





"""simple docstring"""






import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


A			      =	parse(importlib.metadata.version('''torch'''))

def      __A   (      a_    :Union[str, Version] ,							a_    :str ,							a_    :str)		->		Dict:
				if operation not in STR_OPERATION_TO_FUNC.keys():
								raise ValueError(F"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}, received {operation}""")
				__a			:    int      							=	STR_OPERATION_TO_FUNC[operation]
				if isinstance(a_ ,							a_):
								__a			:    List[str]      							=	parse(importlib.metadata.version(a_))
				return operation(a_ ,							parse(a_))






def      __A   (      a_    :str ,							a_    :str)		->		int:
				return compare_versions(a_ ,							a_ ,							a_)






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_   :      Optional[Any]			=   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_   :      Any			=   ['''model.decoder.embed_positions.weights''']



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



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



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



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=None						,   snake_case="cpu"							)		->    Optional[int]:
		__lowercase											=     MusicGen.get_pretrained(snake_case						,   device=snake_case							)
		__lowercase											=     decoder_config_from_checkpoint(snake_case							)

		__lowercase											=     fairseq_model.lm.state_dict()
		__lowercase						,      __lowercase											=     rename_state_dict(
		    snake_case						,   hidden_size=decoder_config.hidden_size							)

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

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

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

		if len(snake_case							) > 0:
				raise ValueError(F"Missing key(s) in state_dict: {missing_keys}"							)

		if len(snake_case							) > 0:
				raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}"							)

		# init the composite model
		__lowercase											=     MusicgenForConditionalGeneration(text_encoder=snake_case						,   audio_encoder=snake_case						,   decoder=snake_case							)

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

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

		with torch.no_grad():
				__lowercase											=     model(input_ids=snake_case						,   decoder_input_ids=snake_case							).logits

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

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

		__lowercase											=     MusicgenProcessor(feature_extractor=snake_case						,   tokenizer=snake_case							)

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

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

		if pytorch_dump_folder is not None:
				Path(snake_case							).mkdir(exist_ok=snake_case							)
				logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}"							)
				model.save_pretrained(snake_case							)
				processor.save_pretrained(snake_case							)

		if repo_id:
				logger.info(F"Pushing model {checkpoint} to {repo_id}"							)
				model.push_to_hub(snake_case							)
				processor.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   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_   :      Any			=   parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




