




'''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()
A     =logging.get_logger(__name__)


A     =['''model.decoder.embed_positions.weights''']





def 						snake_case_							(_a	:							Optional[int]     ):

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





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





def 						snake_case_							(_a	:							str     ):

    if checkpoint == "small":
        # default config values
        UpperCAmelCase			       =		1_0_2_4
        UpperCAmelCase			       =		2_4
        UpperCAmelCase			       =		1_6
    elif checkpoint == "medium":
        UpperCAmelCase			       =		1_5_3_6
        UpperCAmelCase			       =		4_8
        UpperCAmelCase			       =		2_4
    elif checkpoint == "large":
        UpperCAmelCase			       =		2_0_4_8
        UpperCAmelCase			       =		4_8
        UpperCAmelCase			       =		3_2
    else:
        raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."     )
    UpperCAmelCase			       =		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_							(_a	:							Optional[Any]    ,   _a	:							List[str]=None    ,   _a	:							Any=None    ,   _a	:							Dict="cpu"     ):
    UpperCAmelCase			       =		MusicGen.get_pretrained(snake_case_    ,   device=snake_case_     )
    UpperCAmelCase			       =		decoder_config_from_checkpoint(snake_case_     )

    UpperCAmelCase			       =		fairseq_model.lm.state_dict()
    UpperCAmelCase       ,     UpperCAmelCase			       =		rename_state_dict(
        snake_case_    ,   hidden_size=decoder_config.hidden_size     )

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

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

    with torch.no_grad():
        UpperCAmelCase			       =		model(input_ids=snake_case_    ,   decoder_input_ids=snake_case_     ).logits

    if logits.shape != (8, 1, 2_0_4_8):
        raise ValueError('''Incorrect shape for logits'''     )

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

    UpperCAmelCase			       =		MusicgenProcessor(feature_extractor=snake_case_    ,   tokenizer=snake_case_     )

    # set the appropriate bos/pad token ids
    UpperCAmelCase			       =		2_0_4_8
    UpperCAmelCase			       =		2_0_4_8

    # set other default generation config params
    UpperCAmelCase			       =		int(3_0 * audio_encoder.config.frame_rate     )
    UpperCAmelCase			       =		True
    UpperCAmelCase			       =		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__":
   A     =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.'
   )

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





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """encoder.version""",
     """decoder.version""",
     """model.encoder.version""",
     """model.decoder.version""",
     """decoder.output_projection.weight""",
     """_float_tensor""",
     """encoder.embed_positions._float_tensor""",
     """decoder.embed_positions._float_tensor""",
 ]
 for k in ignore_keys:
  state_dict.pop(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						emb.weight.shape
 __lowerCAmelCase											=						nn.Linear(snake_case_						,		snake_case_						,		bias=snake_case_      )
 __lowerCAmelCase											=						emb.weight.data
 return lin_layer







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowerCAmelCase											=						mam_aaa["""model"""]
 remove_ignore_keys_(snake_case_      )
 __lowerCAmelCase											=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase											=						MaMaaaConfig(
     vocab_size=snake_case_						,		max_position_embeddings=10_24						,		encoder_layers=args.encoder_layers						,		decoder_layers=args.decoder_layers						,		encoder_attention_heads=args.encoder_attention_heads						,		decoder_attention_heads=args.decoder_attention_heads						,		encoder_ffn_dim=args.encoder_ffn_embed_dim						,		decoder_ffn_dim=args.decoder_ffn_embed_dim						,		d_model=args.encoder_embed_dim						,		encoder_layerdrop=args.encoder_layerdrop						,		decoder_layerdrop=args.decoder_layerdrop						,		dropout=args.dropout						,		attention_dropout=args.attention_dropout						,		activation_dropout=args.activation_dropout						,		activation_function="""relu"""						,		)

 __lowerCAmelCase											=						state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase											=						MaMaaaForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 __lowerCAmelCase											=						make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
 _A			:      Union[str, Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
 parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 _A			:      str					      =				parser.parse_args()
 _A			:      Optional[int]					      =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






