




"""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()
UpperCamelCase__						=			logging.get_logger(__name__)


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





def 							UpperCAmelCase		(		snake_case			:					List[Any]    ):
					if "emb" in name:
										_lowerCAmelCase:str									=  name.replace('''emb'''      ,       '''model.decoder.embed_tokens'''    )
					if "transformer" in name:
										_lowerCAmelCase:Optional[Any]									=  name.replace('''transformer'''      ,       '''model.decoder'''    )
					if "cross_attention" in name:
										_lowerCAmelCase:int									=  name.replace('''cross_attention'''      ,       '''encoder_attn'''    )
					if "linear1" in name:
										_lowerCAmelCase:List[Any]									=  name.replace('''linear1'''      ,       '''fc1'''    )
					if "linear2" in name:
										_lowerCAmelCase:Union[str, Any]									=  name.replace('''linear2'''      ,       '''fc2'''    )
					if "norm1" in name:
										_lowerCAmelCase:Tuple									=  name.replace('''norm1'''      ,       '''self_attn_layer_norm'''    )
					if "norm_cross" in name:
										_lowerCAmelCase:Any									=  name.replace('''norm_cross'''      ,       '''encoder_attn_layer_norm'''    )
					if "norm2" in name:
										_lowerCAmelCase:List[Any]									=  name.replace('''norm2'''      ,       '''final_layer_norm'''    )
					if "out_norm" in name:
										_lowerCAmelCase:Optional[int]									=  name.replace('''out_norm'''      ,       '''model.decoder.layer_norm'''    )
					if "linears" in name:
										_lowerCAmelCase:Tuple									=  name.replace('''linears'''      ,       '''lm_heads'''    )
					if "condition_provider.conditioners.description.output_proj" in name:
										_lowerCAmelCase:str									=  name.replace('''condition_provider.conditioners.description.output_proj'''      ,       '''enc_to_dec_proj'''    )
					return name





def 							UpperCAmelCase		(		snake_case			:					OrderedDict      ,       snake_case			:					int    ):
					_lowerCAmelCase:Union[str, Any]									=  list(state_dict.keys()    )
					_lowerCAmelCase:Dict									=  {}
					for key in keys:
										_lowerCAmelCase:str									=  state_dict.pop(snake_case    )
										_lowerCAmelCase:Optional[int]									=  rename_keys(snake_case    )
										if "in_proj_weight" in key:
															# split fused qkv proj
															_lowerCAmelCase:Tuple									=  val[:hidden_size, :]
															_lowerCAmelCase:List[str]									=  val[hidden_size : 2 * hidden_size, :]
															_lowerCAmelCase:Dict									=  val[-hidden_size:, :]
										elif "enc_to_dec_proj" in key:
															_lowerCAmelCase:Optional[Any]									=  val
										else:
															_lowerCAmelCase:Tuple									=  val
					return state_dict, enc_dec_proj_state_dict





def 							UpperCAmelCase		(		snake_case			:					str    ):
					if checkpoint == "small":
										# default config values
										_lowerCAmelCase:Optional[int]									=  1024
										_lowerCAmelCase:Tuple									=  24
										_lowerCAmelCase:int									=  16
					elif checkpoint == "medium":
										_lowerCAmelCase:Optional[int]									=  1536
										_lowerCAmelCase:Optional[int]									=  48
										_lowerCAmelCase:List[Any]									=  24
					elif checkpoint == "large":
										_lowerCAmelCase:int									=  2048
										_lowerCAmelCase:int									=  48
										_lowerCAmelCase:Union[str, Any]									=  32
					else:
										raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'    )
					_lowerCAmelCase:Union[str, Any]									=  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 							UpperCAmelCase		(		snake_case			:					Dict      ,       snake_case			:					List[Any]=None      ,       snake_case			:					List[str]=None      ,       snake_case			:					Optional[Any]="cpu"    ):
					_lowerCAmelCase:List[str]									=  MusicGen.get_pretrained(snake_case      ,       device=snake_case    )
					_lowerCAmelCase:Optional[int]									=  decoder_config_from_checkpoint(snake_case    )

					_lowerCAmelCase:List[Any]									=  fairseq_model.lm.state_dict()
					_lowerCAmelCase:int									=  rename_state_dict(
					    snake_case      ,       hidden_size=decoder_config.hidden_size    )

					_lowerCAmelCase:Union[str, Any]									=  TaEncoderModel.from_pretrained('''t5-base'''    )
					_lowerCAmelCase:List[Any]									=  EncodecModel.from_pretrained('''facebook/encodec_32khz'''    )
					_lowerCAmelCase:int									=  MusicgenForCausalLM(snake_case    ).eval()

					# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
					_lowerCAmelCase:str									=  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
					_lowerCAmelCase:Dict									=  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
					_lowerCAmelCase:List[Any]									=  torch.arange(0      ,       8      ,       dtype=torch.long    ).reshape(2      ,       -1    )
					_lowerCAmelCase:List[str]									=  input_ids.reshape(2 * 4      ,       -1    )

					with torch.no_grad():
										_lowerCAmelCase:Union[str, Any]									=  model(input_ids=snake_case      ,       decoder_input_ids=snake_case    ).logits

					if logits.shape != (8, 1, 2048):
										raise ValueError('''Incorrect shape for logits'''    )

					# now construct the processor
					_lowerCAmelCase:int									=  AutoTokenizer.from_pretrained('''t5-base'''    )
					_lowerCAmelCase:Optional[int]									=  AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''      ,       padding_side='''left'''    )

					_lowerCAmelCase:Tuple									=  MusicgenProcessor(feature_extractor=snake_case      ,       tokenizer=snake_case    )

					# set the appropriate bos/pad token ids
					_lowerCAmelCase:Any									=  2048
					_lowerCAmelCase:str									=  2048

					# set other default generation config params
					_lowerCAmelCase:int									=  int(30 * audio_encoder.config.frame_rate    )
					_lowerCAmelCase:Optional[int]									=  True
					_lowerCAmelCase:Tuple									=  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__":
							UpperCamelCase__						=			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.'''
							)

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












"""simple docstring"""






from ...configuration_utils import PretrainedConfig




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''bert-generation'''





						def __init__(							self      :	Dict     ,a__      :	str=5_0358     ,a__      :	List[str]=1024     ,a__      :	int=24     ,a__      :	Optional[Any]=16     ,a__      :	List[str]=4096     ,a__      :	Optional[int]="gelu"     ,a__      :	str=0.1     ,a__      :	Union[str, Any]=0.1     ,a__      :	int=512     ,a__      :	Dict=0.02     ,a__      :	List[Any]=1E-12     ,a__      :	List[Any]=0     ,a__      :	int=2     ,a__      :	str=1     ,a__      :	Dict="absolute"     ,a__      :	int=True     ,**a__      :	List[str]     ,)						->       List[Any]:




											"""simple docstring"""







											super().__init__(pad_token_id=a__     ,bos_token_id=a__     ,eos_token_id=a__     ,**a__)

											_lowerCAmelCase:Optional[Any]									=  vocab_size
											_lowerCAmelCase:Union[str, Any]									=  hidden_size
											_lowerCAmelCase:Any									=  num_hidden_layers
											_lowerCAmelCase:int									=  num_attention_heads
											_lowerCAmelCase:int									=  hidden_act
											_lowerCAmelCase:List[Any]									=  intermediate_size
											_lowerCAmelCase:Optional[Any]									=  hidden_dropout_prob
											_lowerCAmelCase:int									=  attention_probs_dropout_prob
											_lowerCAmelCase:Optional[int]									=  max_position_embeddings
											_lowerCAmelCase:Dict									=  initializer_range
											_lowerCAmelCase:Union[str, Any]									=  layer_norm_eps
											_lowerCAmelCase:int									=  position_embedding_type
											_lowerCAmelCase:Tuple									=  use_cache






