'''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		:				List[Any]	       =		logging.get_logger(__name__)


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




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




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




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




@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Dict=None   ,   UpperCAmelCase__ :      Dict=None   ,   UpperCAmelCase__ :      Dict="cpu"				)    ->   List[Any]:
							lowercase_      :    List[str]       		= MusicGen.get_pretrained(UpperCAmelCase__   ,   device=UpperCAmelCase__				)
							lowercase_      :    Optional[Any]       		= decoder_config_from_checkpoint(UpperCAmelCase__				)

							lowercase_      :    Union[str, Any]       		= fairseq_model.lm.state_dict()
							lowercase_      :    Optional[int]       		= rename_state_dict(
							    UpperCAmelCase__   ,   hidden_size=decoder_config.hidden_size				)

							lowercase_      :    int       		= TaEncoderModel.from_pretrained("""t5-base"""				)
							lowercase_      :    Tuple       		= EncodecModel.from_pretrained("""facebook/encodec_32khz"""				)
							lowercase_      :    Optional[Any]       		= MusicgenForCausalLM(UpperCAmelCase__				).eval()

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

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

							lowercase_      :    Optional[int]       		= MusicgenProcessor(feature_extractor=UpperCAmelCase__   ,   tokenizer=UpperCAmelCase__				)

							# set the appropriate bos/pad token ids
							lowercase_      :    int       		= 2048
							lowercase_      :    Union[str, Any]       		= 2048

							# set other default generation config params
							lowercase_      :    List[str]       		= int(30 * audio_encoder.config.frame_rate				)
							lowercase_      :    List[Any]       		= True
							lowercase_      :    Union[str, Any]       		= 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__":
						_lowercase		:				Dict	       =		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		:				Tuple	       =		parser.parse_args()
						convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






'''simple docstring'''




import argparse


_lowercase		:				Optional[int]	       =		"docs/source/_static/js/custom.js"




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple				)    ->   Dict:
							with open(UpperCAmelCase__   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														lowercase_      :    Optional[int]       		= f.readlines()
							lowercase_      :    Tuple       		= 0

							# First let's put the right version
							while not lines[index].startswith("""const stableVersion ="""				):
														index += 1
							lowercase_      :    Optional[Any]       		= F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith("""const versionMapping = {"""				):
														index += 1

							# We go until the end
							while not lines[index].startswith("""}"""				):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCAmelCase__   ,   """w"""   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														f.writelines(UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				Union[str, Any]	       =		argparse.ArgumentParser()
						parser.add_argument("--version", help="Release version.")
						_lowercase		:				Dict	       =		parser.parse_args()
						update_custom_js(args.version)





