

"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase						=		logging.get_logger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    SwinConfig(
        embed_dim=192 ,   depths=(2, 2, 18, 2) ,   num_heads=(6, 12, 24, 48) ,   window_size=12 ,   out_features=['stage2', 'stage3', 'stage4'] ,   )

    _lowerCAmelCase       :   Tuple  =    DetaConfig(
        backbone_config=_lowerCamelCase ,   num_queries=900 ,   encoder_ffn_dim=2048 ,   decoder_ffn_dim=2048 ,   num_feature_levels=5 ,   assign_first_stage=_lowerCamelCase ,   with_box_refine=_lowerCamelCase ,   two_stage=_lowerCamelCase ,   )

    # set labels
    _lowerCAmelCase       :   Optional[int]  =    'huggingface/label-files'
    if "o365" in model_name:
        _lowerCAmelCase       :   int  =    366
        _lowerCAmelCase       :   Tuple  =    'object365-id2label.json'
    else:
        _lowerCAmelCase       :   List[Any]  =    91
        _lowerCAmelCase       :   str  =    'coco-detection-id2label.json'

    _lowerCAmelCase       :   Any  =    num_labels
    _lowerCAmelCase       :   int  =    json.load(open(cached_download(hf_hub_url(_lowerCamelCase ,   _lowerCamelCase ,   repo_type='dataset'   )   ) ,   'r'   )   )
    _lowerCAmelCase       :   int  =    {int(_lowerCamelCase   ): v for k, v in idalabel.items()}
    _lowerCAmelCase       :   Optional[int]  =    idalabel
    _lowerCAmelCase       :   Tuple  =    {v: k for k, v in idalabel.items()}

    return config

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    []

    # stem
    # fmt: off
    rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')   )
    rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')   )
    rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')   )
    rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')   )
    # stages
    for i in range(len(config.backbone_config.depths   )   ):
        for j in range(config.backbone_config.depths[i]   ):
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")   )

        if i < 3:
            rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")   )
            rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")   )

    rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')   )
    rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')   )
    rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')   )
    rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')   )
    rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')   )
    rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')   )

    # transformer encoder
    for i in range(config.encoder_layers   ):
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")   )

    # transformer decoder
    for i in range(config.decoder_layers   ):
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")   )

    # fmt: on

    return rename_keys

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    dct.pop(_lowerCamelCase   )
    _lowerCAmelCase       :   Optional[Any]  =    val

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
    for i in range(len(backbone_config.depths   )   ):
        _lowerCAmelCase       :   str  =    num_features[i]
        for j in range(backbone_config.depths[i]   ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            _lowerCAmelCase       :   Optional[int]  =    state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""   )
            _lowerCAmelCase       :   Union[str, Any]  =    state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""   )
            # next, add query, keys and values (in that order) to the state dict
            _lowerCAmelCase       :   Optional[int]  =    in_proj_weight[:dim, :]
            _lowerCAmelCase       :   List[str]  =    in_proj_bias[: dim]
            _lowerCAmelCase       :   str  =    in_proj_weight[
                dim : dim * 2, :
            ]
            _lowerCAmelCase       :   List[str]  =    in_proj_bias[
                dim : dim * 2
            ]
            _lowerCAmelCase       :   List[Any]  =    in_proj_weight[
                -dim :, :
            ]
            _lowerCAmelCase       :   Any  =    in_proj_bias[-dim :]
            # fmt: on

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    config.d_model
    for i in range(config.decoder_layers   ):
        # read in weights + bias of input projection layer of self-attention
        _lowerCAmelCase       :   Any  =    state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _lowerCAmelCase       :   Optional[Any]  =    state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _lowerCAmelCase       :   Union[str, Any]  =    in_proj_weight[:hidden_size, :]
        _lowerCAmelCase       :   Optional[Any]  =    in_proj_bias[:hidden_size]
        _lowerCAmelCase       :   List[str]  =    in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        _lowerCAmelCase       :   Optional[int]  =    in_proj_bias[hidden_size : hidden_size * 2]
        _lowerCAmelCase       :   Union[str, Any]  =    in_proj_weight[-hidden_size:, :]
        _lowerCAmelCase       :   List[Any]  =    in_proj_bias[-hidden_size:]

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Union[str, Any]  =    'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowerCAmelCase       :   Union[str, Any]  =    Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase   ).raw   )

    return im

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    get_deta_config(_lowerCamelCase   )

    # load original state dict
    if model_name == "deta-swin-large":
        _lowerCAmelCase       :   List[str]  =    hf_hub_download(repo_id='nielsr/deta-checkpoints' ,   filename='adet_swin_ft.pth'   )
    elif model_name == "deta-swin-large-o365":
        _lowerCAmelCase       :   Tuple  =    hf_hub_download(repo_id='jozhang97/deta-swin-l-o365' ,   filename='deta_swin_pt_o365.pth'   )
    else:
        raise ValueError(f"""Model name {model_name} not supported"""   )

    _lowerCAmelCase       :   Dict  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )['model']

    # original state dict
    for name, param in state_dict.items():
        print(_lowerCamelCase ,   param.shape   )

    # rename keys
    _lowerCAmelCase       :   str  =    create_rename_keys(_lowerCamelCase   )
    for src, dest in rename_keys:
        rename_key(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
    read_in_swin_q_k_v(_lowerCamelCase ,   config.backbone_config   )
    read_in_decoder_q_k_v(_lowerCamelCase ,   _lowerCamelCase   )

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            _lowerCAmelCase       :   Dict  =    state_dict.pop(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    val
        if "input_proj" in key:
            _lowerCAmelCase       :   Optional[int]  =    state_dict.pop(_lowerCamelCase   )
            _lowerCAmelCase       :   Optional[Any]  =    val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            _lowerCAmelCase       :   Any  =    state_dict.pop(_lowerCamelCase   )
            _lowerCAmelCase       :   int  =    val

    # finally, create HuggingFace model and load state dict
    _lowerCAmelCase       :   List[str]  =    DetaForObjectDetection(_lowerCamelCase   )
    model.load_state_dict(_lowerCamelCase   )
    model.eval()

    _lowerCAmelCase       :   Optional[int]  =    'cuda' if torch.cuda.is_available() else 'cpu'
    model.to(_lowerCamelCase   )

    # load image processor
    _lowerCAmelCase       :   int  =    DetaImageProcessor(format='coco_detection'   )

    # verify our conversion on image
    _lowerCAmelCase       :   Optional[int]  =    prepare_img()
    _lowerCAmelCase       :   str  =    processor(images=_lowerCamelCase ,   return_tensors='pt'   )
    _lowerCAmelCase       :   str  =    encoding['pixel_values']
    _lowerCAmelCase       :   str  =    model(pixel_values.to(_lowerCamelCase   )   )

    # verify logits
    print('Logits:' ,   outputs.logits[0, :3, :3]   )
    print('Boxes:' ,   outputs.pred_boxes[0, :3, :3]   )
    if model_name == "deta-swin-large":
        _lowerCAmelCase       :   int  =    torch.tensor(
            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]   )
        _lowerCAmelCase       :   Optional[int]  =    torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]   )
    elif model_name == "deta-swin-large-o365":
        _lowerCAmelCase       :   Union[str, Any]  =    torch.tensor(
            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]   )
        _lowerCAmelCase       :   int  =    torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]   )
    assert torch.allclose(outputs.logits[0, :3, :3] ,   expected_logits.to(_lowerCamelCase   ) ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,   expected_boxes.to(_lowerCamelCase   ) ,   atol=1e-4   )
    print('Everything ok!'   )

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""   )
        Path(_lowerCamelCase   ).mkdir(exist_ok=_lowerCamelCase   )
        model.save_pretrained(_lowerCamelCase   )
        processor.save_pretrained(_lowerCamelCase   )

    # Push to hub
    if push_to_hub:
        print('Pushing model and processor to hub...'   )
        model.push_to_hub(f"""jozhang97/{model_name}"""   )
        processor.push_to_hub(f"""jozhang97/{model_name}"""   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     type=str,
     default="""deta-swin-large""",
     choices=["""deta-swin-large""", """deta-swin-large-o365"""],
     help="""Name of the model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=str,
     help="""Path to the folder to output PyTorch model.""",
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""






import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase						=		logging.get_logger(__name__)


_lowerCAmelCase						=		"""https://openaipublic.azureedge.net/jukebox/models/"""
_lowerCAmelCase						=		{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if key.endswith('.model.1.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.bias' ,   '.conv1d_1.bias'   )
    elif key.endswith('.model.1.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.weight' ,   '.conv1d_1.weight'   )
    elif key.endswith('.model.3.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   int  =    key.replace('.model.3.bias' ,   '.conv1d_2.bias'   )
    elif key.endswith('.model.3.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Tuple  =    key.replace('.model.3.weight' ,   '.conv1d_2.weight'   )

    if "conditioner_blocks.0." in key:
        _lowerCAmelCase       :   Dict  =    key.replace('conditioner_blocks.0' ,   'conditioner_blocks'   )

    if "prime_prior" in key:
        _lowerCAmelCase       :   str  =    key.replace('prime_prior' ,   'encoder'   )

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        _lowerCAmelCase       :   Optional[Any]  =    key.replace('.emb.' ,   '.'   )

    if key.endswith('k'   ):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace('.k' ,   '.codebook'   )
    if "y_emb." in key:
        return key.replace('y_emb.' ,   'metadata_embedding.'   )

    if "x_emb.emb." in key:
        _lowerCAmelCase       :   Any  =    key.replace('0.x_emb.emb' ,   'embed_tokens'   )

    if "prime_state_ln" in key:
        return key.replace('prime_state_ln' ,   'encoder.final_layer_norm'   )
    if ".ln" in key:
        return key.replace('.ln' ,   '.layer_norm'   )
    if "_ln" in key:
        return key.replace('_ln' ,   '_layer_norm'   )

    if "prime_state_proj" in key:
        return key.replace('prime_state_proj' ,   'encoder.proj_in'   )
    if "prime_x_out" in key:
        return key.replace('prime_x_out' ,   'encoder.lm_head'   )
    if "prior.x_out" in key:
        return key.replace('x_out' ,   'fc_proj_out'   )
    if "x_emb" in key:
        return key.replace('x_emb' ,   'embed_tokens'   )

    return key

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {}
    import re

    _lowerCAmelCase       :   Optional[Any]  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Optional[int]  =    re.compile(
        R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Union[str, Any]  =    re.compile(
        R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Optional[int]  =    re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(
        R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   List[str]  =    re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'   )

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   int  =    re_encoder_block_conv_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   int  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[2]   ) * 2 + int(groups[3]   )
            _lowerCAmelCase       :   Dict  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Optional[int]  =    re_encoder_block_conv_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_encoder_block_resnet.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_encoder_block_resnet.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    int(groups[2]   ) * 2 + int(groups[3]   )
            _lowerCAmelCase       :   Tuple  =    {'1': 1, '3': 2}[groups[-2]]
            _lowerCAmelCase       :   Union[str, Any]  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
            _lowerCAmelCase       :   Optional[int]  =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Optional[int]  =    prefix + resnet_block
            _lowerCAmelCase       :   Dict  =    re_encoder_block_resnet.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   str  =    re_encoder_block_proj_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
            _lowerCAmelCase       :   Any  =    re_encoder_block_proj_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Optional[int]  =    re_decoder_block_conv_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            _lowerCAmelCase       :   Dict  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_decoder_block_conv_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_decoder_block_resnet.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Any  =    re_decoder_block_resnet.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Dict  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            _lowerCAmelCase       :   Dict  =    {'1': 1, '3': 2}[groups[-2]]
            _lowerCAmelCase       :   int  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
            _lowerCAmelCase       :   Optional[int]  =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            _lowerCAmelCase       :   List[Any]  =    prefix + resnet_block
            _lowerCAmelCase       :   str  =    re_decoder_block_resnet.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Any  =    re_decoder_block_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   str  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   str  =    re_decoder_block_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   List[Any]  =    re_prior_cond_conv_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Any  =    regex_match.groups()
            _lowerCAmelCase       :   Optional[int]  =    int(groups[1]   ) * 2 + int(groups[2]   ) - 2
            _lowerCAmelCase       :   Any  =    f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   List[str]  =    re_prior_cond_conv_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_prior_cond_resnet.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_prior_cond_resnet.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Tuple  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[1]   ) * 2 + int(groups[2]   ) - 2
            _lowerCAmelCase       :   Tuple  =    {'1': 1, '3': 2}[groups[-2]]
            _lowerCAmelCase       :   List[Any]  =    f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
            _lowerCAmelCase       :   List[str]  =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    prefix + resnet_block
            _lowerCAmelCase       :   List[str]  =    re_prior_cond_resnet.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[str]  =    regex_match.groups()
            _lowerCAmelCase       :   List[Any]  =    f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # keep original key
        else:
            _lowerCAmelCase       :   Optional[Any]  =    original_key

        _lowerCAmelCase       :   List[Any]  =    replace_key(_lowerCamelCase   )

        if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
            print(f"""failed converting {original_key} to {key}, does not match"""   )

        # handle missmatched shape
        elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
            _lowerCAmelCase       :   Dict  =    model_state_dict[f"""{key_prefix}.{key}"""]
            print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""   )
            _lowerCAmelCase       :   Optional[int]  =    original_key

        _lowerCAmelCase       :   Union[str, Any]  =    original_key
        _lowerCAmelCase       :   Optional[Any]  =    value

    return new_dict

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase=None ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'   )[-1]}"""   ):
            _lowerCAmelCase       :   str  =    requests.get(f"""{PREFIX}{file}""" ,   allow_redirects=_lowerCamelCase   )
            os.makedirs(f"""{pytorch_dump_folder_path}/""" ,   exist_ok=_lowerCamelCase   )
            open(f"""{pytorch_dump_folder_path}/{file.split('/'   )[-1]}""" ,   'wb'   ).write(r.content   )

    _lowerCAmelCase       :   Union[str, Any]  =    MODEL_MAPPING[model_name.split('/'   )[-1]]

    _lowerCAmelCase       :   Optional[Any]  =    JukeboxConfig.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    JukeboxModel(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    _lowerCAmelCase       :   Any  =    {}
    for i, dict_name in enumerate(_lowerCamelCase   ):
        _lowerCAmelCase       :   Union[str, Any]  =    torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'   )[-1]}"""   )['model']

        _lowerCAmelCase       :   Optional[Any]  =    {}
        for k in old_dic.keys():
            if k.endswith('.b'   ):
                _lowerCAmelCase       :   int  =    old_dic[k]
            elif k.endswith('.w'   ):
                _lowerCAmelCase       :   Tuple  =    old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                _lowerCAmelCase       :   str  =    old_dic[k]
            else:
                _lowerCAmelCase       :   Optional[Any]  =    old_dic[k]

        _lowerCAmelCase       :   List[str]  =    'vqvae' if i == 0 else f"""priors.{3 - i}"""
        _lowerCAmelCase       :   Tuple  =    fix_jukebox_keys(_lowerCamelCase ,   model.state_dict() ,   _lowerCamelCase ,   _lowerCamelCase   )
        weight_dict.append(_lowerCamelCase   )

    _lowerCAmelCase       :   List[Any]  =    weight_dict.pop(0   )
    model.vqvae.load_state_dict(_lowerCamelCase   )
    for i in range(len(_lowerCamelCase   )   ):
        model.priors[i].load_state_dict(weight_dict[2 - i]   )

    Path(_lowerCamelCase   ).mkdir(exist_ok=_lowerCamelCase   )
    with open(f"""{pytorch_dump_folder_path}/mapping.json""" ,   'w'   ) as txtfile:
        json.dump(_lowerCamelCase ,   _lowerCamelCase   )

    print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(_lowerCamelCase   )

    return weight_dict


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""jukebox-5b-lyrics""",
     type=str,
     help="""Name of the model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""jukebox-5b-lyrics-converted""",
     type=str,
     help="""Path to the output PyTorch model directory.""",
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





