





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
      from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
      lowercase_         =						get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
      from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowercase_         =						128_022
lowercase_         =						128_028
@require_sentencepiece
class 							SCREAMING_SNAKE_CASE  (UpperCAmelCase_     ,      unittest.TestCase ):
   _UpperCamelCase       :						Dict					   =			MaMaaaTokenizer
   _UpperCamelCase       :						Tuple					   =			False
   _UpperCamelCase       :						Optional[Any]					   =			False
   _UpperCamelCase       :						Tuple					   =			True


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[Any]			)->  Optional[int]:





     """simple docstring"""







     super().setUp()

     lowercase__           =    ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
     lowercase__           =    dict(zip(lowerCamelCase__     ,	range(len(lowerCamelCase__			)			)			)			)
     lowercase__           =    Path(self.tmpdirname			)
     save_json(lowerCamelCase__     ,	save_dir / VOCAB_FILES_NAMES['vocab_file']			)
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(lowerCamelCase__     ,	save_dir / VOCAB_FILES_NAMES['spm_file']			)

     lowercase__           =    MaMaaaTokenizer.from_pretrained(self.tmpdirname			)
     tokenizer.save_pretrained(self.tmpdirname			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Tuple     ,	**a     :							Tuple			)->  List[Any]:





     """simple docstring"""







     return MaMaaaTokenizer.from_pretrained(self.tmpdirname     ,	**lowerCamelCase__			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Any     ,	a     :							List[str]			)->  List[Any]:





     """simple docstring"""







     return (
         "This is a test",
         "This is a test",
     )


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Union[str, Any]			)->  Any:





     """simple docstring"""







     lowercase__           =    """</s>"""
     lowercase__           =    0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__			)     ,	lowerCamelCase__			)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__			)     ,	lowerCamelCase__			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  Any:





     """simple docstring"""







     lowercase__           =    self.get_tokenizer()
     lowercase__           =    list(tokenizer.get_vocab().keys()			)

     self.assertEqual(vocab_keys[0]     ,	'</s>'			)
     self.assertEqual(vocab_keys[1]     ,	'<unk>'			)
     self.assertEqual(vocab_keys[-1]     ,	'<s>'			)
     self.assertEqual(len(lowerCamelCase__			)     ,	tokenizer.vocab_size + len(tokenizer.get_added_vocab()			)			)


   @unittest.skip('Skip this test while all models are still to be uploaded.'			)
   def 			SCREAMING_SNAKE_CASE_				(       self     :							str			)->  Optional[Any]:





     """simple docstring"""







     pass


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Tuple			)->  str:





     """simple docstring"""







     lowercase__           =    self.get_tokenizer()

     lowercase__           =    tokenizer.tokenize('This is a test'			)
     self.assertListEqual(lowerCamelCase__     ,	['▁This', '▁is', '▁a', '▁t', 'est']			)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowerCamelCase__			)     ,	[2, 3, 4, 5, 6]     ,	)

     lowercase__           =    tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]			)
     self.assertListEqual(lowerCamelCase__     ,	['▁This', '▁is', '▁a', '▁t', 'est']			)

     lowercase__           =    tokenizer.convert_tokens_to_string(lowerCamelCase__			)
     self.assertEqual(lowerCamelCase__     ,	'This is a test'			)


   @slow
   def 			SCREAMING_SNAKE_CASE_				(       self     :							int			)->  Optional[Any]:





     """simple docstring"""







     lowercase__           =    {"""input_ids""": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowerCamelCase__     ,	model_name='facebook/m2m100_418M'     ,	revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'     ,	)






@require_torch
@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE  (unittest.TestCase ):
   _UpperCamelCase       :						Any					   =			'''facebook/m2m100_418M'''
   _UpperCamelCase       :						List[Any]					   =			[
       '''In my opinion, there are two levels of response from the French government.''',
       '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
   ]
   _UpperCamelCase       :						Optional[int]					   =			[
       '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
       '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
   ]

   # fmt: off
   _UpperCamelCase       :						int					   =			[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]


   @classmethod
   def 			SCREAMING_SNAKE_CASE_				(       cls     :							List[str]			)->  Any:





     """simple docstring"""







     lowercase__           =    MaMaaaTokenizer.from_pretrained(
         cls.checkpoint_name     ,	src_lang='en'     ,	tgt_lang='fr'			)
     lowercase__           =    1
     return cls


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  str:





     """simple docstring"""







     self.assertEqual(self.tokenizer.get_lang_id('ar'			)     ,	128_006			)
     self.assertEqual(self.tokenizer.get_lang_id('en'			)     ,	128_022			)
     self.assertEqual(self.tokenizer.get_lang_id('ro'			)     ,	128_076			)
     self.assertEqual(self.tokenizer.get_lang_id('mr'			)     ,	128_063			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[Any]			)->  Optional[Any]:





     """simple docstring"""







     lowercase__           =    self.tokenizer.get_vocab()
     self.assertEqual(len(lowerCamelCase__			)     ,	self.tokenizer.vocab_size			)
     self.assertEqual(vocab['<unk>']     ,	3			)
     self.assertIn(self.tokenizer.get_lang_token('en'			)     ,	lowerCamelCase__			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  Tuple:





     """simple docstring"""







     lowercase__           =    """en"""
     lowercase__           =    self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
     self.assertListEqual(self.expected_src_tokens     ,	lowerCamelCase__			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Tuple			)->  Optional[Any]:





     """simple docstring"""







     self.assertIn(lowerCamelCase__     ,	self.tokenizer.all_special_ids			)
     # fmt: off
     lowercase__           =    [FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
     # fmt: on
     lowercase__           =    self.tokenizer.decode(lowerCamelCase__     ,	skip_special_tokens=lowerCamelCase__			)
     lowercase__           =    self.tokenizer.decode(generated_ids[1:]     ,	skip_special_tokens=lowerCamelCase__			)
     self.assertEqual(lowerCamelCase__     ,	lowerCamelCase__			)
     self.assertNotIn(self.tokenizer.eos_token     ,	lowerCamelCase__			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							str			)->  str:





     """simple docstring"""







     lowercase__           =    tempfile.mkdtemp()
     lowercase__           =    self.tokenizer.lang_token_to_id
     self.tokenizer.save_pretrained(lowerCamelCase__			)
     lowercase__           =    MaMaaaTokenizer.from_pretrained(lowerCamelCase__			)
     self.assertDictEqual(new_tok.lang_token_to_id     ,	lowerCamelCase__			)


   @require_torch
   def 			SCREAMING_SNAKE_CASE_				(       self     :							Tuple			)->  List[str]:





     """simple docstring"""







     lowercase__           =    """en"""
     lowercase__           =    """fr"""

     lowercase__           =    self.tokenizer(self.src_text     ,	text_target=self.tgt_text     ,	padding=lowerCamelCase__     ,	return_tensors='pt'			)

     lowercase__           =    shift_tokens_right(
         batch['labels']     ,	self.tokenizer.pad_token_id     ,	self.tokenizer.eos_token_id			)

     for k in batch:
       lowercase__           =    batch[k].tolist()
     # batch = {k: v.tolist() for k,v in batch.items()}
     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     # batch.decoder_inputs_ids[0][0] ==
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == FR_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


   @require_torch
   def 			SCREAMING_SNAKE_CASE_				(       self     :							Union[str, Any]			)->  Dict:





     """simple docstring"""







     lowercase__           =    """mr"""
     self.assertListEqual(self.tokenizer.prefix_tokens     ,	[self.tokenizer.get_lang_id('mr'			)]			)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,	[self.tokenizer.eos_token_id]			)

     lowercase__           =    """zh"""
     self.assertListEqual(self.tokenizer.prefix_tokens     ,	[self.tokenizer.get_lang_id('zh'			)]			)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,	[self.tokenizer.eos_token_id]			)


   @require_torch
   def 			SCREAMING_SNAKE_CASE_				(       self     :							Dict			)->  str:





     """simple docstring"""







     lowercase__           =    """mr"""
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,	[self.tokenizer.get_lang_id('mr'			)]			)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,	[self.tokenizer.eos_token_id]			)
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)

     lowercase__           =    """zh"""
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,	[self.tokenizer.get_lang_id('zh'			)]			)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,	[self.tokenizer.eos_token_id]			)
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)


   @require_torch
   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[Any]			)->  List[str]:





     """simple docstring"""







     lowercase__           =    self.tokenizer._build_translation_inputs('A test'     ,	return_tensors='pt'     ,	src_lang='en'     ,	tgt_lang='ar'			)

     self.assertEqual(
         nested_simplify(lowerCamelCase__			)     ,	{
             # en_XX, A, test, EOS
             'input_ids': [[128_022, 58, 4_183, 2]],
             'attention_mask': [[1, 1, 1, 1]],
             # ar_AR
             'forced_bos_token_id': 128_006,
         }     ,	)










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()
UpperCamelCase    							=      logging.get_logger(__name__)


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


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


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      __UpperCamelCase	:			int					=					1024
      __UpperCamelCase	:			Union[str, Any]					=					24
      __UpperCamelCase	:			int					=					16
   elif checkpoint == "medium":
      __UpperCamelCase	:			List[Any]					=					1536
      __UpperCamelCase	:			Dict					=					48
      __UpperCamelCase	:			Dict					=					24
   elif checkpoint == "large":
      __UpperCamelCase	:			List[Any]					=					2048
      __UpperCamelCase	:			str					=					48
      __UpperCamelCase	:			Optional[int]					=					32
   else:
      raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
   __UpperCamelCase	:			Any					=					MusicgenDecoderConfig(
       hidden_size=__lowerCAmelCase					,							ffn_dim=hidden_size * 4					,							num_hidden_layers=__lowerCAmelCase					,							num_attention_heads=__lowerCAmelCase					,							)
   return config


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]=None					,							__lowerCAmelCase							:					Tuple=None					,							__lowerCAmelCase							:					str="cpu"							)     ->  Optional[int]:
   __UpperCamelCase	:			str					=					MusicGen.get_pretrained(__lowerCAmelCase					,							device=__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					decoder_config_from_checkpoint(__lowerCAmelCase							)

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

   __UpperCamelCase	:			List[Any]					=					TaEncoderModel.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			Dict					=					EncodecModel.from_pretrained("""facebook/encodec_32khz"""							)
   __UpperCamelCase	:			List[str]					=					MusicgenForCausalLM(__lowerCAmelCase							).eval()

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

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

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

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

   # init the composite model
   __UpperCamelCase	:			Tuple					=					MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase					,							audio_encoder=__lowerCAmelCase					,							decoder=__lowerCAmelCase							)

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

   # 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	:			int					=					model(input_ids=__lowerCAmelCase					,							decoder_input_ids=__lowerCAmelCase							).logits

   if logits.shape != (8, 1, 2048):
      raise ValueError("""Incorrect shape for logits"""							)

   # now construct the processor
   __UpperCamelCase	:			Union[str, Any]					=					AutoTokenizer.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			List[Any]					=					AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""					,							padding_side="""left"""							)

   __UpperCamelCase	:			Any					=					MusicgenProcessor(feature_extractor=__lowerCAmelCase					,							tokenizer=__lowerCAmelCase							)

   # set the appropriate bos/pad token ids
   __UpperCamelCase	:			Tuple					=					2048
   __UpperCamelCase	:			int					=					2048

   # set other default generation config params
   __UpperCamelCase	:			str					=					int(30 * audio_encoder.config.frame_rate							)
   __UpperCamelCase	:			List[str]					=					True
   __UpperCamelCase	:			Tuple					=					3.0

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

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


if __name__ == "__main__":
       UpperCamelCase    							=      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.'
       )

       UpperCamelCase    							=      parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



