



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class  __magic_name__   (							A__,  unittest.TestCase						):
  lowercase      :       Tuple          =LongformerTokenizer
  lowercase      :       List[Any]          =True
  lowercase      :       Union[str, Any]          =LongformerTokenizerFast
  lowercase      :       str          =True






  def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Union[str, Any]:
        '''simple docstring'''







        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase         =     [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        UpperCAmelCase         =     dict(zip(UpperCamelCase__				,		range(len(UpperCamelCase__      )      )      )      )
        UpperCAmelCase         =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        UpperCAmelCase         =     {"unk_token": "<unk>"}

        UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
        UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["merges_file"]      )
        with open(self.vocab_file				,		"w"				,		encoding="utf-8"      ) as fp:
              fp.write(json.dumps(UpperCamelCase__      ) + "\n"      )
        with open(self.merges_file				,		"w"				,		encoding="utf-8"      ) as fp:
              fp.write("\n".join(UpperCamelCase__      )      )






  def SCREAMING_SNAKE_CASE_			(						self      :     int				,		**UpperCamelCase__      :     Union[str, Any]      )				->					Optional[int]:
        '''simple docstring'''







        kwargs.update(self.special_tokens_map      )
        return self.tokenizer_class.from_pretrained(self.tmpdirname				,		**UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		**UpperCamelCase__      :     Any      )				->					Any:
        '''simple docstring'''







        kwargs.update(self.special_tokens_map      )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,		**UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     int      )				->					str:
        '''simple docstring'''







        UpperCAmelCase         =     "lower newer"
        UpperCAmelCase         =     "lower newer"
        return input_text, output_text






  def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					int:
        '''simple docstring'''







        UpperCAmelCase         =     self.tokenizer_class(self.vocab_file				,		self.merges_file				,		**self.special_tokens_map      )
        UpperCAmelCase         =     "lower newer"
        UpperCAmelCase         =     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
        UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )  # , add_prefix_space=True)
        self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

        UpperCAmelCase         =     tokens + [tokenizer.unk_token]
        UpperCAmelCase         =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )






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







        UpperCAmelCase         =     self.get_tokenizer()

        self.assertListEqual(tokenizer.encode("Hello world!"				,		add_special_tokens=UpperCamelCase__      )				,		[0, 3_14_14, 2_32, 3_28, 2]      )
        self.assertListEqual(
            tokenizer.encode("Hello world! cécé herlolip 418"				,		add_special_tokens=UpperCamelCase__      )				,		[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,		)






  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"      )

        UpperCAmelCase         =     tokenizer.encode("sequence builders"				,		add_special_tokens=UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.encode("multi-sequence build"				,		add_special_tokens=UpperCamelCase__      )

        UpperCAmelCase         =     tokenizer.encode(
            "sequence builders"				,		add_special_tokens=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.encode(
            "sequence builders"				,		"multi-sequence build"				,		add_special_tokens=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )

        UpperCAmelCase         =     tokenizer.build_inputs_with_special_tokens(UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.build_inputs_with_special_tokens(UpperCamelCase__				,		UpperCamelCase__      )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode






  def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     self.get_tokenizer()

        UpperCAmelCase         =     "Encode this sequence."
        UpperCAmelCase         =     tokenizer.byte_encoder[" ".encode("utf-8"      )[0]]

        # Testing encoder arguments
        UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
        self.assertNotEqual(UpperCamelCase__				,		UpperCamelCase__      )

        UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
        self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

        tokenizer.add_special_tokens({"bos_token": "<s>"}      )
        UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
        self.assertNotEqual(UpperCamelCase__				,		UpperCamelCase__      )

        # Testing spaces after special tokens
        UpperCAmelCase         =     "<mask>"
        tokenizer.add_special_tokens(
            {"mask_token": AddedToken(UpperCamelCase__				,		lstrip=UpperCamelCase__				,		rstrip=UpperCamelCase__      )}      )  # mask token has a left space
        UpperCAmelCase         =     tokenizer.convert_tokens_to_ids(UpperCamelCase__      )

        UpperCAmelCase         =     "Encode <mask> sequence"
        UpperCAmelCase         =     "Encode <mask>sequence"

        UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__      )
        UpperCAmelCase         =     encoded.index(UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
        self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

        UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__      )
        UpperCAmelCase         =     encoded.index(UpperCamelCase__      )
        UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
        self.assertNotEqual(UpperCamelCase__				,		UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
        '''simple docstring'''







        pass






  def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
        '''simple docstring'''







        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(UpperCamelCase__				,		**UpperCamelCase__      )
                    UpperCAmelCase         =     self.tokenizer_class.from_pretrained(UpperCamelCase__				,		**UpperCamelCase__      )
                    UpperCAmelCase         =     "A, <mask> AllenNLP sentence."
                    UpperCAmelCase         =     tokenizer_r.encode_plus(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__				,		return_token_type_ids=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_p.encode_plus(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__				,		return_token_type_ids=UpperCamelCase__      )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r["token_type_ids"]      )				,		sum(tokens_p["token_type_ids"]      )      )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r["attention_mask"]      ) / len(tokens_r["attention_mask"]      )				,		sum(tokens_p["attention_mask"]      ) / len(tokens_p["attention_mask"]      )				,		)

                    UpperCAmelCase         =     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
                    UpperCAmelCase         =     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]      )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p["input_ids"]				,		[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
                    self.assertSequenceEqual(tokens_r["input_ids"]				,		[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

                    self.assertSequenceEqual(
                        UpperCamelCase__				,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )
                    self.assertSequenceEqual(
                        UpperCamelCase__				,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )






  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					int:
        '''simple docstring'''







        for trim_offsets, add_prefix_space in itertools.product([True, False]				,		repeat=2      ):
              UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                  self.tmpdirname				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )

              UpperCAmelCase         =     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
              UpperCAmelCase         =     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

              self.assertEqual(pre_tokenizer_state["add_prefix_space"]				,		UpperCamelCase__      )

              self.assertEqual(post_processor_state["add_prefix_space"]				,		UpperCamelCase__      )
              self.assertEqual(post_processor_state["trim_offsets"]				,		UpperCamelCase__      )







  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
        '''simple docstring'''







        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                    UpperCAmelCase         =     "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                    UpperCAmelCase         =     F'{text_of_1_token} {text_of_1_token}'

                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                        UpperCamelCase__				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_r(UpperCamelCase__				,		return_offsets_mapping=UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
                    self.assertEqual(encoding.offset_mapping[0]				,		(0, len(UpperCamelCase__      ))      )
                    self.assertEqual(
                        encoding.offset_mapping[1]				,		(len(UpperCamelCase__      ) + 1, len(UpperCamelCase__      ) + 1 + len(UpperCamelCase__      ))				,		)

                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                        UpperCamelCase__				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_r(UpperCamelCase__				,		return_offsets_mapping=UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
                    self.assertEqual(encoding.offset_mapping[0]				,		(0, len(UpperCamelCase__      ))      )
                    self.assertEqual(
                        encoding.offset_mapping[1]				,		(len(UpperCamelCase__      ) + 1, len(UpperCamelCase__      ) + 1 + len(UpperCamelCase__      ))				,		)

                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                        UpperCamelCase__				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_r(UpperCamelCase__				,		return_offsets_mapping=UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
                    self.assertEqual(encoding.offset_mapping[0]				,		(0, len(UpperCamelCase__      ))      )
                    self.assertEqual(
                        encoding.offset_mapping[1]				,		(len(UpperCamelCase__      ), len(UpperCamelCase__      ) + 1 + len(UpperCamelCase__      ))				,		)

                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                        UpperCamelCase__				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_r(UpperCamelCase__				,		return_offsets_mapping=UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
                    self.assertEqual(encoding.offset_mapping[0]				,		(0, len(UpperCamelCase__      ))      )
                    self.assertEqual(
                        encoding.offset_mapping[1]				,		(len(UpperCamelCase__      ), len(UpperCamelCase__      ) + 1 + len(UpperCamelCase__      ))				,		)

                    UpperCAmelCase         =     F' {text}'

                    # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                    # )
                    # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                    # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                    # self.assertEqual(
                    #     encoding.offset_mapping[1],
                    #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                    # )

                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                        UpperCamelCase__				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_r(UpperCamelCase__				,		return_offsets_mapping=UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
                    self.assertEqual(encoding.offset_mapping[0]				,		(1, 1 + len(UpperCamelCase__      ))      )
                    self.assertEqual(
                        encoding.offset_mapping[1]				,		(1 + len(UpperCamelCase__      ) + 1, 1 + len(UpperCamelCase__      ) + 1 + len(UpperCamelCase__      ))				,		)

                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                        UpperCamelCase__				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_r(UpperCamelCase__				,		return_offsets_mapping=UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
                    self.assertEqual(encoding.offset_mapping[0]				,		(0, 1 + len(UpperCamelCase__      ))      )
                    self.assertEqual(
                        encoding.offset_mapping[1]				,		(1 + len(UpperCamelCase__      ), 1 + len(UpperCamelCase__      ) + 1 + len(UpperCamelCase__      ))				,		)

                    UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(
                        UpperCamelCase__				,		use_fast=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__				,		trim_offsets=UpperCamelCase__      )
                    UpperCAmelCase         =     tokenizer_r(UpperCamelCase__				,		return_offsets_mapping=UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
                    self.assertEqual(encoding.offset_mapping[0]				,		(0, 1 + len(UpperCamelCase__      ))      )
                    self.assertEqual(
                        encoding.offset_mapping[1]				,		(1 + len(UpperCamelCase__      ), 1 + len(UpperCamelCase__      ) + 1 + len(UpperCamelCase__      ))				,		)








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     :							Dict  							=				logging.get_logger(__name__)


__lowerCamelCase     :							Tuple  							=				["model.decoder.embed_positions.weights"]

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

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

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

						UpperCAmelCase         =     fairseq_model.lm.state_dict()
						UpperCAmelCase       ,		UpperCAmelCase         =     rename_state_dict(
						    lowerCamelCase_    ,   hidden_size=decoder_config.hidden_size    )

						UpperCAmelCase         =     TaEncoderModel.from_pretrained("t5-base"    )
						UpperCAmelCase         =     EncodecModel.from_pretrained("facebook/encodec_32khz"    )
						UpperCAmelCase         =     MusicgenForCausalLM(lowerCamelCase_    ).eval()

						# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
						UpperCAmelCase       ,		UpperCAmelCase         =     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         =     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         =     torch.arange(0    ,   8    ,   dtype=torch.long    ).reshape(2    ,   -1    )
						UpperCAmelCase         =     input_ids.reshape(2 * 4    ,   -1    )

						with torch.no_grad():
												UpperCAmelCase         =     model(input_ids=lowerCamelCase_    ,   decoder_input_ids=lowerCamelCase_    ).logits

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

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

						UpperCAmelCase         =     MusicgenProcessor(feature_extractor=lowerCamelCase_    ,   tokenizer=lowerCamelCase_    )

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

						# set other default generation config params
						UpperCAmelCase         =     int(30 * audio_encoder.config.frame_rate    )
						UpperCAmelCase         =     True
						UpperCAmelCase         =     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__":
		__lowerCamelCase     :							List[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."
		)

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



