





'''simple docstring'''

import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:	str			=logging.get_logger(__name__)





def 						UpperCamelCase				(							_lowerCamelCase					:					str       ):
    A__				=			SwinConfig.from_pretrained(
        "microsoft/swin-tiny-patch4-window7-224"						,       out_features=["stage1", "stage2", "stage3", "stage4"]       )
    A__				=			MaskFormerConfig(backbone_config=_lowerCamelCase       )

    A__				=			"huggingface/label-files"
    if "ade20k-full" in model_name:
        # this should be ok
        A__				=			8_47
        A__				=			"maskformer-ade20k-full-id2label.json"
    elif "ade" in model_name:
        # this should be ok
        A__				=			1_50
        A__				=			"ade20k-id2label.json"
    elif "coco-stuff" in model_name:
        # this should be ok
        A__				=			1_71
        A__				=			"maskformer-coco-stuff-id2label.json"
    elif "coco" in model_name:
        # TODO
        A__				=			1_33
        A__				=			"coco-panoptic-id2label.json"
    elif "cityscapes" in model_name:
        # this should be ok
        A__				=			19
        A__				=			"cityscapes-id2label.json"
    elif "vistas" in model_name:
        # this should be ok
        A__				=			65
        A__				=			"mapillary-vistas-id2label.json"

    A__				=			json.load(open(hf_hub_download(_lowerCamelCase						,       _lowerCamelCase						,       repo_type="dataset"       )						,       "r"       )       )
    A__				=			{int(_lowerCamelCase       ): v for k, v in idalabel.items()}

    return config





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict       ):
    A__				=			[]
    # stem
    # fmt: off
    rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")       )
    rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")       )
    rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")       )
    rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")       )
            rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")       )

        if i < 3:
            rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")       )
            rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")       )
            rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")       )
        rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")       )
        rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")       )

    # FPN
    rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")       )
    rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")       )
    rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")       )
    for source_index, target_index in zip(range(3						,       0						,       -1       )						,       range(0						,       3       )       ):
        rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")       )
        rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")       )
        rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")       )
        rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")       )
        rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")       )
        rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")       )
    rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")       )
    rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")       )

    # Transformer decoder
    for idx in range(config.decoder_config.decoder_layers       ):
        # self-attention out projection
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")       )
        # cross-attention out projection
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")       )
        # MLP 1
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias")       )
        # MLP 2
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias")       )
        # layernorm 1 (self-attention layernorm)
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")       )
        # layernorm 2 (cross-attention layernorm)
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")       )
        # layernorm 3 (final layernorm)
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")       )

    rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")       )
    rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")       )

    # heads on top
    rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")       )

    rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")       )
    rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")       )

    rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")       )
    rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")       )

    for i in range(3       ):
        rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight")       )
        rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias")       )
    # fmt: on

    return rename_keys





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[Any]						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Any       ):
    A__				=			dct.pop(_lowerCamelCase       )
    A__				=			val





def 						UpperCamelCase				(							_lowerCamelCase					:					int						,       _lowerCamelCase					:					int       ):
    A__				=			[int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
    for i in range(len(backbone_config.depths       )       ):
        A__				=			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)
            A__				=			state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"       )
            A__				=			state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"       )
            # next, add query, keys and values (in that order) to the state dict
            A__				=			in_proj_weight[:dim, :]
            A__				=			in_proj_bias[: dim]
            A__				=			in_proj_weight[
                dim : dim * 2, :
            ]
            A__				=			in_proj_bias[
                dim : dim * 2
            ]
            A__				=			in_proj_weight[
                -dim :, :
            ]
            A__				=			in_proj_bias[-dim :]
            # fmt: on





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Any       ):
    # fmt: off
    A__				=			config.decoder_config.hidden_size
    for idx in range(config.decoder_config.decoder_layers       ):
        # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
        A__				=			state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"       )
        A__				=			state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"       )
        # next, add query, keys and values (in that order) to the state dict
        A__				=			in_proj_weight[: hidden_size, :]
        A__				=			in_proj_bias[:config.hidden_size]
        A__				=			in_proj_weight[hidden_size : hidden_size * 2, :]
        A__				=			in_proj_bias[hidden_size : hidden_size * 2]
        A__				=			in_proj_weight[-hidden_size :, :]
        A__				=			in_proj_bias[-hidden_size :]
        # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
        A__				=			state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"       )
        A__				=			state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"       )
        # next, add query, keys and values (in that order) to the state dict
        A__				=			in_proj_weight[: hidden_size, :]
        A__				=			in_proj_bias[:config.hidden_size]
        A__				=			in_proj_weight[hidden_size : hidden_size * 2, :]
        A__				=			in_proj_bias[hidden_size : hidden_size * 2]
        A__				=			in_proj_weight[-hidden_size :, :]
        A__				=			in_proj_bias[-hidden_size :]
    # fmt: on





def 						UpperCamelCase				(							):
    A__				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
    A__				=			Image.open(requests.get(_lowerCamelCase						,       stream=_lowerCamelCase       ).raw       )
    return im





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					bool = False       ):
    A__				=			get_maskformer_config(_lowerCamelCase       )

    # load original state_dict
    with open(_lowerCamelCase						,       "rb"       ) as f:
        A__				=			pickle.load(_lowerCamelCase       )
    A__				=			data["model"]

    # for name, param in state_dict.items():
    #     print(name, param.shape)

    # rename keys
    A__				=			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       )

    # update to torch tensors
    for key, value in state_dict.items():
        A__				=			torch.from_numpy(_lowerCamelCase       )

    # load 🤗 model
    A__				=			MaskFormerForInstanceSegmentation(_lowerCamelCase       )
    model.eval()

    for name, param in model.named_parameters():
        print(_lowerCamelCase						,       param.shape       )

    A__,	A__				=			model.load_state_dict(_lowerCamelCase						,       strict=_lowerCamelCase       )
    assert missing_keys == [
        "model.pixel_level_module.encoder.model.layernorm.weight",
        "model.pixel_level_module.encoder.model.layernorm.bias",
    ]
    assert len(_lowerCamelCase       ) == 0, F"Unexpected keys: {unexpected_keys}"

    # verify results
    A__				=			prepare_img()
    if "vistas" in model_name:
        A__				=			65
    elif "cityscapes" in model_name:
        A__				=			6_55_35
    else:
        A__				=			2_55
    A__				=			True if "ade" in model_name else False
    A__				=			MaskFormerImageProcessor(ignore_index=_lowerCamelCase						,       reduce_labels=_lowerCamelCase       )

    A__				=			image_processor(_lowerCamelCase						,       return_tensors="pt"       )

    A__				=			model(**_lowerCamelCase       )

    print("Logits:"						,       outputs.class_queries_logits[0, :3, :3]       )

    if model_name == "maskformer-swin-tiny-ade":
        A__				=			torch.tensor(
            [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]       )
    assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,       _lowerCamelCase						,       atol=1e-4       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        print(F"Saving model and image processor to {pytorch_dump_folder_path}"       )
        Path(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
        model.save_pretrained(_lowerCamelCase       )
        image_processor.save_pretrained(_lowerCamelCase       )

    if push_to_hub:
        print("Pushing model and image processor to the hub..."       )
        model.push_to_hub(F"nielsr/{model_name}"       )
        image_processor.push_to_hub(F"nielsr/{model_name}"       )


if __name__ == "__main__":
  __lowerCAmelCase							:	Any			=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="maskformer-swin-tiny-ade",
      type=str,
      help=("Name of the MaskFormer model you'd like to convert",),
  )
  parser.add_argument(
      "--checkpoint_path",
      default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
      type=str,
      help="Path to the original state dict (.pth file).",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  __lowerCAmelCase							:	str			=parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )










'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:	List[Any]			=logging.get_logger(__name__)





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Union[str, Any]=False       ):
    A__				=			[]

    # fmt: off
    # stem:
    rename_keys.append(("cls_token", "vit.embeddings.cls_token")       )
    rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")       )

    rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")       )
    rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")       )

    # backbone
    rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")       )
    rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")       )
    rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")       )

    for stage_idx in range(len(config.backbone_config.depths       )       ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")       )

        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")       )
        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")       )
        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")       )

    # transformer encoder
    for i in range(config.num_hidden_layers       ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")       )
        rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")       )
        rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")       )
        rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")       )
        rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")       )
        rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")       )
        rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")       )
        rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")       )
        rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")       )
        rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")       )

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ("norm.weight", "layernorm.weight"),
                ("norm.bias", "layernorm.bias"),
                ("pre_logits.fc.weight", "pooler.dense.weight"),
                ("pre_logits.fc.bias", "pooler.dense.bias"),
            ]       )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        A__				=			[(pair[0], pair[1][4:]) if pair[1].startswith("vit"       ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("norm.weight", "vit.layernorm.weight"),
                ("norm.bias", "vit.layernorm.bias"),
                ("head.weight", "classifier.weight"),
                ("head.bias", "classifier.bias"),
            ]       )
    # fmt: on

    return rename_keys





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					List[Any]=False       ):
    for i in range(config.num_hidden_layers       ):
        if base_model:
            A__				=			""
        else:
            A__				=			"vit."
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        A__				=			state_dict.pop(F"blocks.{i}.attn.qkv.weight"       )
        A__				=			state_dict.pop(F"blocks.{i}.attn.qkv.bias"       )
        # next, add query, keys and values (in that order) to the state dict
        A__				=			in_proj_weight[
            : config.hidden_size, :
        ]
        A__				=			in_proj_bias[: config.hidden_size]
        A__				=			in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        A__				=			in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        A__				=			in_proj_weight[
            -config.hidden_size :, :
        ]
        A__				=			in_proj_bias[-config.hidden_size :]





def 						UpperCamelCase				(							_lowerCamelCase					:					int       ):
    A__				=			["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,       _lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			dct.pop(_lowerCamelCase       )
    A__				=			val





def 						UpperCamelCase				(							):
    A__				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
    A__				=			Image.open(requests.get(_lowerCamelCase						,       stream=_lowerCamelCase       ).raw       )
    return im





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Optional[Any]=False       ):
    A__				=			BitConfig(
        global_padding="same"						,       layer_type="bottleneck"						,       depths=(3, 4, 9)						,       out_features=["stage3"]						,       embedding_dynamic_padding=_lowerCamelCase						,       )
    A__				=			ViTHybridConfig(backbone_config=_lowerCamelCase						,       image_size=3_84						,       num_labels=10_00       )
    A__				=			False

    # load original model from timm
    A__				=			timm.create_model(_lowerCamelCase						,       pretrained=_lowerCamelCase       )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    A__				=			timm_model.state_dict()
    if base_model:
        remove_classification_head_(_lowerCamelCase       )
    A__				=			create_rename_keys(_lowerCamelCase						,       _lowerCamelCase       )
    for src, dest in rename_keys:
        rename_key(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    read_in_q_k_v(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			"huggingface/label-files"
    A__				=			"imagenet-1k-id2label.json"
    A__				=			json.load(open(hf_hub_download(_lowerCamelCase						,       _lowerCamelCase						,       repo_type="dataset"       )						,       "r"       )       )
    A__				=			{int(_lowerCamelCase       ): v for k, v in idalabel.items()}
    A__				=			idalabel
    A__				=			{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        A__				=			ViTHybridModel(_lowerCamelCase       ).eval()
    else:
        A__				=			ViTHybridForImageClassification(_lowerCamelCase       ).eval()
    model.load_state_dict(_lowerCamelCase       )

    # create image processor
    A__				=			create_transform(**resolve_data_config({}						,       model=_lowerCamelCase       )       )
    A__				=			transform.transforms

    A__				=			{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    A__				=			ViTHybridImageProcessor(
        do_resize=_lowerCamelCase						,       size={"shortest_edge": timm_transforms[0].size}						,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,       do_center_crop=_lowerCamelCase						,       crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}						,       do_normalize=_lowerCamelCase						,       image_mean=timm_transforms[-1].mean.tolist()						,       image_std=timm_transforms[-1].std.tolist()						,       )

    A__				=			prepare_img()
    A__				=			transform(_lowerCamelCase       ).unsqueeze(0       )
    A__				=			processor(_lowerCamelCase						,       return_tensors="pt"       ).pixel_values

    # verify pixel values
    assert torch.allclose(_lowerCamelCase						,       _lowerCamelCase       )

    # verify logits
    with torch.no_grad():
        A__				=			model(_lowerCamelCase       )
        A__				=			outputs.logits

    print("Predicted class:"						,       logits.argmax(-1       ).item()       )
    if base_model:
        A__				=			timm_model.forward_features(_lowerCamelCase       )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(_lowerCamelCase						,       outputs.pooler_output						,       atol=1e-3       )
    else:
        A__				=			timm_model(_lowerCamelCase       )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(_lowerCamelCase						,       outputs.logits						,       atol=1e-3       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        Path(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
        print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"       )
        model.save_pretrained(_lowerCamelCase       )
        print(F"Saving processor to {pytorch_dump_folder_path}"       )
        processor.save_pretrained(_lowerCamelCase       )

    if push_to_hub:
        print(F"Pushing model and processor to the hub {vit_name}"       )
        model.push_to_hub(F"ybelkada/{vit_name}"       )
        processor.push_to_hub(F"ybelkada/{vit_name}"       )


if __name__ == "__main__":
  __lowerCAmelCase							:	List[str]			=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_r50_s16_384",
      type=str,
      help="Name of the hybrid ViT timm model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
  )

  __lowerCAmelCase							:	Optional[int]			=parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



