



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()
__snake_case		:str   	=  logging.get_logger(__name__)


__snake_case		:str   	=  ['''model.decoder.embed_positions.weights''']





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





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





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





@torch.no_grad()
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase="cpu"       ):
    __a		 =       MusicGen.get_pretrained(_UpperCAmelCase     ,				device=_UpperCAmelCase       )
    __a		 =       decoder_config_from_checkpoint(_UpperCAmelCase       )

    __a		 =       fairseq_model.lm.state_dict()
    __a ,     __a		 =       rename_state_dict(
        _UpperCAmelCase     ,				hidden_size=decoder_config.hidden_size       )

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

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

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

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

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

    # init the composite model
    __a		 =       MusicgenForConditionalGeneration(text_encoder=_UpperCAmelCase     ,				audio_encoder=_UpperCAmelCase     ,				decoder=_UpperCAmelCase       )

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

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

    with torch.no_grad():
        __a		 =       model(input_ids=_UpperCAmelCase     ,				decoder_input_ids=_UpperCAmelCase       ).logits

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

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

    __a		 =       MusicgenProcessor(feature_extractor=_UpperCAmelCase     ,				tokenizer=_UpperCAmelCase       )

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

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

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

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


if __name__ == "__main__":
 __snake_case		:int   	=  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.'''
 )

 __snake_case		:Optional[Any]   	=  parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)










import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
            f'stage{idx}.patch_embed.proj.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
            f'stage{idx}.patch_embed.proj.bias',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
            f'stage{idx}.patch_embed.norm.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
            f'stage{idx}.patch_embed.norm.bias',
        )       )
    return embed





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       []
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj.bias',
        )       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')       )
    return attention_weights





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token''')       )
    return token





def   __snake_case			(    ):
    __a		 =       []
    head.append(('''layernorm.weight''', '''norm.weight''')       )
    head.append(('''layernorm.bias''', '''norm.bias''')       )
    head.append(('''classifier.weight''', '''head.weight''')       )
    head.append(('''classifier.bias''', '''head.bias''')       )
    return head





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       '''imagenet-1k-id2label.json'''
    __a		 =       1000

    __a		 =       '''huggingface/label-files'''
    __a		 =       num_labels
    __a		 =       json.load(open(cached_download(hf_hub_url(_UpperCAmelCase     ,				_UpperCAmelCase     ,				repo_type='''dataset'''       )       )     ,				'''r'''       )       )
    __a		 =       {int(_UpperCAmelCase       ): v for k, v in idalabel.items()}

    __a		 =       idalabel
    __a		 =       {v: k for k, v in idalabel.items()}

    __a		 =       __a		 =       CvtConfig(num_labels=_UpperCAmelCase     ,				idalabel=_UpperCAmelCase     ,				labelaid=_UpperCAmelCase       )

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "13":
        __a		 =       [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "21":
        __a		 =       [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        __a		 =       [2, 2, 20]
        __a		 =       [3, 12, 16]
        __a		 =       [192, 768, 1024]

    __a		 =       CvtForImageClassification(_UpperCAmelCase       )
    __a		 =       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       )
    __a		 =       image_size
    __a		 =       torch.load(_UpperCAmelCase     ,				map_location=torch.device('''cpu'''       )       )

    __a		 =       OrderedDict()
    __a		 =       []

    for idx in range(len(config.depth       )       ):
        if config.cls_token[idx]:
            __a		 =       list_of_state_dict + cls_token(_UpperCAmelCase       )
        __a		 =       list_of_state_dict + embeddings(_UpperCAmelCase       )
        for cnt in range(config.depth[idx]       ):
            __a		 =       list_of_state_dict + attention(_UpperCAmelCase     ,				_UpperCAmelCase       )

    __a		 =       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase       )
    for i in range(len(_UpperCAmelCase       )       ):
        __a		 =       original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase       )
    model.save_pretrained(_UpperCAmelCase       )
    image_processor.save_pretrained(_UpperCAmelCase       )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
 __snake_case		:str   	=  argparse.ArgumentParser()
 parser.add_argument(
     '''--cvt_model''',
     default='''cvt-w24''',
     type=str,
     help='''Name of the cvt model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--image_size''',
     default=384,
     type=int,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--cvt_file_name''',
     default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
     type=str,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 __snake_case		:Dict   	=  parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





