



"""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__		:    Tuple				   =     logging.get_logger(__name__)


lowercase__		:    List[str]				   =     ["""model.decoder.embed_positions.weights"""]

def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       str:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    OrderedDict   ,       lowerCAmelCase__				:    int						)						->       Tuple[Dict, Dict]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       MusicgenDecoderConfig:

  """simple docstring"""




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

@torch.no_grad()
def     UpperCamelCase_   (   lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    List[str]=None   ,       lowerCAmelCase__				:    Optional[int]=None   ,       lowerCAmelCase__				:    List[Any]="cpu"						)						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    Dict								=			MusicGen.get_pretrained(lowerCAmelCase__   ,       device=lowerCAmelCase__						)
  lowerCAmelCase_       :    str								=			decoder_config_from_checkpoint(lowerCAmelCase__						)

  lowerCAmelCase_       :    Any								=			fairseq_model.lm.state_dict()
  lowerCAmelCase_						,lowerCAmelCase_       :    Dict								=			rename_state_dict(
      lowerCAmelCase__   ,       hidden_size=decoder_config.hidden_size						)

  lowerCAmelCase_       :    Any								=			TaEncoderModel.from_pretrained('t5-base'						)
  lowerCAmelCase_       :    Optional[Any]								=			EncodecModel.from_pretrained('facebook/encodec_32khz'						)
  lowerCAmelCase_       :    Any								=			MusicgenForCausalLM(lowerCAmelCase__						).eval()

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

  with torch.no_grad():
    lowerCAmelCase_       :    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
  lowerCAmelCase_       :    int								=			AutoTokenizer.from_pretrained('t5-base'						)
  lowerCAmelCase_       :    int								=			AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'   ,       padding_side='left'						)

  lowerCAmelCase_       :    str								=			MusicgenProcessor(feature_extractor=lowerCAmelCase__   ,       tokenizer=lowerCAmelCase__						)

  # set the appropriate bos/pad token ids
  lowerCAmelCase_       :    Dict								=			2048
  lowerCAmelCase_       :    Dict								=			2048

  # set other default generation config params
  lowerCAmelCase_       :    Optional[Any]								=			int(30 * audio_encoder.config.frame_rate						)
  lowerCAmelCase_       :    Any								=			True
  lowerCAmelCase_       :    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__":
     lowercase__		:    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."""
     )

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










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    list[int]   ,       lowerCAmelCase__				:    list[int]						)						->       None:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			len(lowerCAmelCase__						)
  print('The following activities are selected:'						)

  # The first activity is always selected
  lowerCAmelCase_       :    str								=			0
  print(lowerCAmelCase__   ,       end=','						)

  # Consider rest of the activities
  for j in range(lowerCAmelCase__						):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(lowerCAmelCase__   ,       end=','						)
      lowerCAmelCase_       :    Tuple								=			j


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     lowercase__		:    List[str]				   =     [1, 3, 0, 5, 8, 5]
     lowercase__		:    Dict				   =     [2, 4, 6, 7, 9, 9]
     print_max_activities(start, finish)





