"""simple docstring"""





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowerCAmelCase_     =	'''bert-base-cased'''
lowerCAmelCase_     =	'''google/pegasus-xsum'''
lowerCAmelCase_     =	[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCAmelCase_     =	['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCAmelCase_     =	'''patrickvonplaten/t5-tiny-random'''
lowerCAmelCase_     =	'''sshleifer/bart-tiny-random'''
lowerCAmelCase_     =	'''sshleifer/tiny-mbart'''
lowerCAmelCase_     =	'''sshleifer/tiny-marian-en-de'''



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    """\n""".join(__SCREAMING_SNAKE_CASE  )
							Path(__SCREAMING_SNAKE_CASE  ).open("""w"""  ).writelines(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Dict:
							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE     ,	F"""{split}.source"""  )     ,	__SCREAMING_SNAKE_CASE  )
														_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE     ,	F"""{split}.target"""  )     ,	__SCREAMING_SNAKE_CASE  )
							return tmp_dir




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]      ,    )
				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    max(len(tokenizer.encode(_A)) for a in ARTICLES)
											_SCREAMING_SNAKE_CASE		:						str			  =    max(len(tokenizer.encode(_A)) for a in SUMMARIES)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    4
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    8
											assert max_len_target > max_src_len  # Will be truncated
											assert max_len_source > max_src_len  # Will be truncated
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    SeqaSeqDataset(
											    _A      ,    data_dir=_A      ,    type_path="""train"""      ,    max_source_length=_A      ,    max_target_length=_A      ,    src_lang=_A      ,    tgt_lang=_A      ,    )
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    DataLoader(_A      ,    batch_size=2      ,    collate_fn=train_dataset.collate_fn)
											for batch in dataloader:
																		assert isinstance(_A      ,    _A)
																		assert batch["attention_mask"].shape == batch["input_ids"].shape
																		# show that articles were trimmed.
																		assert batch["input_ids"].shape[1] == max_src_len
																		# show that targets are the same len
																		assert batch["labels"].shape[1] == max_tgt_len
																		if tok_name != MBART_TINY:
																									continue
																		# check language codes in correct place
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    shift_tokens_right(batch["""labels"""]      ,    tokenizer.pad_token_id)
																		assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
																		assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
																		assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
																		assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

																		break  # No need to test every batch





				@parameterized.expand([BART_TINY, BERT_BASE_CASED])
				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    AutoTokenizer.from_pretrained(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    max(len(tokenizer.encode(_A)) for a in ARTICLES)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    max(len(tokenizer.encode(_A)) for a in SUMMARIES)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    4
											_SCREAMING_SNAKE_CASE		:						Dict			  =    LegacySeqaSeqDataset(
											    _A      ,    data_dir=_A      ,    type_path="""train"""      ,    max_source_length=2_0      ,    max_target_length=_A      ,    )
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    DataLoader(_A      ,    batch_size=2      ,    collate_fn=train_dataset.collate_fn)
											for batch in dataloader:
																		assert batch["attention_mask"].shape == batch["input_ids"].shape
																		# show that articles were trimmed.
																		assert batch["input_ids"].shape[1] == max_len_source
																		assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
																		# show that targets were truncated
																		assert batch["labels"].shape[1] == trunc_target  # Truncated
																		assert max_len_target > trunc_target  # Truncated
																		break  # No need to test every batch





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25""")

											_SCREAMING_SNAKE_CASE		:						int			  =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    tmp_dir.joinpath("""train.source""").open().readlines()
											_SCREAMING_SNAKE_CASE		:						Dict			  =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
											pack_data_dir(_A      ,    _A      ,    1_2_8      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {x.name for x in tmp_dir.iterdir()}
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {x.name for x in save_dir.iterdir()}
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    save_dir.joinpath("""train.source""").open().readlines()
											# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
											# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
											assert len(_A) < len(_A)
											assert len(_A) == 1
											assert len(packed_examples[0]) == sum(len(_A) for x in orig_examples)
											assert orig_paths == new_paths





				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE      ,    reason="""This test requires fairseq""")
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											if not FAIRSEQ_AVAILABLE:
																		return
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						str			  =    self._get_dataset(max_len=6_4)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    6_4
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ds.make_dynamic_sampler(_A      ,    required_batch_size_multiple=_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [len(_A) for x in batch_sampler]
											assert len(set(_A)) > 1  # it's not dynamic batch size if every batch is the same length
											assert sum(_A) == len(_A)  # no dropped or added examples
											_SCREAMING_SNAKE_CASE		:						Any			  =    DataLoader(_A      ,    batch_sampler=_A      ,    collate_fn=ds.collate_fn      ,    num_workers=2)
											_SCREAMING_SNAKE_CASE		:						str			  =    []
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
											for batch in data_loader:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    batch["""input_ids"""].shape
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    src_shape[0]
																		assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    np.product(batch["""input_ids"""].shape)
																		num_src_per_batch.append(_A)
																		if num_src_tokens > (max_tokens * 1.1):
																									failures.append(_A)
											assert num_src_per_batch[0] == max(_A)
											if failures:
																		raise AssertionError(f"""too many tokens in {len(_A)} batches""")





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self._get_dataset(max_len=5_1_2)
											_SCREAMING_SNAKE_CASE		:						int			  =    2
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    ds.make_sortish_sampler(_A      ,    shuffle=_A)

											_SCREAMING_SNAKE_CASE		:						int			  =    DataLoader(_A      ,    batch_size=_A      ,    collate_fn=ds.collate_fn      ,    num_workers=2)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    DataLoader(_A      ,    batch_size=_A      ,    collate_fn=ds.collate_fn      ,    num_workers=2      ,    sampler=_A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer.pad_token_id

											def count_pad_tokens(_A							:							Tuple      ,    _A							:							Union[str, Any]="input_ids"):
																		return [batch[k].eq(_A).sum().item() for batch in data_loader]

											assert sum(count_pad_tokens(_A      ,    k="""labels""")) < sum(count_pad_tokens(_A      ,    k="""labels"""))
											assert sum(count_pad_tokens(_A)) < sum(count_pad_tokens(_A))
											assert len(_A) == len(_A)





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							List[Any]=1_0_0_0      ,    _A							:							List[str]=1_2_8):



											"""simple docstring"""







											if os.getenv("""USE_REAL_DATA"""      ,    _A):
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """examples/seq2seq/wmt_en_ro"""
																		_SCREAMING_SNAKE_CASE		:						str			  =    max_len * 2 * 6_4
																		if not Path(_A).joinpath("""train.len""").exists():
																									save_len_file(_A      ,    _A)
											else:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """examples/seq2seq/test_data/wmt_en_ro"""
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    max_len * 4
																		save_len_file(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    AutoTokenizer.from_pretrained(_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    SeqaSeqDataset(
											    _A      ,    data_dir=_A      ,    type_path="""train"""      ,    max_source_length=_A      ,    max_target_length=_A      ,    n_obs=_A      ,    )
											return ds, max_tokens, tokenizer





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    self._get_dataset()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    set(DistributedSortishSampler(_A      ,    2_5_6      ,    num_replicas=2      ,    rank=0      ,    add_extra_examples=_A))
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    set(DistributedSortishSampler(_A      ,    2_5_6      ,    num_replicas=2      ,    rank=1      ,    add_extra_examples=_A))
											assert idsa.intersection(_A) == set()





				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]      ,    )
				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    AutoTokenizer.from_pretrained(_A      ,    use_fast=_A)
											if tok_name == MBART_TINY:
																		_SCREAMING_SNAKE_CASE		:						str			  =    SeqaSeqDataset(
																		    _A      ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())      ,    type_path="""train"""      ,    max_source_length=4      ,    max_target_length=8      ,    src_lang="""EN"""      ,    tgt_lang="""FR"""      ,    )
																		_SCREAMING_SNAKE_CASE		:						int			  =    train_dataset.dataset_kwargs
																		assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    SeqaSeqDataset(
																		    _A      ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())      ,    type_path="""train"""      ,    max_source_length=4      ,    max_target_length=8      ,    )
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    train_dataset.dataset_kwargs
																		assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																		assert len(_A) == 1 if tok_name == BART_TINY else len(_A) == 0



"""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_     =	logging.get_logger(__name__)


lowerCAmelCase_     =	['''model.decoder.embed_positions.weights''']



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



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_024
														_SCREAMING_SNAKE_CASE		:						str			  =    24
														_SCREAMING_SNAKE_CASE		:						Any			  =    16
							elif checkpoint == "medium":
														_SCREAMING_SNAKE_CASE		:						Dict			  =    1_536
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    48
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    24
							elif checkpoint == "large":
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_048
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    48
														_SCREAMING_SNAKE_CASE		:						str			  =    32
							else:
														raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""  )
							_SCREAMING_SNAKE_CASE		:						Optional[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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="cpu"  )->    str:
							_SCREAMING_SNAKE_CASE		:						str			  =    MusicGen.get_pretrained(__SCREAMING_SNAKE_CASE     ,	device=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    decoder_config_from_checkpoint(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    fairseq_model.lm.state_dict()
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    rename_state_dict(
							    __SCREAMING_SNAKE_CASE     ,	hidden_size=decoder_config.hidden_size  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    TaEncoderModel.from_pretrained("""t5-base"""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    EncodecModel.from_pretrained("""facebook/encodec_32khz"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    MusicgenForCausalLM(__SCREAMING_SNAKE_CASE  ).eval()

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						str			  =    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
							_SCREAMING_SNAKE_CASE		:						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
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.arange(0     ,	8     ,	dtype=torch.long  ).reshape(2     ,	-1  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    input_ids.reshape(2 * 4     ,	-1  )

							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained("""t5-base"""  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""     ,	padding_side="""left"""  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    MusicgenProcessor(feature_extractor=__SCREAMING_SNAKE_CASE     ,	tokenizer=__SCREAMING_SNAKE_CASE  )

							# set the appropriate bos/pad token ids
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_048
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_048

							# set other default generation config params
							_SCREAMING_SNAKE_CASE		:						Any			  =    int(30 * audio_encoder.config.frame_rate  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    True
							_SCREAMING_SNAKE_CASE		:						int			  =    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_     =	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_     =	parser.parse_args()
			convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)


