

'''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[str]							       =				["""model.decoder.embed_positions.weights"""]


def       snake_case   (	snake_case      : Union[str, Any]			) ->  Optional[int]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : Optional[Any]					,		snake_case      : Any			) ->  Optional[Any]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : List[Any]			) ->  Optional[int]:


    """simple docstring"""


    if checkpoint == "small":
        # default config values
        lowerCAmelCase 						=							1024
        lowerCAmelCase 						=							24
        lowerCAmelCase 						=							16
    elif checkpoint == "medium":
        lowerCAmelCase 						=							1536
        lowerCAmelCase 						=							48
        lowerCAmelCase 						=							24
    elif checkpoint == "large":
        lowerCAmelCase 						=							2048
        lowerCAmelCase 						=							48
        lowerCAmelCase 						=							32
    else:
        raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'			)
    lowerCAmelCase 						=							MusicgenDecoderConfig(
        hidden_size=snake_case					,		ffn_dim=hidden_size * 4					,		num_hidden_layers=snake_case					,		num_attention_heads=snake_case					,		)
    return config


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


    """simple docstring"""


    lowerCAmelCase 						=							MusicGen.get_pretrained(snake_case					,		device=snake_case			)
    lowerCAmelCase 						=							decoder_config_from_checkpoint(snake_case			)

    lowerCAmelCase 						=							fairseq_model.lm.state_dict()
    lowerCAmelCase 						=							rename_state_dict(
        snake_case					,		hidden_size=decoder_config.hidden_size			)

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

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

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

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

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

    # init the composite model
    lowerCAmelCase 						=							MusicgenForConditionalGeneration(text_encoder=snake_case					,		audio_encoder=snake_case					,		decoder=snake_case			)

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

    # check we can do a forward pass
    lowerCAmelCase 						=							torch.arange(0					,		8					,		dtype=torch.long			).reshape(2					,		-1			)
    lowerCAmelCase 						=							input_ids.reshape(2 * 4					,		-1			)

    with torch.no_grad():
        lowerCAmelCase 						=							model(input_ids=snake_case					,		decoder_input_ids=snake_case			).logits

    if logits.shape != (8, 1, 2048):
        raise ValueError('Incorrect shape for logits'			)

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

    lowerCAmelCase 						=							MusicgenProcessor(feature_extractor=snake_case					,		tokenizer=snake_case			)

    # set the appropriate bos/pad token ids
    lowerCAmelCase 						=							2048
    lowerCAmelCase 						=							2048

    # set other default generation config params
    lowerCAmelCase 						=							int(30 * audio_encoder.config.frame_rate			)
    lowerCAmelCase 						=							True
    lowerCAmelCase 						=							3.0

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

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


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

     _UpperCamelCase						:  Optional[Any]							       =				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


__magic_name__       :					int						=			{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					List[Any]						=			[
		    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecAudioForAudioFrameClassification""",
		    """Data2VecAudioForCTC""",
		    """Data2VecAudioForSequenceClassification""",
		    """Data2VecAudioForXVector""",
		    """Data2VecAudioModel""",
		    """Data2VecAudioPreTrainedModel""",
		]
		__magic_name__       :					Optional[int]						=			[
		    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecTextForCausalLM""",
		    """Data2VecTextForMaskedLM""",
		    """Data2VecTextForMultipleChoice""",
		    """Data2VecTextForQuestionAnswering""",
		    """Data2VecTextForSequenceClassification""",
		    """Data2VecTextForTokenClassification""",
		    """Data2VecTextModel""",
		    """Data2VecTextPreTrainedModel""",
		]
		__magic_name__       :					str						=			[
		    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecVisionForImageClassification""",
		    """Data2VecVisionForMaskedImageModeling""",
		    """Data2VecVisionForSemanticSegmentation""",
		    """Data2VecVisionModel""",
		    """Data2VecVisionPreTrainedModel""",
		]

if is_tf_available():
		__magic_name__       :					Optional[Any]						=			[
		    """TFData2VecVisionForImageClassification""",
		    """TFData2VecVisionForSemanticSegmentation""",
		    """TFData2VecVisionModel""",
		    """TFData2VecVisionPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

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



