


"""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()
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    int		=    ['model.decoder.embed_positions.weights']







def     _a				(      _SCREAMING_SNAKE_CASE				)    ->				str:
							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     _a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple[Dict, Dict]:
							snake_case_           =		list(state_dict.keys()				)
							snake_case_           =		{}
							for key in keys:
														snake_case_           =		state_dict.pop(_SCREAMING_SNAKE_CASE				)
														snake_case_           =		rename_keys(_SCREAMING_SNAKE_CASE				)
														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     _a				(      _SCREAMING_SNAKE_CASE				)    ->				MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														snake_case_           =		1_024
														snake_case_           =		24
														snake_case_           =		16
							elif checkpoint == "medium":
														snake_case_           =		1_536
														snake_case_           =		48
														snake_case_           =		24
							elif checkpoint == "large":
														snake_case_           =		2_048
														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=_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     _a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE="cpu"				)    ->				Tuple:
							snake_case_           =		MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE				, device=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE				)

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

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

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							snake_case_       ,						snake_case_           =		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
							snake_case_           =		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
							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=_SCREAMING_SNAKE_CASE				, decoder_input_ids=_SCREAMING_SNAKE_CASE				).logits

							if logits.shape != (8, 1, 2_048):
														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=_SCREAMING_SNAKE_CASE				, tokenizer=_SCREAMING_SNAKE_CASE				)

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

							# 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(_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__":
				__SCREAMING_SNAKE_CASE							:    Tuple		=    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							:    int		=    parser.parse_args()
				convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)







"""simple docstring"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



