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


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



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Any:







       """simple docstring"""
       if "emb" in name:
              UpperCamelCase					 =		name.replace("emb"		,      "model.decoder.embed_tokens"				)
       if "transformer" in name:
              UpperCamelCase					 =		name.replace("transformer"		,      "model.decoder"				)
       if "cross_attention" in name:
              UpperCamelCase					 =		name.replace("cross_attention"		,      "encoder_attn"				)
       if "linear1" in name:
              UpperCamelCase					 =		name.replace("linear1"		,      "fc1"				)
       if "linear2" in name:
              UpperCamelCase					 =		name.replace("linear2"		,      "fc2"				)
       if "norm1" in name:
              UpperCamelCase					 =		name.replace("norm1"		,      "self_attn_layer_norm"				)
       if "norm_cross" in name:
              UpperCamelCase					 =		name.replace("norm_cross"		,      "encoder_attn_layer_norm"				)
       if "norm2" in name:
              UpperCamelCase					 =		name.replace("norm2"		,      "final_layer_norm"				)
       if "out_norm" in name:
              UpperCamelCase					 =		name.replace("out_norm"		,      "model.decoder.layer_norm"				)
       if "linears" in name:
              UpperCamelCase					 =		name.replace("linears"		,      "lm_heads"				)
       if "condition_provider.conditioners.description.output_proj" in name:
              UpperCamelCase					 =		name.replace("condition_provider.conditioners.description.output_proj"		,      "enc_to_dec_proj"				)
       return name



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Tuple[Dict, Dict]:







       """simple docstring"""
       UpperCamelCase					 =		list(state_dict.keys()				)
       UpperCamelCase					 =		{}
       for key in keys:
              UpperCamelCase					 =		state_dict.pop(UpperCAmelCase_				)
              UpperCamelCase					 =		rename_keys(UpperCAmelCase_				)
              if "in_proj_weight" in key:
                     # split fused qkv proj
                     UpperCamelCase					 =		val[:hidden_size, :]
                     UpperCamelCase					 =		val[hidden_size : 2 * hidden_size, :]
                     UpperCamelCase					 =		val[-hidden_size:, :]
              elif "enc_to_dec_proj" in key:
                     UpperCamelCase					 =		val
              else:
                     UpperCamelCase					 =		val
       return state_dict, enc_dec_proj_state_dict



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							MusicgenDecoderConfig:







       """simple docstring"""
       if checkpoint == "small":
              # default config values
              UpperCamelCase					 =		10_24
              UpperCamelCase					 =		24
              UpperCamelCase					 =		16
       elif checkpoint == "medium":
              UpperCamelCase					 =		15_36
              UpperCamelCase					 =		48
              UpperCamelCase					 =		24
       elif checkpoint == "large":
              UpperCamelCase					 =		20_48
              UpperCamelCase					 =		48
              UpperCamelCase					 =		32
       else:
              raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."				)
       UpperCamelCase					 =		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_		,      UpperCAmelCase_=None		,      UpperCAmelCase_=None		,      UpperCAmelCase_="cpu"				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		MusicGen.get_pretrained(UpperCAmelCase_		,      device=UpperCAmelCase_				)
       UpperCamelCase					 =		decoder_config_from_checkpoint(UpperCAmelCase_				)

       UpperCamelCase					 =		fairseq_model.lm.state_dict()
       UpperCamelCase ,       UpperCamelCase					 =		rename_state_dict(
           UpperCAmelCase_		,      hidden_size=decoder_config.hidden_size				)

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

       # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
       UpperCamelCase ,       UpperCamelCase					 =		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
       UpperCamelCase					 =		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
       UpperCamelCase					 =		torch.arange(0		,      8		,      dtype=torch.long				).reshape(2		,      -1				)
       UpperCamelCase					 =		input_ids.reshape(2 * 4		,      -1				)

       with torch.no_grad():
              UpperCamelCase					 =		model(input_ids=UpperCAmelCase_		,      decoder_input_ids=UpperCAmelCase_				).logits

       if logits.shape != (8, 1, 20_48):
              raise ValueError("Incorrect shape for logits"				)

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

       UpperCamelCase					 =		MusicgenProcessor(feature_extractor=UpperCAmelCase_		,      tokenizer=UpperCAmelCase_				)

       # set the appropriate bos/pad token ids
       UpperCamelCase					 =		20_48
       UpperCamelCase					 =		20_48

       # set other default generation config params
       UpperCamelCase					 =		int(30 * audio_encoder.config.frame_rate				)
       UpperCamelCase					 =		True
       UpperCamelCase					 =		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__":
      SCREAMING_SNAKE_CASE      =     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."""
      )

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







"""simple docstring"""


import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


SCREAMING_SNAKE_CASE      =     4
SCREAMING_SNAKE_CASE      =     3



class   __a  (      _lowerCAmelCase	):
       pass





def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       for shard in shards:
              for i in range(UpperCAmelCase_				):
                     yield {"i": i, "shard": shard}



def 	lowerCamelCase__			(							)->							Union[str, Any]:







       """simple docstring"""
       UpperCamelCase					 =		int(os.environ["RANK"]				)
       UpperCamelCase					 =		int(os.environ["WORLD_SIZE"]				)

       UpperCamelCase					 =		ArgumentParser()
       parser.add_argument("--streaming"		,      type=UpperCAmelCase_				)
       parser.add_argument("--local_rank"		,      type=UpperCAmelCase_				)
       parser.add_argument("--num_workers"		,      type=UpperCAmelCase_		,      default=0				)
       UpperCamelCase					 =		parser.parse_args()
       UpperCamelCase					 =		args.streaming
       UpperCamelCase					 =		args.num_workers

       UpperCamelCase					 =		{"shards": [F"shard_{shard_idx}" for shard_idx in range(UpperCAmelCase_				)]}
       UpperCamelCase					 =		IterableDataset.from_generator(UpperCAmelCase_		,      gen_kwargs=UpperCAmelCase_				)
       if not streaming:
              UpperCamelCase					 =		Dataset.from_list(list(UpperCAmelCase_				)				)

       UpperCamelCase					 =		split_dataset_by_node(UpperCAmelCase_		,      rank=UpperCAmelCase_		,      world_size=UpperCAmelCase_				)
       UpperCamelCase					 =		torch.utils.data.DataLoader(UpperCAmelCase_		,      num_workers=UpperCAmelCase_				)

       UpperCamelCase					 =		NUM_SHARDS * NUM_ITEMS_PER_SHARD
       UpperCamelCase					 =		full_size // world_size
       expected_local_size += int(rank < (full_size % world_size)				)

       UpperCamelCase					 =		sum(1 for _ in dataloader				)
       if local_size != expected_local_size:
              raise FailedTestError(F"local_size {local_size} != expected_local_size {expected_local_size}"				)


if __name__ == "__main__":
      main()





