
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


lowercase_ 				=			logging.getLogger(__name__)


def 							a__					(							snake_case=2       ,			snake_case=3       ,			snake_case=16       ,			snake_case = 10       ,			snake_case = 2							):





  """simple docstring"""

  def get_dataset(snake_case							):
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		torch.randn(batch_size * n_batches       ,			1							)
    return TensorDataset(snake_case       ,			a * x + b + 0.1 * torch.randn(batch_size * n_batches       ,			1							)							)

  __SCREAMING_SNAKE_CASE      :	str					=		get_dataset(snake_case							)
  __SCREAMING_SNAKE_CASE      :	Dict					=		get_dataset(snake_case							)
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		DataLoader(snake_case       ,			shuffle=snake_case       ,			batch_size=snake_case       ,			num_workers=4							)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		DataLoader(snake_case       ,			shuffle=snake_case       ,			batch_size=snake_case       ,			num_workers=4							)
  return (train_dataloader, valid_dataloader)


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=None							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Tuple					=		[]
  for epoch in range(snake_case							):
    # Train quickly
    model.train()
    for batch in dataloader:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		batch
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		model(snake_case							)
      __SCREAMING_SNAKE_CASE      :	str					=		torch.nn.functional.mse_loss(snake_case       ,			snake_case							)
      accelerator.backward(snake_case							)
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random()							)  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands





class     __UpperCamelCase (							nn.Module       ):






     """simple docstring"""







     def __init__(				self							:							Union[str, Any] ):







       """simple docstring"""




       super().__init__()
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		nn.Parameter(torch.randn(1 ) )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		nn.Parameter(torch.randn(1 ) )




     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							List[str] ):







       """simple docstring"""




       return x * self.a + self.b






class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       with tempfile.TemporaryDirectory() as tmpdir:
         set_seed(42 )
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		DummyModel()
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		torch.optim.Adam(params=model.parameters()     ,						lr=1e-3 )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		dummy_dataloaders()
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		ProjectConfiguration(total_limit=1     ,						project_dir=_A     ,						automatic_checkpoint_naming=_A )
         # Train baseline
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		Accelerator(project_config=_A )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[Any]					=		accelerator.prepare(
             _A     ,						_A     ,						_A     ,						_A )
         # Save initial
         accelerator.save_state()

         # Save second state
         accelerator.save_state()
         self.assertEqual(len(os.listdir(accelerator.project_dir ) )     ,						1 )



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       with tempfile.TemporaryDirectory() as tmpdir:
         set_seed(42 )
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		DummyModel()
         __SCREAMING_SNAKE_CASE      :	int					=		torch.optim.Adam(params=model.parameters()     ,						lr=1e-3 )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		dummy_dataloaders()
         # Train baseline
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		Accelerator()
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		accelerator.prepare(
             _A     ,						_A     ,						_A     ,						_A )
         # Save initial
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		os.path.join(_A     ,						'''initial''' )
         accelerator.save_state(_A )
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Any					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	Dict					=		optimizer.state_dict()
         __SCREAMING_SNAKE_CASE      :	Tuple					=		train(3     ,						_A     ,						_A     ,						_A     ,						_A )
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Union[str, Any]					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		optimizer.state_dict()

         # Train partially
         set_seed(42 )
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		DummyModel()
         __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.optim.Adam(params=model.parameters()     ,						lr=1e-3 )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		dummy_dataloaders()
         __SCREAMING_SNAKE_CASE      :	List[str]					=		Accelerator()
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		accelerator.prepare(
             _A     ,						_A     ,						_A     ,						_A )
         accelerator.load_state(_A )
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Any					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	Dict					=		optimizer.state_dict()
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )

         __SCREAMING_SNAKE_CASE      :	Tuple					=		train(2     ,						_A     ,						_A     ,						_A     ,						_A )
         # Save everything
         __SCREAMING_SNAKE_CASE      :	Dict					=		os.path.join(_A     ,						'''checkpoint''' )
         accelerator.save_state(_A )

         # Load everything back in and make sure all states work
         accelerator.load_state(_A )
         test_rands += train(1     ,						_A     ,						_A     ,						_A     ,						_A )
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Tuple					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	str					=		optimizer.state_dict()
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       with tempfile.TemporaryDirectory() as tmpdir:
         set_seed(42 )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		DummyModel()
         __SCREAMING_SNAKE_CASE      :	List[str]					=		torch.optim.Adam(params=model.parameters()     ,						lr=1e-3 )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	str					=		dummy_dataloaders()
         __SCREAMING_SNAKE_CASE      :	Tuple					=		ProjectConfiguration(automatic_checkpoint_naming=_A )

         # Train baseline
         __SCREAMING_SNAKE_CASE      :	Any					=		Accelerator(project_dir=_A     ,						project_config=_A )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	int					=		accelerator.prepare(
             _A     ,						_A     ,						_A     ,						_A )
         # Save initial
         accelerator.save_state()
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Optional[int]					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	Tuple					=		optimizer.state_dict()
         __SCREAMING_SNAKE_CASE      :	Any					=		train(3     ,						_A     ,						_A     ,						_A     ,						_A )
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Union[str, Any]					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	Tuple					=		optimizer.state_dict()

         # Train partially
         set_seed(42 )
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		DummyModel()
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		torch.optim.Adam(params=model.parameters()     ,						lr=1e-3 )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		dummy_dataloaders()
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		ProjectConfiguration(iteration=1     ,						automatic_checkpoint_naming=_A )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		Accelerator(project_dir=_A     ,						project_config=_A )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		accelerator.prepare(
             _A     ,						_A     ,						_A     ,						_A )
         accelerator.load_state(os.path.join(_A     ,						'''checkpoints'''     ,						'''checkpoint_0''' ) )
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Optional[Any]					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		optimizer.state_dict()
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )

         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		train(2     ,						_A     ,						_A     ,						_A     ,						_A )
         # Save everything
         accelerator.save_state()

         # Load everything back in and make sure all states work
         accelerator.load_state(os.path.join(_A     ,						'''checkpoints'''     ,						'''checkpoint_1''' ) )
         test_rands += train(1     ,						_A     ,						_A     ,						_A     ,						_A )
         ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Optional[int]					=		model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      :	int					=		optimizer.state_dict()
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )
         self.assertEqual(_A     ,						_A )



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.tensor([1, 2, 3] )
       __SCREAMING_SNAKE_CASE      :	str					=		torch.tensor([2, 3, 4] )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		DummyModel()
       __SCREAMING_SNAKE_CASE      :	int					=		torch.optim.Adam(net.parameters() )
       __SCREAMING_SNAKE_CASE      :	int					=		Accelerator()
       with self.assertRaises(_A ) as ve:
         accelerator.register_for_checkpointing(_A     ,						_A     ,						_A     ,						_A )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		str(ve.exception )
       self.assertTrue('''Item at index 0''' in message )
       self.assertTrue('''Item at index 1''' in message )
       self.assertFalse('''Item at index 2''' in message )
       self.assertFalse('''Item at index 3''' in message )



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       with tempfile.TemporaryDirectory() as tmpdir:
         set_seed(42 )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		DummyModel()
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		torch.optim.Adam(params=model.parameters()     ,						lr=1e-3 )
         __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.optim.lr_scheduler.StepLR(_A     ,						step_size=1     ,						gamma=0.99 )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		dummy_dataloaders()
         __SCREAMING_SNAKE_CASE      :	int					=		ProjectConfiguration(automatic_checkpoint_naming=_A )
         # Train baseline
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		Accelerator(project_dir=_A     ,						project_config=_A )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		accelerator.prepare(
             _A     ,						_A     ,						_A     ,						_A     ,						_A )
         # Save initial
         accelerator.save_state()
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		scheduler.state_dict()
         train(3     ,						_A     ,						_A     ,						_A     ,						_A     ,						_A )
         self.assertNotEqual(_A     ,						scheduler.state_dict() )

         # Load everything back in and make sure all states work
         accelerator.load_state(os.path.join(_A     ,						'''checkpoints'''     ,						'''checkpoint_0''' ) )
         self.assertEqual(_A     ,						scheduler.state_dict() )



     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       with tempfile.TemporaryDirectory() as tmpdir:
         set_seed(42 )
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		DummyModel()
         __SCREAMING_SNAKE_CASE      :	List[str]					=		ProjectConfiguration(automatic_checkpoint_naming=_A     ,						total_limit=2 )
         # Train baseline
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		Accelerator(project_dir=_A     ,						project_config=_A )
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		accelerator.prepare(_A )
         # Save 3 states:
         for _ in range(11 ):
           accelerator.save_state()
         self.assertTrue(not os.path.exists(os.path.join(_A     ,						'''checkpoints'''     ,						'''checkpoint_0''' ) ) )
         self.assertTrue(os.path.exists(os.path.join(_A     ,						'''checkpoints'''     ,						'''checkpoint_9''' ) ) )
         self.assertTrue(os.path.exists(os.path.join(_A     ,						'''checkpoints'''     ,						'''checkpoint_10''' ) ) )




     @require_cuda
     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
       execute_subprocess_async(_A     ,						env=os.environ.copy() )


if __name__ == "__main__":
   lowercase_ 				=			"""/tmp/accelerate/state_checkpointing"""
   lowercase_ 				=			DummyModel()
   lowercase_ 				=			torch.optim.Adam(params=model.parameters(), lr=1e-3)
   lowercase_ 				=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
   lowercase_				,    lowercase_ 				=			dummy_dataloaders()
   lowercase_ 				=			ProjectConfiguration(automatic_checkpoint_naming=True)
   # Train baseline
   lowercase_ 				=			Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
   if accelerator.process_index == 0:
      if os.path.exists(savedir):
         shutil.rmtree(savedir)
      os.makedirs(savedir)
   lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ 				=			accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   lowercase_				,    lowercase_ 				=			accelerator.prepare(model, optimizer)
   train(3, model, train_dataloader, optimizer, accelerator, scheduler)
   # Check that the intial optimizer is loaded on the GPU
   for group in optimizer.param_groups:
      lowercase_ 				=			group["""params"""][0].device
      break
   assert param_device.type == accelerator.device.type
   lowercase_ 				=			model.cpu()
   accelerator.wait_for_everyone()
   accelerator.save_state()
   accelerator.wait_for_everyone()

   # Check CPU state
   accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
   for group in optimizer.param_groups:
      lowercase_ 				=			group["""params"""][0].device
      break
   assert (
       param_device.type == torch.device("""cpu""").type
   ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

   # Check device state
   model.to(accelerator.device)
   accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
   for group in optimizer.param_groups:
      lowercase_ 				=			group["""params"""][0].device
      break
   assert (
       param_device.type == accelerator.device.type
   ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

   # Check error
   with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
      accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
   accelerator.wait_for_everyone()
   if accelerator.process_index == 0:
      shutil.rmtree(savedir)
   accelerator.wait_for_everyone()







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


SCREAMING_SNAKE_CASE_   :      Any			=   ['''model.decoder.embed_positions.weights''']



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		if "emb" in name:
				__lowercase											=     name.replace('emb'						,   'model.decoder.embed_tokens'							)
		if "transformer" in name:
				__lowercase											=     name.replace('transformer'						,   'model.decoder'							)
		if "cross_attention" in name:
				__lowercase											=     name.replace('cross_attention'						,   'encoder_attn'							)
		if "linear1" in name:
				__lowercase											=     name.replace('linear1'						,   'fc1'							)
		if "linear2" in name:
				__lowercase											=     name.replace('linear2'						,   'fc2'							)
		if "norm1" in name:
				__lowercase											=     name.replace('norm1'						,   'self_attn_layer_norm'							)
		if "norm_cross" in name:
				__lowercase											=     name.replace('norm_cross'						,   'encoder_attn_layer_norm'							)
		if "norm2" in name:
				__lowercase											=     name.replace('norm2'						,   'final_layer_norm'							)
		if "out_norm" in name:
				__lowercase											=     name.replace('out_norm'						,   'model.decoder.layer_norm'							)
		if "linears" in name:
				__lowercase											=     name.replace('linears'						,   'lm_heads'							)
		if "condition_provider.conditioners.description.output_proj" in name:
				__lowercase											=     name.replace('condition_provider.conditioners.description.output_proj'						,   'enc_to_dec_proj'							)
		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Tuple[Dict, Dict]:
		__lowercase											=     list(state_dict.keys()							)
		__lowercase											=     {}
		for key in keys:
				__lowercase											=     state_dict.pop(snake_case							)
				__lowercase											=     rename_keys(snake_case							)
				if "in_proj_weight" in key:
						# split fused qkv proj
						__lowercase											=     val[:hidden_size, :]
						__lowercase											=     val[hidden_size : 2 * hidden_size, :]
						__lowercase											=     val[-hidden_size:, :]
				elif "enc_to_dec_proj" in key:
						__lowercase											=     val
				else:
						__lowercase											=     val
		return state_dict, enc_dec_proj_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    MusicgenDecoderConfig:
		if checkpoint == "small":
				# default config values
				__lowercase											=     1_024
				__lowercase											=     24
				__lowercase											=     16
		elif checkpoint == "medium":
				__lowercase											=     1_536
				__lowercase											=     48
				__lowercase											=     24
		elif checkpoint == "large":
				__lowercase											=     2_048
				__lowercase											=     48
				__lowercase											=     32
		else:
				raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."							)
		__lowercase											=     MusicgenDecoderConfig(
		    hidden_size=snake_case						,   ffn_dim=hidden_size * 4						,   num_hidden_layers=snake_case						,   num_attention_heads=snake_case						,   )
		return config



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=None						,   snake_case="cpu"							)		->    Optional[int]:
		__lowercase											=     MusicGen.get_pretrained(snake_case						,   device=snake_case							)
		__lowercase											=     decoder_config_from_checkpoint(snake_case							)

		__lowercase											=     fairseq_model.lm.state_dict()
		__lowercase						,      __lowercase											=     rename_state_dict(
		    snake_case						,   hidden_size=decoder_config.hidden_size							)

		__lowercase											=     TaEncoderModel.from_pretrained('t5-base'							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_32khz'							)
		__lowercase											=     MusicgenForCausalLM(snake_case							).eval()

		# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
		__lowercase						,      __lowercase											=     decoder.load_state_dict(snake_case						,   strict=snake_case							)

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

		if len(snake_case							) > 0:
				raise ValueError(F"Missing key(s) in state_dict: {missing_keys}"							)

		if len(snake_case							) > 0:
				raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}"							)

		# init the composite model
		__lowercase											=     MusicgenForConditionalGeneration(text_encoder=snake_case						,   audio_encoder=snake_case						,   decoder=snake_case							)

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

		# check we can do a forward pass
		__lowercase											=     torch.arange(0						,   8						,   dtype=torch.long							).reshape(2						,   -1							)
		__lowercase											=     input_ids.reshape(2 * 4						,   -1							)

		with torch.no_grad():
				__lowercase											=     model(input_ids=snake_case						,   decoder_input_ids=snake_case							).logits

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

		# now construct the processor
		__lowercase											=     AutoTokenizer.from_pretrained('t5-base'							)
		__lowercase											=     AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'						,   padding_side='left'							)

		__lowercase											=     MusicgenProcessor(feature_extractor=snake_case						,   tokenizer=snake_case							)

		# set the appropriate bos/pad token ids
		__lowercase											=     2_048
		__lowercase											=     2_048

		# set other default generation config params
		__lowercase											=     int(30 * audio_encoder.config.frame_rate							)
		__lowercase											=     True
		__lowercase											=     3.0

		if pytorch_dump_folder is not None:
				Path(snake_case							).mkdir(exist_ok=snake_case							)
				logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}"							)
				model.save_pretrained(snake_case							)
				processor.save_pretrained(snake_case							)

		if repo_id:
				logger.info(F"Pushing model {checkpoint} to {repo_id}"							)
				model.push_to_hub(snake_case							)
				processor.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[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.'''
		)

		SCREAMING_SNAKE_CASE_   :      Any			=   parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




