


'''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							:   Tuple  						=	logging.get_logger(__name__)


lowerCamelCase							:   List[Any]  						=	['model.decoder.embed_positions.weights']

def _SCREAMING_SNAKE_CASE      (A       )       ->       Any:






       """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      (A  , A       )       ->       Tuple[Dict, Dict]:






       """simple docstring"""







       lowercase__      			= list(state_dict.keys()       )
       lowercase__      			= {}
       for key in keys:
              lowercase__      			= state_dict.pop(A       )
              lowercase__      			= rename_keys(A       )
              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      (A       )       ->       MusicgenDecoderConfig:






       """simple docstring"""







       if checkpoint == "small":
              # default config values
              lowercase__      			= 1_024
              lowercase__      			= 24
              lowercase__      			= 16
       elif checkpoint == "medium":
              lowercase__      			= 1_536
              lowercase__      			= 48
              lowercase__      			= 24
       elif checkpoint == "large":
              lowercase__      			= 2_048
              lowercase__      			= 48
              lowercase__      			= 32
       else:
              raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."       )
       lowercase__      			= MusicgenDecoderConfig(
           hidden_size=A  , ffn_dim=hidden_size * 4  , num_hidden_layers=A  , num_attention_heads=A  , )
       return config

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






       """simple docstring"""







       lowercase__      			= MusicGen.get_pretrained(A  , device=A       )
       lowercase__      			= decoder_config_from_checkpoint(A       )

       lowercase__      			= fairseq_model.lm.state_dict()
       lowercase__			,lowercase__      			= rename_state_dict(
           A  , hidden_size=decoder_config.hidden_size       )

       lowercase__      			= TaEncoderModel.from_pretrained('''t5-base'''       )
       lowercase__      			= EncodecModel.from_pretrained('''facebook/encodec_32khz'''       )
       lowercase__      			= MusicgenForCausalLM(A       ).eval()

       # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
       lowercase__			,lowercase__      			= decoder.load_state_dict(A  , strict=A       )

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

       if len(A       ) > 0:
              raise ValueError(f"Missing key(s) in state_dict: {missing_keys}"       )

       if len(A       ) > 0:
              raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}"       )

       # init the composite model
       lowercase__      			= MusicgenForConditionalGeneration(text_encoder=A  , audio_encoder=A  , decoder=A       )

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

       # 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=A  , decoder_input_ids=A       ).logits

       if logits.shape != (8, 1, 2_048):
              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=A  , tokenizer=A       )

       # set the appropriate bos/pad token ids
       lowercase__      			= 2_048
       lowercase__      			= 2_048

       # 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(A       ).mkdir(exist_ok=A       )
              logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}"       )
              model.save_pretrained(A       )
              processor.save_pretrained(A       )

       if repo_id:
              logger.info(f"Pushing model {checkpoint} to {repo_id}"       )
              model.push_to_hub(A       )
              processor.push_to_hub(A       )


if __name__ == "__main__":
 lowerCamelCase							:   Union[str, Any]  						=	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							:   List[str]  						=	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_torch_available


lowercase__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)