






'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase   :							Union[str, Any]         =   get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

__UpperCamelCase   :							Tuple         =   250004
__UpperCamelCase   :							Tuple         =   250020
@require_sentencepiece
@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(				__SCREAMING_SNAKE_CASE   ,   unittest.TestCase  ):
      __a	       =MBartaaTokenizer
      __a	       =MBartaaTokenizerFast
      __a	       =True
      __a	       =True




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         super().setUp()

         # We have a SentencePiece fixture for testing
         __a					  =     MBartaaTokenizer(_A			, src_lang='en_XX'			, tgt_lang='ro_RO'			, keep_accents=_A   )
         tokenizer.save_pretrained(self.tmpdirname   )




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     """<s>"""
         __a					  =     0

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




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     list(self.get_tokenizer().get_vocab().keys()   )

         self.assertEqual(vocab_keys[0]			, '<s>'   )
         self.assertEqual(vocab_keys[1]			, '<pad>'   )
         self.assertEqual(vocab_keys[-1]			, '<mask>'   )
         self.assertEqual(len(_A   )			, 1054   )




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



         '''simple docstring'''
         self.assertEqual(self.get_tokenizer().vocab_size			, 1054   )




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     MBartaaTokenizer(_A			, src_lang='en_XX'			, tgt_lang='ro_RO'			, keep_accents=_A   )

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

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_A   )			, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			, )

         __a					  =     tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
         self.assertListEqual(
             _A			, [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']			, )
         __a					  =     tokenizer.convert_tokens_to_ids(_A   )
         self.assertListEqual(
             _A			, [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]			, )

         __a					  =     tokenizer.convert_ids_to_tokens(_A   )
         self.assertListEqual(
             _A			, [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']			, )




      @slow
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     {"""input_ids""": [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 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], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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]], """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, 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], [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]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_A			, model_name='facebook/mbart-large-50'			, revision='d3913889c59cd5c9e456b269c376325eabad57e2'			, )






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



         '''simple docstring'''







         if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

         __a					  =     (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
               __a					  =     self.rust_tokenizer_class.from_pretrained(_A			, **_A   )
               __a					  =     self.tokenizer_class.from_pretrained(_A			, **_A   )

               __a					  =     tempfile.mkdtemp()

               __a					  =     tokenizer_r.save_pretrained(_A   )
               __a					  =     tokenizer_p.save_pretrained(_A   )

               # Checks it save with the same files + the tokenizer.json file for the fast one
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )
               __a					  =     tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f   )
               self.assertSequenceEqual(_A			, _A   )

               # Checks everything loads correctly in the same way
               __a					  =     tokenizer_r.from_pretrained(_A   )
               __a					  =     tokenizer_p.from_pretrained(_A   )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_A			, _A   )   )
                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

               shutil.rmtree(_A   )

               # Save tokenizer rust, legacy_format=True
               __a					  =     tempfile.mkdtemp()

               __a					  =     tokenizer_r.save_pretrained(_A			, legacy_format=_A   )
               __a					  =     tokenizer_p.save_pretrained(_A   )

               # Checks it save with the same files
               self.assertSequenceEqual(_A			, _A   )

               # Checks everything loads correctly in the same way
               __a					  =     tokenizer_r.from_pretrained(_A   )
               __a					  =     tokenizer_p.from_pretrained(_A   )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_A			, _A   )   )

               shutil.rmtree(_A   )

               # Save tokenizer rust, legacy_format=False
               __a					  =     tempfile.mkdtemp()

               __a					  =     tokenizer_r.save_pretrained(_A			, legacy_format=_A   )
               __a					  =     tokenizer_p.save_pretrained(_A   )

               # Checks it saved the tokenizer.json file
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )

               # Checks everything loads correctly in the same way
               __a					  =     tokenizer_r.from_pretrained(_A   )
               __a					  =     tokenizer_p.from_pretrained(_A   )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_A			, _A   )   )

               shutil.rmtree(_A   )







@require_torch
@require_sentencepiece
@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):
      __a	       ="""facebook/mbart-large-50-one-to-many-mmt"""
      __a	       =[
          """ UN Chief Says There Is No Military Solution in Syria""",
          """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
      ]
      __a	       =[
          """Şeful ONU declară că nu există o soluţie militară în Siria""",
          """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
          """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
          """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
      ]
      __a	       =[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]




      @classmethod
      def 			__UpperCamelCase				(	cls   )				->int:



         '''simple docstring'''
         __a					  =     MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name			, src_lang='en_XX'			, tgt_lang='ro_RO'   )
         __a					  =     1
         return cls




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



         '''simple docstring'''
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']			, 25_0001   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']			, 25_0004   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']			, 25_0020   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']			, 25_0038   )




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens			, _A   )




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         self.assertIn(_A			, self.tokenizer.all_special_ids   )
         __a					  =     [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
         __a					  =     self.tokenizer.decode(_A			, skip_special_tokens=_A   )
         __a					  =     self.tokenizer.decode(generated_ids[1:]			, skip_special_tokens=_A   )
         self.assertEqual(_A			, _A   )
         self.assertNotIn(self.tokenizer.eos_token			, _A   )




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     ["""this is gunna be a long sentence """ * 20]
         assert isinstance(src_text[0]			, _A   )
         __a					  =     10
         __a					  =     self.tokenizer(_A			, max_length=_A			, truncation=_A   ).input_ids[0]
         self.assertEqual(ids[0]			, _A   )
         self.assertEqual(ids[-1]			, 2   )
         self.assertEqual(len(_A   )			, _A   )




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']   )			, [25_0053, 25_0001]   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     tempfile.mkdtemp()
         __a					  =     self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(_A   )
         __a					  =     MBartaaTokenizer.from_pretrained(_A   )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids			, _A   )




      @require_torch
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     self.tokenizer(self.src_text			, text_target=self.tgt_text			, padding=_A			, return_tensors='pt'   )
         __a					  =     shift_tokens_right(batch['labels']			, self.tokenizer.pad_token_id   )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]




      @require_torch
      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         __a					  =     self.tokenizer(
             self.src_text			, text_target=self.tgt_text			, padding=_A			, truncation=_A			, max_length=len(self.expected_src_tokens   )			, return_tensors='pt'			, )
         __a					  =     shift_tokens_right(batch['labels']			, self.tokenizer.pad_token_id   )

         self.assertIsInstance(_A			, _A   )

         self.assertEqual((2, 14)			, batch.input_ids.shape   )
         self.assertEqual((2, 14)			, batch.attention_mask.shape   )
         __a					  =     batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens			, _A   )
         self.assertEqual(2			, batch.decoder_input_ids[0, 0]   )  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens			, [EN_CODE]   )
         self.assertEqual(self.tokenizer.suffix_tokens			, [self.tokenizer.eos_token_id]   )




      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         __a					  =     self.tokenizer(self.src_text			, padding=_A			, truncation=_A			, max_length=3			, return_tensors='pt'   )
         __a					  =     self.tokenizer(
             text_target=self.tgt_text			, padding=_A			, truncation=_A			, max_length=10			, return_tensors='pt'   )
         __a					  =     targets["""input_ids"""]
         __a					  =     shift_tokens_right(_A			, self.tokenizer.pad_token_id   )

         self.assertEqual(batch.input_ids.shape[1]			, 3   )
         self.assertEqual(batch.decoder_input_ids.shape[1]			, 10   )






      @require_torch
      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.tokenizer._build_translation_inputs(
             'A test'			, return_tensors='pt'			, src_lang='en_XX'			, tgt_lang='ar_AR'   )

         self.assertEqual(
             nested_simplify(_A   )			, {
                 # en_XX, A, test, EOS
                 'input_ids': [[25_0004, 62, 3034, 2]],
                 'attention_mask': [[1, 1, 1, 1]],
                 # ar_AR
                 'forced_bos_token_id': 25_0001,
             }			, )






"""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()
__magic_name__       :					List[Any]						=			logging.get_logger(__name__)


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

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

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if checkpoint == "small":
												# default config values
												UpperCamelCase				:		Tuple				   =			1024
												UpperCamelCase				:		Dict				   =			24
												UpperCamelCase				:		Optional[int]				   =			16
						elif checkpoint == "medium":
												UpperCamelCase				:		Optional[Any]				   =			1536
												UpperCamelCase				:		int				   =			48
												UpperCamelCase				:		int				   =			24
						elif checkpoint == "large":
												UpperCamelCase				:		Any				   =			2048
												UpperCamelCase				:		List[Any]				   =			48
												UpperCamelCase				:		str				   =			32
						else:
												raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""     )
						UpperCamelCase				:		Optional[int]				   =			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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE="cpu"     ):
						UpperCamelCase				:		Optional[Any]				   =			MusicGen.get_pretrained(SCREAMING_SNAKE_CASE							,     device=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Tuple				   =			decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[Any]				   =			fairseq_model.lm.state_dict()
						UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			rename_state_dict(
						    SCREAMING_SNAKE_CASE							,     hidden_size=decoder_config.hidden_size     )

						UpperCamelCase				:		int				   =			TaEncoderModel.from_pretrained("""t5-base"""     )
						UpperCamelCase				:		List[str]				   =			EncodecModel.from_pretrained("""facebook/encodec_32khz"""     )
						UpperCamelCase				:		Optional[int]				   =			MusicgenForCausalLM(SCREAMING_SNAKE_CASE     ).eval()

						# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
						UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			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				:		Tuple				   =			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				:		Tuple				   =			input_ids.reshape(2 * 4							,     -1     )

						with torch.no_grad():
												UpperCamelCase				:		Any				   =			model(input_ids=SCREAMING_SNAKE_CASE							,     decoder_input_ids=SCREAMING_SNAKE_CASE     ).logits

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

						# now construct the processor
						UpperCamelCase				:		Any				   =			AutoTokenizer.from_pretrained("""t5-base"""     )
						UpperCamelCase				:		Tuple				   =			AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""							,     padding_side="""left"""     )

						UpperCamelCase				:		int				   =			MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE							,     tokenizer=SCREAMING_SNAKE_CASE     )

						# set the appropriate bos/pad token ids
						UpperCamelCase				:		Optional[int]				   =			2048
						UpperCamelCase				:		Tuple				   =			2048

						# set other default generation config params
						UpperCamelCase				:		Optional[int]				   =			int(30 * audio_encoder.config.frame_rate     )
						UpperCamelCase				:		int				   =			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__":
		__magic_name__       :					Any						=			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."""
		)

		__magic_name__       :					Optional[Any]						=			parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



