

import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu







class 					__lowercase  (						nn.Module				):





							"""simple docstring"""

							def __init__(					self						)		->       str:
														super().__init__()
														A      :					Dict      		=					nn.Linear(3    ,						4						)
														A      :					Optional[Any]      		=					nn.BatchNormad(4						)
														A      :					Optional[Any]      		=					nn.Linear(4    ,						5						)






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Any:
														return self.lineara(self.batchnorm(self.lineara(__UpperCAmelCase						)						)						)







class 					__lowercase  (						_UpperCamelCase				):





							"""simple docstring"""

							def       snake_case			(					self    ,						__UpperCAmelCase    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
														return (args[0] + 1,) + args[1:], kwargs







class 					__lowercase  (						_UpperCamelCase				):





							"""simple docstring"""

							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Tuple:
														return output + 1







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Any:
														A      :					int      		=					ModelForTest()
														A      :					List[Any]      		=					ModelHook()

														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)
														self.assertEqual(test_model._hf_hook    ,						__UpperCAmelCase						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''_old_forward'''						)						)

														# Check adding the hook did not change the name or the signature
														self.assertEqual(test_model.forward.__name__    ,						'''forward'''						)
														self.assertListEqual(list(inspect.signature(test_model.forward						).parameters						)    ,						['''x''']						)

														remove_hook_from_module(__UpperCAmelCase						)
														self.assertFalse(hasattr(__UpperCAmelCase    ,						'''_hf_hook'''						)						)
														self.assertFalse(hasattr(__UpperCAmelCase    ,						'''_old_forward'''						)						)




							def       snake_case			(					self						)		->       Any:
														A      :					List[str]      		=					ModelForTest()
														A      :					List[str]      		=					ModelHook()

														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase    ,						append=__UpperCAmelCase						)

														self.assertEqual(isinstance(test_model._hf_hook    ,						__UpperCAmelCase						)    ,						__UpperCAmelCase						)
														self.assertEqual(len(test_model._hf_hook.hooks						)    ,						2						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''_old_forward'''						)						)

														# Check adding the hook did not change the name or the signature
														self.assertEqual(test_model.forward.__name__    ,						'''forward'''						)
														self.assertListEqual(list(inspect.signature(test_model.forward						).parameters						)    ,						['''x''']						)

														remove_hook_from_module(__UpperCAmelCase						)
														self.assertFalse(hasattr(__UpperCAmelCase    ,						'''_hf_hook'''						)						)
														self.assertFalse(hasattr(__UpperCAmelCase    ,						'''_old_forward'''						)						)




							def       snake_case			(					self						)		->       List[Any]:
														A      :					str      		=					ModelForTest()
														A      :					int      		=					torch.randn(2    ,						3						)
														A      :					int      		=					test_model(x + 1						)
														A      :					List[Any]      		=					test_model(x + 2						)

														A      :					Tuple      		=					PreForwardHook()
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)
														A      :					Dict      		=					test_model(__UpperCAmelCase						)
														self.assertTrue(torch.allclose(__UpperCAmelCase    ,						__UpperCAmelCase    ,						atol=1E-5						)						)

														# Attaching a hook to a model when it already has one replaces, does not chain
														A      :					str      		=					PreForwardHook()
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					test_model(__UpperCAmelCase						)
														self.assertTrue(torch.allclose(__UpperCAmelCase    ,						__UpperCAmelCase    ,						atol=1E-5						)						)

														# You need to use the sequential hook to chain two or more hooks
														A      :					Dict      		=					SequentialHook(PreForwardHook()    ,						PreForwardHook()						)
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					test_model(__UpperCAmelCase						)
														assert torch.allclose(__UpperCAmelCase    ,						__UpperCAmelCase    ,						atol=1E-5						)




							def       snake_case			(					self						)		->       Dict:
														A      :					Union[str, Any]      		=					ModelForTest()
														A      :					Any      		=					torch.randn(2    ,						3						)
														A      :					Dict      		=					test_model(__UpperCAmelCase						)

														A      :					Dict      		=					PostForwardHook()
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)
														A      :					Optional[Any]      		=					test_model(__UpperCAmelCase						)
														self.assertTrue(torch.allclose(__UpperCAmelCase    ,						output + 1    ,						atol=1E-5						)						)

														# Attaching a hook to a model when it already has one replaces, does not chain
														A      :					Dict      		=					PostForwardHook()
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)
														A      :					int      		=					test_model(__UpperCAmelCase						)
														self.assertTrue(torch.allclose(__UpperCAmelCase    ,						output + 1    ,						atol=1E-5						)						)

														# You need to use the sequential hook to chain two or more hooks
														A      :					Optional[int]      		=					SequentialHook(PostForwardHook()    ,						PostForwardHook()						)
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)

														A      :					Optional[int]      		=					test_model(__UpperCAmelCase						)
														assert torch.allclose(__UpperCAmelCase    ,						output + 2    ,						atol=1E-5						)




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Optional[int]      		=					ModelForTest()
														A      :					str      		=					torch.randn(2    ,						3						)
														A      :					List[Any]      		=					test_model(__UpperCAmelCase						)

														A      :					Tuple      		=					PostForwardHook()
														add_hook_to_module(__UpperCAmelCase    ,						__UpperCAmelCase						)
														A      :					int      		=					test_model(__UpperCAmelCase						)
														self.assertTrue(torch.allclose(__UpperCAmelCase    ,						output + 1						)						)
														self.assertTrue(outputa.requires_grad						)

														A      :					Any      		=					True
														A      :					Any      		=					test_model(__UpperCAmelCase						)
														self.assertFalse(outputa.requires_grad						)




							@require_multi_gpu
							def       snake_case			(					self						)		->       str:
														A      :					Optional[Any]      		=					ModelForTest()
														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)

														# This will move each submodule on different devices
														add_hook_to_module(model.lineara    ,						AlignDevicesHook(execution_device=0						)						)
														add_hook_to_module(model.batchnorm    ,						AlignDevicesHook(execution_device=0						)						)
														add_hook_to_module(model.lineara    ,						AlignDevicesHook(execution_device=1						)						)

														self.assertEqual(model.lineara.weight.device    ,						torch.device(0						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device(0						)						)
														self.assertEqual(model.batchnorm.running_mean.device    ,						torch.device(0						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device(1						)						)

														# We can still make a forward pass. The input does not need to be on any particular device
														A      :					Dict      		=					torch.randn(2    ,						3						)
														A      :					Optional[Any]      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						torch.device(1						)						)

														# We can add a general hook to put back output on same device as input.
														add_hook_to_module(__UpperCAmelCase    ,						AlignDevicesHook(io_same_device=__UpperCAmelCase						)						)
														A      :					Optional[Any]      		=					torch.randn(2    ,						3						).to(0						)
														A      :					int      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						torch.device(0						)						)




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Tuple      		=					ModelForTest()

														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)

														# This will move each submodule on different devices
														A      :					int      		=					{"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

														add_hook_to_module(model.lineara    ,						AlignDevicesHook(**__UpperCAmelCase						)						)
														add_hook_to_module(model.batchnorm    ,						AlignDevicesHook(**__UpperCAmelCase						)						)
														add_hook_to_module(model.lineara    ,						AlignDevicesHook(**__UpperCAmelCase						)						)

														# Parameters have been offloaded, so on the meta device
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														# Buffers are not included in the offload by default, so are on the execution device
														A      :					str      		=					torch.device(hook_kwargs['''execution_device''']						)
														self.assertEqual(model.batchnorm.running_mean.device    ,						__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					torch.randn(2    ,						3						)
														A      :					Any      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						__UpperCAmelCase						)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_module(model.lineara						)
														remove_hook_from_module(model.batchnorm						)
														remove_hook_from_module(model.lineara						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)

														# Now test with buffers included in the offload
														A      :					Any      		=					{
														    "execution_device": 0 if torch.cuda.is_available() else "cpu",
														    "offload": True,
														    "offload_buffers": True,
														}

														add_hook_to_module(model.lineara    ,						AlignDevicesHook(**__UpperCAmelCase						)						)
														add_hook_to_module(model.batchnorm    ,						AlignDevicesHook(**__UpperCAmelCase						)						)
														add_hook_to_module(model.lineara    ,						AlignDevicesHook(**__UpperCAmelCase						)						)

														# Parameters have been offloaded, so on the meta device, buffers included
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.running_mean.device    ,						torch.device('''meta'''						)						)

														A      :					Any      		=					torch.randn(2    ,						3						)
														A      :					List[Any]      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						__UpperCAmelCase						)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_module(model.lineara						)
														remove_hook_from_module(model.batchnorm						)
														remove_hook_from_module(model.lineara						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)




							def       snake_case			(					self						)		->       Tuple:
														A      :					List[str]      		=					ModelForTest()

														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)

														# This will move each submodule on different devices
														A      :					Dict      		=					0 if torch.cuda.is_available() else "cpu"
														attach_align_device_hook(__UpperCAmelCase    ,						execution_device=__UpperCAmelCase    ,						offload=__UpperCAmelCase						)

														# Parameters have been offloaded, so on the meta device
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														# Buffers are not included in the offload by default, so are on the execution device
														A      :					Dict      		=					torch.device(__UpperCAmelCase						)
														self.assertEqual(model.batchnorm.running_mean.device    ,						__UpperCAmelCase						)

														A      :					List[str]      		=					torch.randn(2    ,						3						)
														A      :					Optional[Any]      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						__UpperCAmelCase						)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(__UpperCAmelCase						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)

														# Now test with buffers included in the offload
														attach_align_device_hook(__UpperCAmelCase    ,						execution_device=__UpperCAmelCase    ,						offload=__UpperCAmelCase    ,						offload_buffers=__UpperCAmelCase						)

														# Parameters have been offloaded, so on the meta device, buffers included
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.running_mean.device    ,						torch.device('''meta'''						)						)

														A      :					int      		=					torch.randn(2    ,						3						)
														A      :					int      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						__UpperCAmelCase						)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(__UpperCAmelCase						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)






							def       snake_case			(					self						)		->       int:
														A      :					Any      		=					ModelForTest()

														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)

														# This will move each submodule on different devices
														A      :					str      		=					0 if torch.cuda.is_available() else "cpu"
														attach_align_device_hook(
														    __UpperCAmelCase    ,						execution_device=__UpperCAmelCase    ,						offload=__UpperCAmelCase    ,						weights_map=model.state_dict()						)

														# Parameters have been offloaded, so on the meta device
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														# Buffers are not included in the offload by default, so are on the execution device
														A      :					Union[str, Any]      		=					torch.device(__UpperCAmelCase						)
														self.assertEqual(model.batchnorm.running_mean.device    ,						__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					torch.randn(2    ,						3						)
														A      :					Optional[Any]      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						__UpperCAmelCase						)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(__UpperCAmelCase						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)

														# Now test with buffers included in the offload
														attach_align_device_hook(
														    __UpperCAmelCase    ,						execution_device=__UpperCAmelCase    ,						offload=__UpperCAmelCase    ,						weights_map=model.state_dict()    ,						offload_buffers=__UpperCAmelCase    ,						)

														# Parameters have been offloaded, so on the meta device, buffers included
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''meta'''						)						)
														self.assertEqual(model.batchnorm.running_mean.device    ,						torch.device('''meta'''						)						)

														A      :					Optional[int]      		=					torch.randn(2    ,						3						)
														A      :					Dict      		=					model(__UpperCAmelCase						)
														self.assertEqual(output.device    ,						__UpperCAmelCase						)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(__UpperCAmelCase						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.batchnorm.weight.device    ,						torch.device('''cpu'''						)						)
														self.assertEqual(model.lineara.weight.device    ,						torch.device('''cpu'''						)						)











"""simple docstring"""




import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase							:   Optional[Any]				=				logging.get_logger(__name__)


_lowerCAmelCase							:   List[str]				=				["model.decoder.embed_positions.weights"]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     if "emb" in name:
          _UpperCAmelCase     :      int														=       name.replace("emb"		,    "model.decoder.embed_tokens"							)
     if "transformer" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("transformer"		,    "model.decoder"							)
     if "cross_attention" in name:
          _UpperCAmelCase     :      Any														=       name.replace("cross_attention"		,    "encoder_attn"							)
     if "linear1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("linear1"		,    "fc1"							)
     if "linear2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("linear2"		,    "fc2"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "self_attn_layer_norm"							)
     if "norm_cross" in name:
          _UpperCAmelCase     :      List[Any]														=       name.replace("norm_cross"		,    "encoder_attn_layer_norm"							)
     if "norm2" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("norm2"		,    "final_layer_norm"							)
     if "out_norm" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("out_norm"		,    "model.decoder.layer_norm"							)
     if "linears" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("linears"		,    "lm_heads"							)
     if "condition_provider.conditioners.description.output_proj" in name:
          _UpperCAmelCase     :      Optional[Any]														=       name.replace("condition_provider.conditioners.description.output_proj"		,    "enc_to_dec_proj"							)
     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		OrderedDict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Tuple[Dict, Dict]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       list(state_dict.keys()							)
     _UpperCAmelCase     :      Dict														=       {}
     for key in keys:
          _UpperCAmelCase     :      Optional[Any]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      Any														=       rename_keys(SCREAMING_SNAKE_CASE__							)
          if "in_proj_weight" in key:
               # split fused qkv proj
               _UpperCAmelCase     :      Optional[int]														=       val[:hidden_size, :]
               _UpperCAmelCase     :      str														=       val[hidden_size : 2 * hidden_size, :]
               _UpperCAmelCase     :      List[Any]														=       val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               _UpperCAmelCase     :      Union[str, Any]														=       val
          else:
               _UpperCAmelCase     :      int														=       val
     return state_dict, enc_dec_proj_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  MusicgenDecoderConfig:





     '''simple docstring'''

     if checkpoint == "small":
          # default config values
          _UpperCAmelCase     :      Union[str, Any]														=       1_024
          _UpperCAmelCase     :      Tuple														=       24
          _UpperCAmelCase     :      List[str]														=       16
     elif checkpoint == "medium":
          _UpperCAmelCase     :      List[str]														=       1_536
          _UpperCAmelCase     :      List[str]														=       48
          _UpperCAmelCase     :      str														=       24
     elif checkpoint == "large":
          _UpperCAmelCase     :      Dict														=       2_048
          _UpperCAmelCase     :      Optional[int]														=       48
          _UpperCAmelCase     :      Tuple														=       32
     else:
          raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenDecoderConfig(
         hidden_size=SCREAMING_SNAKE_CASE__		,    ffn_dim=hidden_size * 4		,    num_hidden_layers=SCREAMING_SNAKE_CASE__		,    num_attention_heads=SCREAMING_SNAKE_CASE__		,    )
     return config

@torch.no_grad()
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		int=None		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		Dict="cpu"							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       MusicGen.get_pretrained(SCREAMING_SNAKE_CASE__		,    device=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       fairseq_model.lm.state_dict()
     _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       rename_state_dict(
         SCREAMING_SNAKE_CASE__		,    hidden_size=decoder_config.hidden_size							)

     _UpperCAmelCase     :      Tuple														=       TaEncoderModel.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      List[str]														=       EncodecModel.from_pretrained("facebook/encodec_32khz"							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenForCausalLM(SCREAMING_SNAKE_CASE__							).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       decoder.load_state_dict(SCREAMING_SNAKE_CASE__		,    strict=SCREAMING_SNAKE_CASE__							)

     for key in missing_keys.copy():
          if key.startswith(("text_encoder", "audio_encoder")							) or key in EXPECTED_MISSING_KEYS:
               missing_keys.remove(SCREAMING_SNAKE_CASE__							)

     if len(SCREAMING_SNAKE_CASE__							) > 0:
          raise ValueError(f'Missing key(s) in state_dict: {missing_keys}'							)

     if len(SCREAMING_SNAKE_CASE__							) > 0:
          raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}'							)

     # init the composite model
     _UpperCAmelCase     :      Dict														=       MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE__		,    audio_encoder=SCREAMING_SNAKE_CASE__		,    decoder=SCREAMING_SNAKE_CASE__							)

     # load the pre-trained enc-dec projection (from the decoder state dict)
     model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE__							)

     # check we can do a forward pass
     _UpperCAmelCase     :      int														=       torch.arange(0		,    8		,    dtype=torch.long							).reshape(2		,    -1							)
     _UpperCAmelCase     :      List[Any]														=       input_ids.reshape(2 * 4		,    -1							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(input_ids=SCREAMING_SNAKE_CASE__		,    decoder_input_ids=SCREAMING_SNAKE_CASE__							).logits

     if logits.shape != (8, 1, 2_048):
          raise ValueError("Incorrect shape for logits"							)

     # now construct the processor
     _UpperCAmelCase     :      Optional[int]														=       AutoTokenizer.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      str														=       AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,    padding_side="left"							)

     _UpperCAmelCase     :      Optional[Any]														=       MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__							)

     # set the appropriate bos/pad token ids
     _UpperCAmelCase     :      Optional[int]														=       2_048
     _UpperCAmelCase     :      Union[str, Any]														=       2_048

     # set other default generation config params
     _UpperCAmelCase     :      Optional[int]														=       int(30 * audio_encoder.config.frame_rate							)
     _UpperCAmelCase     :      Union[str, Any]														=       True
     _UpperCAmelCase     :      Dict														=       3.0

     if pytorch_dump_folder is not None:
          Path(SCREAMING_SNAKE_CASE__							).mkdir(exist_ok=SCREAMING_SNAKE_CASE__							)
          logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)
          processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if repo_id:
          logger.info(f'Pushing model {checkpoint} to {repo_id}'							)
          model.push_to_hub(SCREAMING_SNAKE_CASE__							)
          processor.push_to_hub(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
 _lowerCAmelCase							:   int				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint",
     default="small",
     type=str,
     help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
 )
 parser.add_argument(
     "--pytorch_dump_folder",
     required=True,
     default=None,
     type=str,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument(
     "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
 )
 parser.add_argument(
     "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
 )

 _lowerCAmelCase							:   List[str]				=				parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
