





"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[str]						  =       ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__				:str						  =       "BlipImageProcessor"
  SCREAMING_SNAKE_CASE__				:Union[str, Any]						  =       "AutoTokenizer"

  def __init__(		self :							Dict       , __a :							Optional[int]       , __a :							List[Any]       , __a :							List[str]						)		->					str:
        super().__init__(__a       , __a						)

        # add QFormer tokenizer
        _UpperCamelCase   :						Any    =							qformer_tokenizer

  def __call__(		self :							Optional[int]       , __a :							ImageInput = None       , __a :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       , __a :							bool = True       , __a :							Union[bool, str, PaddingStrategy] = False       , __a :							Union[bool, str, TruncationStrategy] = None       , __a :							Optional[int] = None       , __a :							int = 0       , __a :							Optional[int] = None       , __a :							Optional[bool] = None       , __a :							bool = False       , __a :							bool = False       , __a :							bool = False       , __a :							bool = False       , __a :							bool = False       , __a :							bool = True       , __a :							Optional[Union[str, TensorType]] = None       , **__a :							Union[str, Any]       , )		->					BatchFeature:
        if images is None and text is None:
              raise ValueError("You have to specify at least images or text."						)

        _UpperCamelCase   :						Dict    =							BatchFeature()

        if text is not None:
              _UpperCamelCase   :						Optional[Any]    =							self.tokenizer(
                  text=__a       , add_special_tokens=__a       , padding=__a       , truncation=__a       , max_length=__a       , stride=__a       , pad_to_multiple_of=__a       , return_attention_mask=__a       , return_overflowing_tokens=__a       , return_special_tokens_mask=__a       , return_offsets_mapping=__a       , return_token_type_ids=__a       , return_length=__a       , verbose=__a       , return_tensors=__a       , **__a       , )
              encoding.update(__a						)
              _UpperCamelCase   :						Union[str, Any]    =							self.qformer_tokenizer(
                  text=__a       , add_special_tokens=__a       , padding=__a       , truncation=__a       , max_length=__a       , stride=__a       , pad_to_multiple_of=__a       , return_attention_mask=__a       , return_overflowing_tokens=__a       , return_special_tokens_mask=__a       , return_offsets_mapping=__a       , return_token_type_ids=__a       , return_length=__a       , verbose=__a       , return_tensors=__a       , **__a       , )
              _UpperCamelCase   :						Union[str, Any]    =							qformer_text_encoding.pop("input_ids"						)
              _UpperCamelCase   :						Union[str, Any]    =							qformer_text_encoding.pop("attention_mask"						)

        if images is not None:
              _UpperCamelCase   :						List[str]    =							self.image_processor(__a       , return_tensors=__a						)
              encoding.update(__a						)

        return encoding

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]       , *__a :							Tuple       , **__a :							Dict						)		->					Tuple:
        return self.tokenizer.batch_decode(*__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , *__a :							Dict       , **__a :							Dict						)		->					Any:
        return self.tokenizer.decode(*__a       , **__a						)

  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					int:
        _UpperCamelCase   :						Any    =							self.tokenizer.model_input_names
        _UpperCamelCase   :						int    =							self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							Union[str, Any]       , **__a :							Union[str, Any]						)		->					Optional[int]:
        if os.path.isfile(__a						):
              raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''						)
        os.makedirs(__a       , exist_ok=__a						)
        _UpperCamelCase   :						Optional[int]    =							os.path.join(__a       , "qformer_tokenizer"						)
        self.qformer_tokenizer.save_pretrained(__a						)
        return super().save_pretrained(__a       , **__a						)


  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		cls :							str       , __a :							List[str]       , **__a :							Optional[Any]						)		->					Union[str, Any]:
        _UpperCamelCase   :						Optional[Any]    =							AutoTokenizer.from_pretrained(__a       , subfolder="qformer_tokenizer"						)
        _UpperCamelCase   :						List[Any]    =							cls._get_arguments_from_pretrained(__a       , **__a						)
        args.append(__a						)
        return cls(*__a						)







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


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




def        lowercase__     (       lowercase_      )       ->		Dict:




      """simple docstring"""

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




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Tuple[Dict, Dict]:




      """simple docstring"""

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




def        lowercase__     (       lowercase_      )       ->		MusicgenDecoderConfig:




      """simple docstring"""

      if checkpoint == "small":
            # default config values
            _UpperCamelCase   :						Optional[Any]    =							1_024
            _UpperCamelCase   :						List[str]    =							24
            _UpperCamelCase   :						Optional[int]    =							16
      elif checkpoint == "medium":
            _UpperCamelCase   :						int    =							1_536
            _UpperCamelCase   :						Any    =							48
            _UpperCamelCase   :						Optional[Any]    =							24
      elif checkpoint == "large":
            _UpperCamelCase   :						int    =							2_048
            _UpperCamelCase   :						Dict    =							48
            _UpperCamelCase   :						Dict    =							32
      else:
            raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''      )
      _UpperCamelCase   :						List[Any]    =							MusicgenDecoderConfig(
          hidden_size=lowercase_		,ffn_dim=hidden_size * 4		,num_hidden_layers=lowercase_		,num_attention_heads=lowercase_		,)
      return config




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_="cpu"      )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							MusicGen.get_pretrained(lowercase_		,device=lowercase_      )
      _UpperCamelCase   :						int    =							decoder_config_from_checkpoint(lowercase_      )

      _UpperCamelCase   :						int    =							fairseq_model.lm.state_dict()
      _UpperCamelCase,	_UpperCamelCase   :						Tuple    =							rename_state_dict(
          lowercase_		,hidden_size=decoder_config.hidden_size      )

      _UpperCamelCase   :						int    =							TaEncoderModel.from_pretrained("t5-base"      )
      _UpperCamelCase   :						str    =							EncodecModel.from_pretrained("facebook/encodec_32khz"      )
      _UpperCamelCase   :						Dict    =							MusicgenForCausalLM(lowercase_      ).eval()

      # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
      _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							decoder.load_state_dict(lowercase_		,strict=lowercase_      )

      for key in missing_keys.copy():
            if key.startswith(("text_encoder", "audio_encoder")      ) or key in EXPECTED_MISSING_KEYS:
                  missing_keys.remove(lowercase_      )

      if len(lowercase_      ) > 0:
            raise ValueError(F'''Missing key(s) in state_dict: {missing_keys}'''      )

      if len(lowercase_      ) > 0:
            raise ValueError(F'''Unexpected key(s) in state_dict: {unexpected_keys}'''      )

      # init the composite model
      _UpperCamelCase   :						Tuple    =							MusicgenForConditionalGeneration(text_encoder=lowercase_		,audio_encoder=lowercase_		,decoder=lowercase_      )

      # load the pre-trained enc-dec projection (from the decoder state dict)
      model.enc_to_dec_proj.load_state_dict(lowercase_      )

      # check we can do a forward pass
      _UpperCamelCase   :						Any    =							torch.arange(0		,8		,dtype=torch.long      ).reshape(2		,-1      )
      _UpperCamelCase   :						List[Any]    =							input_ids.reshape(2 * 4		,-1      )

      with torch.no_grad():
            _UpperCamelCase   :						List[str]    =							model(input_ids=lowercase_		,decoder_input_ids=lowercase_      ).logits

      if logits.shape != (8, 1, 2_048):
            raise ValueError("Incorrect shape for logits"      )

      # now construct the processor
      _UpperCamelCase   :						str    =							AutoTokenizer.from_pretrained("t5-base"      )
      _UpperCamelCase   :						Union[str, Any]    =							AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,padding_side="left"      )

      _UpperCamelCase   :						Any    =							MusicgenProcessor(feature_extractor=lowercase_		,tokenizer=lowercase_      )

      # set the appropriate bos/pad token ids
      _UpperCamelCase   :						Optional[Any]    =							2_048
      _UpperCamelCase   :						str    =							2_048

      # set other default generation config params
      _UpperCamelCase   :						Optional[int]    =							int(30 * audio_encoder.config.frame_rate      )
      _UpperCamelCase   :						Optional[Any]    =							True
      _UpperCamelCase   :						Any    =							3.0

      if pytorch_dump_folder is not None:
            Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
            logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}'''      )
            model.save_pretrained(lowercase_      )
            processor.save_pretrained(lowercase_      )

      if repo_id:
            logger.info(F'''Pushing model {checkpoint} to {repo_id}'''      )
            model.push_to_hub(lowercase_      )
            processor.push_to_hub(lowercase_      )


if __name__ == "__main__":
  lowerCamelCase__						= 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."
  )

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