



'''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      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Dict							      =					SwinConfig.from_pretrained(
        """microsoft/swin-tiny-patch4-window7-224"""							,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			)
    lowerCAmelCase__ :  Optional[int]							      =					MaskFormerConfig(backbone_config=UpperCamelCase			)

    lowerCAmelCase__ :  str							      =					"""huggingface/label-files"""
    if "ade20k-full" in model_name:
        # this should be ok
        lowerCAmelCase__ :  List[Any]							      =					847
        lowerCAmelCase__ :  Union[str, Any]							      =					"""maskformer-ade20k-full-id2label.json"""
    elif "ade" in model_name:
        # this should be ok
        lowerCAmelCase__ :  Optional[int]							      =					150
        lowerCAmelCase__ :  Dict							      =					"""ade20k-id2label.json"""
    elif "coco-stuff" in model_name:
        # this should be ok
        lowerCAmelCase__ :  Optional[Any]							      =					171
        lowerCAmelCase__ :  Any							      =					"""maskformer-coco-stuff-id2label.json"""
    elif "coco" in model_name:
        # TODO
        lowerCAmelCase__ :  Optional[Any]							      =					133
        lowerCAmelCase__ :  Union[str, Any]							      =					"""coco-panoptic-id2label.json"""
    elif "cityscapes" in model_name:
        # this should be ok
        lowerCAmelCase__ :  int							      =					19
        lowerCAmelCase__ :  Union[str, Any]							      =					"""cityscapes-id2label.json"""
    elif "vistas" in model_name:
        # this should be ok
        lowerCAmelCase__ :  Tuple							      =					65
        lowerCAmelCase__ :  List[Any]							      =					"""mapillary-vistas-id2label.json"""

    lowerCAmelCase__ :  Any							      =					json.load(open(hf_hub_download(UpperCamelCase							,    UpperCamelCase							,    repo_type="""dataset"""			)							,    """r"""			)			)
    lowerCAmelCase__ :  Union[str, Any]							      =					{int(UpperCamelCase			): v for k, v in idalabel.items()}

    return config
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					[]
    # 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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					dct.pop(UpperCamelCase			)
    lowerCAmelCase__ :  List[str]							      =					val
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
    for i in range(len(backbone_config.depths			)			):
        lowerCAmelCase__ :  Optional[int]							      =					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__ :  Dict							      =					state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""			)
            lowerCAmelCase__ :  List[str]							      =					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
            lowerCAmelCase__ :  Union[str, Any]							      =					in_proj_weight[:dim, :]
            lowerCAmelCase__ :  Optional[int]							      =					in_proj_bias[: dim]
            lowerCAmelCase__ :  Optional[Any]							      =					in_proj_weight[
                dim : dim * 2, :
            ]
            lowerCAmelCase__ :  int							      =					in_proj_bias[
                dim : dim * 2
            ]
            lowerCAmelCase__ :  int							      =					in_proj_weight[
                -dim :, :
            ]
            lowerCAmelCase__ :  int							      =					in_proj_bias[-dim :]
            # fmt: on
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					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)
        lowerCAmelCase__ :  Tuple							      =					state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""			)
        lowerCAmelCase__ :  List[Any]							      =					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
        lowerCAmelCase__ :  Dict							      =					in_proj_weight[: hidden_size, :]
        lowerCAmelCase__ :  Dict							      =					in_proj_bias[:config.hidden_size]
        lowerCAmelCase__ :  str							      =					in_proj_weight[hidden_size : hidden_size * 2, :]
        lowerCAmelCase__ :  Union[str, Any]							      =					in_proj_bias[hidden_size : hidden_size * 2]
        lowerCAmelCase__ :  int							      =					in_proj_weight[-hidden_size :, :]
        lowerCAmelCase__ :  List[str]							      =					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)
        lowerCAmelCase__ :  str							      =					state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""			)
        lowerCAmelCase__ :  int							      =					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
        lowerCAmelCase__ :  Optional[Any]							      =					in_proj_weight[: hidden_size, :]
        lowerCAmelCase__ :  Optional[Any]							      =					in_proj_bias[:config.hidden_size]
        lowerCAmelCase__ :  List[str]							      =					in_proj_weight[hidden_size : hidden_size * 2, :]
        lowerCAmelCase__ :  Any							      =					in_proj_bias[hidden_size : hidden_size * 2]
        lowerCAmelCase__ :  List[str]							      =					in_proj_weight[-hidden_size :, :]
        lowerCAmelCase__ :  Dict							      =					in_proj_bias[-hidden_size :]
    # fmt: on
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase__ :  List[str]							      =					Image.open(requests.get(UpperCamelCase							,    stream=UpperCamelCase			).raw			)
    return im
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase = False			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					get_maskformer_config(UpperCamelCase			)

    # load original state_dict
    with open(UpperCamelCase							,    """rb"""			) as f:
        lowerCAmelCase__ :  List[str]							      =					pickle.load(UpperCamelCase			)
    lowerCAmelCase__ :  List[Any]							      =					data["""model"""]

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

    # rename keys
    lowerCAmelCase__ :  List[Any]							      =					create_rename_keys(UpperCamelCase			)
    for src, dest in rename_keys:
        rename_key(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)
    read_in_swin_q_k_v(UpperCamelCase							,    config.backbone_config			)
    read_in_decoder_q_k_v(UpperCamelCase							,    UpperCamelCase			)

    # update to torch tensors
    for key, value in state_dict.items():
        lowerCAmelCase__ :  Optional[Any]							      =					torch.from_numpy(UpperCamelCase			)

    # load 🤗 model
    lowerCAmelCase__ :  int							      =					MaskFormerForInstanceSegmentation(UpperCamelCase			)
    model.eval()

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

    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					model.load_state_dict(UpperCamelCase							,    strict=UpperCamelCase			)
    assert missing_keys == [
        "model.pixel_level_module.encoder.model.layernorm.weight",
        "model.pixel_level_module.encoder.model.layernorm.bias",
    ]
    assert len(UpperCamelCase			) == 0, f"""Unexpected keys: {unexpected_keys}"""

    # verify results
    lowerCAmelCase__ :  List[Any]							      =					prepare_img()
    if "vistas" in model_name:
        lowerCAmelCase__ :  Any							      =					65
    elif "cityscapes" in model_name:
        lowerCAmelCase__ :  int							      =					65535
    else:
        lowerCAmelCase__ :  Optional[int]							      =					255
    lowerCAmelCase__ :  Union[str, Any]							      =					True if """ade""" in model_name else False
    lowerCAmelCase__ :  Optional[int]							      =					MaskFormerImageProcessor(ignore_index=UpperCamelCase							,    reduce_labels=UpperCamelCase			)

    lowerCAmelCase__ :  List[Any]							      =					image_processor(UpperCamelCase							,    return_tensors="""pt"""			)

    lowerCAmelCase__ :  List[str]							      =					model(**UpperCamelCase			)

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

    if model_name == "maskformer-swin-tiny-ade":
        lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor(
            [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]			)
    assert torch.allclose(outputs.class_queries_logits[0, :3, :3]							,    UpperCamelCase							,    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(UpperCamelCase			).mkdir(exist_ok=UpperCamelCase			)
        model.save_pretrained(UpperCamelCase			)
        image_processor.save_pretrained(UpperCamelCase			)

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







'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      '''focalnet'''




  def __init__(    self  ,__UpperCAmelCase=224  ,__UpperCAmelCase=4  ,__UpperCAmelCase=3  ,__UpperCAmelCase=96  ,__UpperCAmelCase=False  ,__UpperCAmelCase=[192, 384, 768, 768]  ,__UpperCAmelCase=[2, 2, 6, 2]  ,__UpperCAmelCase=[2, 2, 2, 2]  ,__UpperCAmelCase=[3, 3, 3, 3]  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=4.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=False  ,__UpperCAmelCase=1E-4  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-5  ,__UpperCAmelCase=32  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		Optional[Any]:
      super().__init__(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					image_size
      lowerCAmelCase__ :  int							      =					patch_size
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  Dict							      =					embed_dim
      lowerCAmelCase__ :  List[str]							      =					use_conv_embed
      lowerCAmelCase__ :  List[Any]							      =					hidden_sizes
      lowerCAmelCase__ :  Dict							      =					depths
      lowerCAmelCase__ :  List[str]							      =					focal_levels
      lowerCAmelCase__ :  List[str]							      =					focal_windows
      lowerCAmelCase__ :  Dict							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					mlp_ratio
      lowerCAmelCase__ :  Tuple							      =					hidden_dropout_prob
      lowerCAmelCase__ :  Tuple							      =					drop_path_rate
      lowerCAmelCase__ :  Dict							      =					use_layerscale
      lowerCAmelCase__ :  Optional[Any]							      =					layerscale_value
      lowerCAmelCase__ :  str							      =					use_post_layernorm
      lowerCAmelCase__ :  Union[str, Any]							      =					use_post_layernorm_in_modulation
      lowerCAmelCase__ :  int							      =					normalize_modulator
      lowerCAmelCase__ :  Optional[Any]							      =					initializer_range
      lowerCAmelCase__ :  List[str]							      =					layer_norm_eps
      lowerCAmelCase__ :  List[Any]							      =					encoder_stride
      lowerCAmelCase__ :  Dict							      =					["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths      ) + 1      )]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names      )


