



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()
__snake_case		:str   	=  logging.get_logger(__name__)


__snake_case		:str   	=  ['''model.decoder.embed_positions.weights''']





def   __snake_case			(    _UpperCAmelCase       ):
				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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
				__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   __snake_case			(    _UpperCAmelCase       ):
				if checkpoint == "small":
								# default config values
								__a		 =       1024
								__a		 =       24
								__a		 =       16
				elif checkpoint == "medium":
								__a		 =       1536
								__a		 =       48
								__a		 =       24
				elif checkpoint == "large":
								__a		 =       2048
								__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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase="cpu"       ):
				__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, 2048):
								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		 =       2048
				__a		 =       2048

				# 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__":
	__snake_case		: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.'''
	)

	__snake_case		:Optional[Any]   	=  parser.parse_args()
	convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)










# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


__snake_case		:List[str]   	=  re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')

@total_ordering
@dataclass
class 					_A :
       UpperCamelCase__						:					str
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a ,     __a ,     __a		 =       _str_to_version_tuple(self.version_str)



       def __repr__(	self							:  Tuple):


           '''simple docstring'''

           return F'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'



       @property
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           return self.major, self.minor, self.patch



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               return Version(__SCREAMING_SNAKE_CASE)
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               return other
           raise TypeError(F'{other} (type {type(__SCREAMING_SNAKE_CASE)}) cannot be compared to version.')



       def __eq__(	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           try:
               __a		 =       self._validate_operand(__SCREAMING_SNAKE_CASE)
           except (TypeError, ValueError):
               return False
           else:
               return self.tuple == other.tuple



       def __lt__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       self._validate_operand(__SCREAMING_SNAKE_CASE)
           return self.tuple < other.tuple



       def __hash__(	self							:  Optional[Any]):


           '''simple docstring'''

           return hash(_version_tuple_to_str(self.tuple))



       @classmethod
       def 	_lowerCamelCase    (	cls							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       {f.name for f in dataclasses.fields(cls)}
           return cls(**{k: v for k, v in dic.items() if k in field_names})





       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.version_str





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       _VERSION_REG.match(_UpperCAmelCase       )
    if not res:
        raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
    return tuple(int(_UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )





def   __snake_case			(    _UpperCAmelCase       ):
    return ".".join(str(_UpperCAmelCase       ) for v in version_tuple       )





