



import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


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


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


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


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


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase="cpu"					)						->      int:
			A__       =    MusicGen.get_pretrained(_UpperCAmelCase ,	device=_UpperCAmelCase					)
			A__       =    decoder_config_from_checkpoint(_UpperCAmelCase					)

			A__       =    fairseq_model.lm.state_dict()
			A__     , A__       =    rename_state_dict(
			    _UpperCAmelCase ,	hidden_size=decoder_config.hidden_size					)

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

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

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

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

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

			# init the composite model
			A__       =    MusicgenForConditionalGeneration(text_encoder=_UpperCAmelCase ,	audio_encoder=_UpperCAmelCase ,	decoder=_UpperCAmelCase					)

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

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

			with torch.no_grad():
						A__       =    model(input_ids=_UpperCAmelCase ,	decoder_input_ids=_UpperCAmelCase					).logits

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

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

			A__       =    MusicgenProcessor(feature_extractor=_UpperCAmelCase ,	tokenizer=_UpperCAmelCase					)

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

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

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

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint''',
		    default='''small''',
		    type=str,
		    help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder''',
		    required=True,
		    default=None,
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)
		parser.add_argument(
		    '''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
		)

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









from typing import TYPE_CHECKING

from ..utils import _LazyModule


SCREAMING_SNAKE_CASE__       =      {
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
							from .config import (
							    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
							    OnnxConfig,
							    OnnxConfigWithPast,
							    OnnxSeqaSeqConfigWithPast,
							    PatchingSpec,
							)
							from .convert import export, validate_model_outputs
							from .features import FeaturesManager
							from .utils import ParameterFormat, compute_serialized_parameters_size

else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




