






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()
lowercase  							=					logging.get_logger(__name__)


lowercase  							=					["model.decoder.embed_positions.weights"]





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





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





def 						__UpperCAmelCase			(     a_):
							if checkpoint == "small":
														# default config values
														snake_case_				=							10_24
														snake_case_				=							24
														snake_case_				=							16
							elif checkpoint == "medium":
														snake_case_				=							15_36
														snake_case_				=							48
														snake_case_				=							24
							elif checkpoint == "large":
														snake_case_				=							20_48
														snake_case_				=							48
														snake_case_				=							32
							else:
														raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''')
							snake_case_				=							MusicgenDecoderConfig(
							    hidden_size=a_							,     ffn_dim=hidden_size * 4							,     num_hidden_layers=a_							,     num_attention_heads=a_							,     )
							return config





@torch.no_grad()
def 						__UpperCAmelCase			(     a_							,     a_=None							,     a_=None							,     a_="cpu"):
							snake_case_				=							MusicGen.get_pretrained(a_							,     device=a_)
							snake_case_				=							decoder_config_from_checkpoint(a_)

							snake_case_				=							fairseq_model.lm.state_dict()
							snake_case_			,		snake_case_				=							rename_state_dict(
							    a_							,     hidden_size=decoder_config.hidden_size)

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

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

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

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

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

							# init the composite model
							snake_case_				=							MusicgenForConditionalGeneration(text_encoder=a_							,     audio_encoder=a_							,     decoder=a_)

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

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

							with torch.no_grad():
														snake_case_				=							model(input_ids=a_							,     decoder_input_ids=a_).logits

							if logits.shape != (8, 1, 20_48):
														raise ValueError('Incorrect shape for logits')

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

							snake_case_				=							MusicgenProcessor(feature_extractor=a_							,     tokenizer=a_)

							# set the appropriate bos/pad token ids
							snake_case_				=							20_48
							snake_case_				=							20_48

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

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

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


if __name__ == "__main__":
	lowercase  							=					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."
	)

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













from __future__ import annotations





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_):  # noqa: E741
							while r - l > 1:
														snake_case_				=							(l + r) // 2
														if v[m] >= key:
																					snake_case_				=							m
														else:
																					snake_case_				=							m  # noqa: E741
							return r





def 						__UpperCAmelCase			(     a_):
							if len(a_) == 0:
														return 0

							snake_case_				=							[0] * len(a_)
							snake_case_				=							1

							snake_case_				=							v[0]

							for i in range(1							,     len(a_)):
														if v[i] < tail[0]:
																					snake_case_				=							v[i]
														elif v[i] > tail[length - 1]:
																					snake_case_				=							v[i]
																					length += 1
														else:
																					snake_case_				=							v[i]

							return length


if __name__ == "__main__":
	import doctest

	doctest.testmod()





