



from collections import deque

from .hash_table import HashTable




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], *_snake_case  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            super().__init__(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->Dict:
            snake_case__							:	int   	=	deque([]					) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(_snake_case					)
            snake_case__							:	Dict   	=	self.values[key]






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            return (
                sum(self.charge_factor - len(_snake_case					) for slot in self.values					)
                / self.size_table
                * self.charge_factor
            )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Optional[int]=None					)   ->Optional[Any]:
            if not (
                len(self.values[key]					) == self.charge_factor and self.values.count(_snake_case					) == 0
            ):
                   return key
            return super()._collision_resolution(_snake_case, _snake_case					)











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()
a_   :str        =      logging.get_logger(__name__)
def        lowercase_							(A : str      ):
       snake_case__							:	Tuple   	=	SwinConfig.from_pretrained(
           'microsoft/swin-tiny-patch4-window7-224'		,					out_features=['stage1', 'stage2', 'stage3', 'stage4']      )
       snake_case__							:	List[Any]   	=	MaskFormerConfig(backbone_config=A      )

       snake_case__							:	Union[str, Any]   	=	'huggingface/label-files'
       if "ade20k-full" in model_name:
              # this should be ok
              snake_case__							:	Dict   	=	8_4_7
              snake_case__							:	List[str]   	=	'maskformer-ade20k-full-id2label.json'
       elif "ade" in model_name:
              # this should be ok
              snake_case__							:	Union[str, Any]   	=	1_5_0
              snake_case__							:	Any   	=	'ade20k-id2label.json'
       elif "coco-stuff" in model_name:
              # this should be ok
              snake_case__							:	List[str]   	=	1_7_1
              snake_case__							:	Union[str, Any]   	=	'maskformer-coco-stuff-id2label.json'
       elif "coco" in model_name:
              # TODO
              snake_case__							:	Dict   	=	1_3_3
              snake_case__							:	str   	=	'coco-panoptic-id2label.json'
       elif "cityscapes" in model_name:
              # this should be ok
              snake_case__							:	List[str]   	=	1_9
              snake_case__							:	Union[str, Any]   	=	'cityscapes-id2label.json'
       elif "vistas" in model_name:
              # this should be ok
              snake_case__							:	Tuple   	=	6_5
              snake_case__							:	List[str]   	=	'mapillary-vistas-id2label.json'

       snake_case__							:	Dict   	=	json.load(open(hf_hub_download(A		,					A		,					repo_type='dataset'      )		,					'r'      )      )
       snake_case__							:	List[str]   	=	{int(A      ): v for k, v in idalabel.items()}

       return config
def        lowercase_							(A : Any      ):
       snake_case__							:	Optional[int]   	=	[]
       # 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        lowercase_							(A : Tuple		,					A : Tuple		,					A : Optional[Any]      ):
       snake_case__							:	Optional[int]   	=	dct.pop(A      )
       snake_case__							:	Union[str, Any]   	=	val
def        lowercase_							(A : Optional[Any]		,					A : Tuple      ):
       snake_case__							:	Optional[int]   	=	[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
       for i in range(len(backbone_config.depths      )      ):
              snake_case__							:	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)
                     snake_case__							:	int   	=	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''      )
                     snake_case__							:	Tuple   	=	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
                     snake_case__							:	str   	=	in_proj_weight[:dim, :]
                     snake_case__							:	int   	=	in_proj_bias[: dim]
                     snake_case__							:	List[Any]   	=	in_proj_weight[
                         dim : dim * 2, :
                     ]
                     snake_case__							:	List[str]   	=	in_proj_bias[
                         dim : dim * 2
                     ]
                     snake_case__							:	List[Any]   	=	in_proj_weight[
                         -dim :, :
                     ]
                     snake_case__							:	Dict   	=	in_proj_bias[-dim :]
                     # fmt: on
def        lowercase_							(A : List[str]		,					A : List[Any]      ):
       # fmt: off
       snake_case__							:	str   	=	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)
              snake_case__							:	List[Any]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''      )
              snake_case__							:	int   	=	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
              snake_case__							:	Any   	=	in_proj_weight[: hidden_size, :]
              snake_case__							:	Tuple   	=	in_proj_bias[:config.hidden_size]
              snake_case__							:	List[str]   	=	in_proj_weight[hidden_size : hidden_size * 2, :]
              snake_case__							:	Dict   	=	in_proj_bias[hidden_size : hidden_size * 2]
              snake_case__							:	Any   	=	in_proj_weight[-hidden_size :, :]
              snake_case__							:	int   	=	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)
              snake_case__							:	List[Any]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''      )
              snake_case__							:	List[str]   	=	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
              snake_case__							:	Optional[int]   	=	in_proj_weight[: hidden_size, :]
              snake_case__							:	Optional[Any]   	=	in_proj_bias[:config.hidden_size]
              snake_case__							:	int   	=	in_proj_weight[hidden_size : hidden_size * 2, :]
              snake_case__							:	List[str]   	=	in_proj_bias[hidden_size : hidden_size * 2]
              snake_case__							:	List[str]   	=	in_proj_weight[-hidden_size :, :]
              snake_case__							:	str   	=	in_proj_bias[-hidden_size :]
       # fmt: on
def        lowercase_							():
       snake_case__							:	Any   	=	'http://images.cocodataset.org/val2017/000000039769.jpg'
       snake_case__							:	int   	=	Image.open(requests.get(A		,					stream=A      ).raw      )
       return im
@torch.no_grad()
def        lowercase_							(A : str		,					A : str		,					A : str		,					A : bool = False      ):
       snake_case__							:	Optional[int]   	=	get_maskformer_config(A      )

       # load original state_dict
       with open(A		,					'rb'      ) as f:
              snake_case__							:	List[Any]   	=	pickle.load(A      )
       snake_case__							:	Optional[int]   	=	data['model']

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

       # rename keys
       snake_case__							:	List[str]   	=	create_rename_keys(A      )
       for src, dest in rename_keys:
              rename_key(A		,					A		,					A      )
       read_in_swin_q_k_v(A		,					config.backbone_config      )
       read_in_decoder_q_k_v(A		,					A      )

       # update to torch tensors
       for key, value in state_dict.items():
              snake_case__							:	int   	=	torch.from_numpy(A      )

       # load 🤗 model
       snake_case__							:	str   	=	MaskFormerForInstanceSegmentation(A      )
       model.eval()

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

       snake_case__	,				snake_case__							:	Union[str, Any]   	=	model.load_state_dict(A		,					strict=A      )
       assert missing_keys == [
           "model.pixel_level_module.encoder.model.layernorm.weight",
           "model.pixel_level_module.encoder.model.layernorm.bias",
       ]
       assert len(A      ) == 0, F'''Unexpected keys: {unexpected_keys}'''

       # verify results
       snake_case__							:	Optional[Any]   	=	prepare_img()
       if "vistas" in model_name:
              snake_case__							:	int   	=	6_5
       elif "cityscapes" in model_name:
              snake_case__							:	Dict   	=	6_5_5_3_5
       else:
              snake_case__							:	Tuple   	=	2_5_5
       snake_case__							:	Optional[int]   	=	True if 'ade' in model_name else False
       snake_case__							:	Dict   	=	MaskFormerImageProcessor(ignore_index=A		,					reduce_labels=A      )

       snake_case__							:	Any   	=	image_processor(A		,					return_tensors='pt'      )

       snake_case__							:	Any   	=	model(**A      )

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

       if model_name == "maskformer-swin-tiny-ade":
              snake_case__							:	Tuple   	=	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]		,					A		,					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(A      ).mkdir(exist_ok=A      )
              model.save_pretrained(A      )
              image_processor.save_pretrained(A      )

       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__":
 a_   :Optional[int]        =      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."
 )

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






