
'''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()
_lowercase          =						logging.get_logger(__name__)


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


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


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


def A (__lowerCamelCase :str						):
						if checkpoint == "small":
												# default config values
												_lowerCAmelCase    		=					1024
												_lowerCAmelCase    		=					24
												_lowerCAmelCase    		=					16
						elif checkpoint == "medium":
												_lowerCAmelCase    		=					1536
												_lowerCAmelCase    		=					48
												_lowerCAmelCase    		=					24
						elif checkpoint == "large":
												_lowerCAmelCase    		=					2048
												_lowerCAmelCase    		=					48
												_lowerCAmelCase    		=					32
						else:
												raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'						)
						_lowerCAmelCase    		=					MusicgenDecoderConfig(
						    hidden_size=__lowerCamelCase					,      ffn_dim=hidden_size * 4					,      num_hidden_layers=__lowerCamelCase					,      num_attention_heads=__lowerCamelCase					,      )
						return config


@torch.no_grad()
def A (__lowerCamelCase :str					,      __lowerCamelCase :Union[str, Any]=None					,      __lowerCamelCase :List[str]=None					,      __lowerCamelCase :Optional[int]="cpu"						):
						_lowerCAmelCase    		=					MusicGen.get_pretrained(__lowerCamelCase					,      device=__lowerCamelCase						)
						_lowerCAmelCase    		=					decoder_config_from_checkpoint(__lowerCamelCase						)

						_lowerCAmelCase    		=					fairseq_model.lm.state_dict()
						_lowerCAmelCase		,       _lowerCAmelCase    		=					rename_state_dict(
						    __lowerCamelCase					,      hidden_size=decoder_config.hidden_size						)

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

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

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

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

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

						# init the composite model
						_lowerCAmelCase    		=					MusicgenForConditionalGeneration(text_encoder=__lowerCamelCase					,      audio_encoder=__lowerCamelCase					,      decoder=__lowerCamelCase						)

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

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

						with torch.no_grad():
												_lowerCAmelCase    		=					model(input_ids=__lowerCamelCase					,      decoder_input_ids=__lowerCamelCase						).logits

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

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

						_lowerCAmelCase    		=					MusicgenProcessor(feature_extractor=__lowerCamelCase					,      tokenizer=__lowerCamelCase						)

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

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

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

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


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)




'''simple docstring'''






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__UpperCAmelCase       							=				TypeVar('''KEY''')
__UpperCAmelCase       							=				TypeVar('''VAL''')




@dataclass(frozen=a__							,							slots=a__    )
class 						a__							(				Generic[KEY, VAL]    ):





							'''simple docstring'''






							lowercase__     :  KEY
							lowercase__     :  VAL




class 						a__							(				_Item    ):





							'''simple docstring'''






							def __init__(						self  )			->				None:
														super().__init__(lowerCamelCase_   ,       lowerCamelCase_  )







							def __bool__(						self  )			->				bool:
														return False

__UpperCAmelCase       							=				_DeletedItem()




class 						a__							(				MutableMapping[KEY, VAL]    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_ = 8   ,       lowerCamelCase_ = 0.75  )			->				None:
														lowerCAmelCase__			  =      initial_block_size
														lowerCAmelCase__			  =      [None] * initial_block_size
														assert 0.0 < capacity_factor < 1.0
														lowerCAmelCase__			  =      capacity_factor
														lowerCAmelCase__			  =      0







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return hash(lowerCamelCase_  ) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return (ind + 1) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				bool:
														lowerCAmelCase__			  =      self._buckets[ind]
														if not stored:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					self._len += 1
																					return True
														elif stored.key == key:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					return True
														else:
																					return False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor
														return len(self  ) >= int(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														if len(self._buckets  ) <= self._initial_block_size:
																					return False
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor / 2
														return len(self  ) < limit







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				None:
														lowerCAmelCase__			  =      self._buckets
														lowerCAmelCase__			  =      [None] * new_size
														lowerCAmelCase__			  =      0
														for item in old_buckets:
																					if item:
																												self._add_item(item.key   ,       item.val  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) * 2  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) // 2  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Iterator[int]:
														lowerCAmelCase__			  =      self._get_bucket_index(lowerCamelCase_  )
														for _ in range(len(self._buckets  )  ):
																					yield ind
																					lowerCAmelCase__			  =      self._get_next_ind(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					if self._try_set(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																												break







							def __setitem__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														if self._is_full():
																					self._size_up()

														self._add_item(lowerCamelCase_   ,       lowerCamelCase_  )







							def __delitem__(						self   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												raise KeyError(lowerCamelCase_  )
																					if item is _deleted:
																												continue
																					if item.key == key:
																												lowerCAmelCase__			  =      _deleted
																												self._len -= 1
																												break
														if self._is_sparse():
																					self._size_down()







							def __getitem__(						self   ,       lowerCamelCase_  )			->				VAL:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												break
																					if item is _deleted:
																												continue
																					if item.key == key:
																												return item.val
														raise KeyError(lowerCamelCase_  )







							def __len__(						self  )			->				int:
														return self._len







							def __iter__(						self  )			->				Iterator[KEY]:
														yield from (item.key for item in self._buckets if item)







							def __repr__(						self  )			->				str:
														lowerCAmelCase__			  =      ''' ,'''.join(
														    F"""{item.key}: {item.val}""" for item in self._buckets if item  )
														return F"""HashMap({val_string})"""