



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


UpperCAmelCase						:		List[Any]			= ["model.decoder.embed_positions.weights"]


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Tuple[Dict, Dict]:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						MusicgenDecoderConfig:







  '''simple docstring'''


  if checkpoint == "small":
    # default config values
    lowercase_								= 10_24
    lowercase_								= 24
    lowercase_								= 16
  elif checkpoint == "medium":
    lowercase_								= 15_36
    lowercase_								= 48
    lowercase_								= 24
  elif checkpoint == "large":
    lowercase_								= 20_48
    lowercase_								= 48
    lowercase_								= 32
  else:
    raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''							)
  lowercase_								= MusicgenDecoderConfig(
      hidden_size=__lowerCAmelCase ,  ffn_dim=hidden_size * 4 ,  num_hidden_layers=__lowerCAmelCase ,  num_attention_heads=__lowerCAmelCase ,  )
  return config


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase="cpu"							) ->						List[Any]:







  '''simple docstring'''


  lowercase_								= MusicGen.get_pretrained(__lowerCAmelCase ,  device=__lowerCAmelCase							)
  lowercase_								= decoder_config_from_checkpoint(__lowerCAmelCase							)

  lowercase_								= fairseq_model.lm.state_dict()
  lowercase_					,    lowercase_								= rename_state_dict(
      __lowerCAmelCase ,  hidden_size=decoder_config.hidden_size							)

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

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

  with torch.no_grad():
    lowercase_								= model(input_ids=__lowerCAmelCase ,  decoder_input_ids=__lowerCAmelCase							).logits

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

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

  lowercase_								= MusicgenProcessor(feature_extractor=__lowerCAmelCase ,  tokenizer=__lowerCAmelCase							)

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

  # set other default generation config params
  lowercase_								= int(30 * audio_encoder.config.frame_rate							)
  lowercase_								= True
  lowercase_								= 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						:		str			= 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						:		Optional[int]			= parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase						:		Any			= {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Any			= ["BlipImageProcessor"]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Optional[int]			= [
           "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlipModel",
           "BlipPreTrainedModel",
           "BlipForConditionalGeneration",
           "BlipForQuestionAnswering",
           "BlipVisionModel",
           "BlipTextModel",
           "BlipForImageTextRetrieval",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFBlipModel",
           "TFBlipPreTrainedModel",
           "TFBlipForConditionalGeneration",
           "TFBlipForQuestionAnswering",
           "TFBlipVisionModel",
           "TFBlipTextModel",
           "TFBlipForImageTextRetrieval",
       ]

if TYPE_CHECKING:
       from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
       from .processing_blip import BlipProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_blip import BlipImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blip import (
                  BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlipForConditionalGeneration,
                  BlipForImageTextRetrieval,
                  BlipForQuestionAnswering,
                  BlipModel,
                  BlipPreTrainedModel,
                  BlipTextModel,
                  BlipVisionModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blip import (
                  TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFBlipForConditionalGeneration,
                  TFBlipForImageTextRetrieval,
                  TFBlipForQuestionAnswering,
                  TFBlipModel,
                  TFBlipPreTrainedModel,
                  TFBlipTextModel,
                  TFBlipVisionModel,
              )

else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

