




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_		:					List[str]          =  logging.get_logger(__name__)


UpperCAmelCase_		:					Optional[Any]          =  ["model.decoder.embed_positions.weights"]




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




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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if checkpoint == "small":
								# default config values
								__magic_name__						:						int										=1024
								__magic_name__						:						str										=24
								__magic_name__						:						int										=16
				elif checkpoint == "medium":
								__magic_name__						:						int										=1536
								__magic_name__						:						List[Any]										=48
								__magic_name__						:						List[str]										=24
				elif checkpoint == "large":
								__magic_name__						:						List[Any]										=2048
								__magic_name__						:						Any										=48
								__magic_name__						:						Union[str, Any]										=32
				else:
								raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}." )
				__magic_name__						:						Tuple										=MusicgenDecoderConfig(
				    hidden_size=lowerCamelCase						,  ffn_dim=hidden_size * 4						,  num_hidden_layers=lowerCamelCase						,  num_attention_heads=lowerCamelCase						,  )
				return config




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=None						,  lowerCamelCase="cpu" ):
				__magic_name__						:						str										=MusicGen.get_pretrained(lowerCamelCase						,  device=lowerCamelCase )
				__magic_name__						:						List[Any]										=decoder_config_from_checkpoint(lowerCamelCase )

				__magic_name__						:						int										=fairseq_model.lm.state_dict()
				__magic_name__					,			__magic_name__						:						Optional[int]										=rename_state_dict(
				    lowerCamelCase						,  hidden_size=decoder_config.hidden_size )

				__magic_name__						:						List[Any]										=TaEncoderModel.from_pretrained("""t5-base""" )
				__magic_name__						:						List[Any]										=EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
				__magic_name__						:						Any										=MusicgenForCausalLM(lowerCamelCase ).eval()

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

				with torch.no_grad():
								__magic_name__						:						int										=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
				__magic_name__						:						Dict										=AutoTokenizer.from_pretrained("""t5-base""" )
				__magic_name__						:						List[Any]										=AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""						,  padding_side="""left""" )

				__magic_name__						:						Optional[int]										=MusicgenProcessor(feature_extractor=lowerCamelCase						,  tokenizer=lowerCamelCase )

				# set the appropriate bos/pad token ids
				__magic_name__						:						List[str]										=2048
				__magic_name__						:						Optional[int]										=2048

				# set other default generation config params
				__magic_name__						:						str										=int(30 * audio_encoder.config.frame_rate )
				__magic_name__						:						Union[str, Any]										=True
				__magic_name__						:						Any										=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__":
							UpperCAmelCase_		:					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."
							)

							UpperCAmelCase_		:					Tuple          =  parser.parse_args()
							convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




