lowerCAmelCase__    						=						1_60

												lowerCAmelCase__    						=						inspect.getfile(accelerate.test_utils   )
												lowerCAmelCase__    						=						os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['scripts', 'external_deps']   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						os.path.join(self.test_scripts_folder     ,						'test_performance.py'   )
												lowerCAmelCase__    						=						['accelerate', 'launch', '--num_processes=2', '--num_machines=1', '--machine_rank=0', '--use_fsdp']
												for config in self.performance_configs:
																		lowerCAmelCase__    						=						cmd.copy()
																		for i, strategy in enumerate(_UpperCamelCase   ):
																								if strategy.lower() in config:
																														cmd_config.append(F"--fsdp_sharding_strategy={i+1}"   )
																														break

																		if "fp32" in config:
																								cmd_config.append('--mixed_precision=no'   )
																		else:
																								cmd_config.append('--mixed_precision=fp16'   )

																		if "cpu_offload" in config:
																								cmd_config.append('--fsdp_offload_params=True'   )

																		for policy in FSDP_AUTO_WRAP_POLICY:
																								if policy.lower() in config:
																														cmd_config.append(F"--fsdp_auto_wrap_policy={policy}"   )
																														break

																		if policy == "TRANSFORMER_BASED_WRAP":
																								cmd_config.append('--fsdp_transformer_layer_cls_to_wrap=BertLayer'   )
																		elif policy == "SIZE_BASED_WRAP":
																								cmd_config.append('--fsdp_min_num_params=2000'   )

																		cmd_config.extend(
																		    [
																		        self.test_file_path,
																		        F"--output_dir={self.tmpdir}",
																		        F"--performance_lower_bound={self.performance_lower_bound}",
																		    ]   )
																		with patch_environment(omp_num_threads=1   ):
																								execute_subprocess_async(_UpperCamelCase     ,						env=os.environ.copy()   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						os.path.join(self.test_scripts_folder     ,						'test_checkpointing.py'   )
												lowerCAmelCase__    						=						[
												    'accelerate',
												    'launch',
												    '--num_processes=2',
												    '--num_machines=1',
												    '--machine_rank=0',
												    '--use_fsdp',
												    '--mixed_precision=fp16',
												    '--fsdp_transformer_layer_cls_to_wrap=BertLayer',
												]

												for i, strategy in enumerate(_UpperCamelCase   ):
																		lowerCAmelCase__    						=						cmd.copy()
																		cmd_config.append(F"--fsdp_sharding_strategy={i+1}"   )
																		if strategy != "FULL_SHARD":
																								continue
																		lowerCAmelCase__    						=						len(_UpperCamelCase   )
																		for state_dict_type in FSDP_STATE_DICT_TYPE:
																								lowerCAmelCase__    						=						cmd_config[:state_dict_config_index]
																								cmd_config.append(F"--fsdp_state_dict_type={state_dict_type}"   )
																								cmd_config.extend(
																								    [
																								        self.test_file_path,
																								        F"--output_dir={self.tmpdir}",
																								        '--partial_train_epoch=1',
																								    ]   )
																								with patch_environment(omp_num_threads=1   ):
																														execute_subprocess_async(_UpperCamelCase     ,						env=os.environ.copy()   )

																								lowerCAmelCase__    						=						cmd_config[:-1]
																								lowerCAmelCase__    						=						os.path.join(self.tmpdir     ,						'epoch_0'   )
																								cmd_config.extend(
																								    [
																								        F"--resume_from_checkpoint={resume_from_checkpoint}",
																								    ]   )
																								with patch_environment(omp_num_threads=1   ):
																														execute_subprocess_async(_UpperCamelCase     ,						env=os.environ.copy()   )







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						os.path.join(self.test_scripts_folder     ,						'test_peak_memory_usage.py'   )
												lowerCAmelCase__    						=						[
												    'accelerate',
												    'launch',
												    '--num_processes=2',
												    '--num_machines=1',
												    '--machine_rank=0',
												]
												for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
																		lowerCAmelCase__    						=						cmd.copy()
																		if "fp16" in spec:
																								cmd_config.extend(['--mixed_precision=fp16']   )
																		else:
																								cmd_config.extend(['--mixed_precision=no']   )

																		if "multi_gpu" in spec:
																								continue
																		else:
																								cmd_config.extend(['--use_fsdp']   )
																								for i, strategy in enumerate(_UpperCamelCase   ):
																														if strategy.lower() in spec:
																																				cmd_config.append(F"--fsdp_sharding_strategy={i+1}"   )
																																				break

																								if "cpu_offload" in spec:
																														cmd_config.append('--fsdp_offload_params=True'   )

																								for policy in FSDP_AUTO_WRAP_POLICY:
																														if policy.lower() in spec:
																																				cmd_config.append(F"--fsdp_auto_wrap_policy={policy}"   )
																																				break

																								if policy == "TRANSFORMER_BASED_WRAP":
																														cmd_config.append('--fsdp_transformer_layer_cls_to_wrap=BertLayer'   )
																								elif policy == "SIZE_BASED_WRAP":
																														cmd_config.append('--fsdp_min_num_params=2000'   )

																		cmd_config.extend(
																		    [
																		        self.test_file_path,
																		        F"--output_dir={self.tmpdir}",
																		        F"--peak_memory_upper_bound={peak_mem_upper_bound}",
																		        F"--n_train={self.n_train}",
																		        F"--n_val={self.n_val}",
																		    ]   )
																		with patch_environment(omp_num_threads=1   ):
																								execute_subprocess_async(_UpperCamelCase     ,						env=os.environ.copy()   )













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()
__snake_case    :    int         =     logging.get_logger("""transformers.models.encodec""")

__snake_case    :    Tuple         =     {
    """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""",
}
__snake_case    :    List[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""",
}
__snake_case    :    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""",
}
__snake_case    :    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""",
}
__snake_case    :    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""",
}
__snake_case    :    int         =     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case    :    int         =     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case    :    Union[str, Any]         =     []
__snake_case    :    Tuple         =     []







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Optional[int]		)  ->   List[str]:

						"""simple docstring"""



						for attribute in key.split('.'		):
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       UpperCamelCase_		)

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

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[str]		)  ->   Tuple:

						"""simple docstring"""



						for key in ignore_keys:
												if key.endswith('.*'		):
																		if name.startswith(key[:-1]		):
																								return True
												elif ".*." in key:
																		lowerCAmelCase__						,       lowerCAmelCase__    						=						key.split('.*.'		)
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					str		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]

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

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

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

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

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

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







@torch.no_grad()
def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Optional[int]=None    ,       UpperCamelCase_       :					List[Any]=None    ,       )  ->   Dict:

						"""simple docstring"""



						if config_path is not None:
												lowerCAmelCase__    						=						EncodecConfig.from_pretrained(UpperCamelCase_		)
						else:
												lowerCAmelCase__    						=						EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												lowerCAmelCase__    						=						[8, 5, 4, 4]
												lowerCAmelCase__    						=						[2.2]
												lowerCAmelCase__    						=						64
												lowerCAmelCase__    						=						3_2000
												lowerCAmelCase__    						=						2048
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						False
						elif model_name == "encodec_48khz":
												lowerCAmelCase__    						=						[8, 5, 4, 2]
												lowerCAmelCase__    						=						[3.0, 6.0, 12.0, 24.0]
												lowerCAmelCase__    						=						4_8000
												lowerCAmelCase__    						=						2
												lowerCAmelCase__    						=						False
												lowerCAmelCase__    						=						'time_group_norm'
												lowerCAmelCase__    						=						True
												lowerCAmelCase__    						=						1.0
												lowerCAmelCase__    						=						0.01
						else:
												raise ValueError(F"Unknown model name: {model_name}"		)

						lowerCAmelCase__    						=						EncodecModel(UpperCamelCase_		)

						lowerCAmelCase__    						=						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(UpperCamelCase_		)

						lowerCAmelCase__    						=						torch.load(UpperCamelCase_		)
						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
												lowerCAmelCase__    						=						original_checkpoint['best_state']
						recursively_load_weights(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
						model.save_pretrained(UpperCamelCase_		)

						if repo_id:
												print('Pushing to the hub...'		)
												feature_extractor.push_to_hub(UpperCamelCase_		)
												model.push_to_hub(UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    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."""
					)

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





