

"""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()
SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =						logging.get_logger(__name__)



def 	UpperCAmelCase__   (				A__   )      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224"	,    out_features=["stage1", "stage2", "stage3", "stage4"]   )
   lowerCamelCase__						=			MaskFormerConfig(backbone_config=__lowerCAmelCase   )

   lowerCamelCase__						=			"huggingface/label-files"
   if "ade20k-full" in model_name:
      # this should be ok
      lowerCamelCase__						=			847
      lowerCamelCase__						=			"maskformer-ade20k-full-id2label.json"
   elif "ade" in model_name:
      # this should be ok
      lowerCamelCase__						=			150
      lowerCamelCase__						=			"ade20k-id2label.json"
   elif "coco-stuff" in model_name:
      # this should be ok
      lowerCamelCase__						=			171
      lowerCamelCase__						=			"maskformer-coco-stuff-id2label.json"
   elif "coco" in model_name:
      # TODO
      lowerCamelCase__						=			133
      lowerCamelCase__						=			"coco-panoptic-id2label.json"
   elif "cityscapes" in model_name:
      # this should be ok
      lowerCamelCase__						=			19
      lowerCamelCase__						=			"cityscapes-id2label.json"
   elif "vistas" in model_name:
      # this should be ok
      lowerCamelCase__						=			65
      lowerCamelCase__						=			"mapillary-vistas-id2label.json"

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

   return config



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   lowerCamelCase__						=			[]
   # 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__   (				A__	,    A__	,    A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			dct.pop(__lowerCAmelCase   )
   lowerCamelCase__						=			val



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int:




   """simple docstring"""
   lowerCamelCase__						=			[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
   for i in range(len(backbone_config.depths   )   ):
      lowerCamelCase__						=			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__						=			state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'   )
         lowerCamelCase__						=			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__						=			in_proj_weight[:dim, :]
         lowerCamelCase__						=			in_proj_bias[: dim]
         lowerCamelCase__						=			in_proj_weight[
             dim : dim * 2, :
         ]
         lowerCamelCase__						=			in_proj_bias[
             dim : dim * 2
         ]
         lowerCamelCase__						=			in_proj_weight[
             -dim :, :
         ]
         lowerCamelCase__						=			in_proj_bias[-dim :]
         # fmt: on



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Any:




   """simple docstring"""
   # fmt: off
   lowerCamelCase__						=			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__						=			state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'   )
      lowerCamelCase__						=			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__						=			in_proj_weight[: hidden_size, :]
      lowerCamelCase__						=			in_proj_bias[:config.hidden_size]
      lowerCamelCase__						=			in_proj_weight[hidden_size : hidden_size * 2, :]
      lowerCamelCase__						=			in_proj_bias[hidden_size : hidden_size * 2]
      lowerCamelCase__						=			in_proj_weight[-hidden_size :, :]
      lowerCamelCase__						=			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__						=			state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'   )
      lowerCamelCase__						=			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__						=			in_proj_weight[: hidden_size, :]
      lowerCamelCase__						=			in_proj_bias[:config.hidden_size]
      lowerCamelCase__						=			in_proj_weight[hidden_size : hidden_size * 2, :]
      lowerCamelCase__						=			in_proj_bias[hidden_size : hidden_size * 2]
      lowerCamelCase__						=			in_proj_weight[-hidden_size :, :]
      lowerCamelCase__						=			in_proj_bias[-hidden_size :]
   # fmt: on



def 	UpperCAmelCase__   (				)      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowerCamelCase__						=			Image.open(requests.get(__lowerCAmelCase	,    stream=__lowerCAmelCase   ).raw   )
   return im



@torch.no_grad()
def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__ = False   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			get_maskformer_config(__lowerCAmelCase   )

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

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

   # rename keys
   lowerCamelCase__						=			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():
      lowerCamelCase__						=			torch.from_numpy(__lowerCAmelCase   )

   # load 🤗 model
   lowerCamelCase__						=			MaskFormerForInstanceSegmentation(__lowerCAmelCase   )
   model.eval()

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

   lowerCamelCase__					,       lowerCamelCase__						=			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
   lowerCamelCase__						=			prepare_img()
   if "vistas" in model_name:
      lowerCamelCase__						=			65
   elif "cityscapes" in model_name:
      lowerCamelCase__						=			6_5535
   else:
      lowerCamelCase__						=			255
   lowerCamelCase__						=			True if "ade" in model_name else False
   lowerCamelCase__						=			MaskFormerImageProcessor(ignore_index=__lowerCAmelCase	,    reduce_labels=__lowerCAmelCase   )

   lowerCamelCase__						=			image_processor(__lowerCAmelCase	,    return_tensors="pt"   )

   lowerCamelCase__						=			model(**__lowerCAmelCase   )

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

   if model_name == "maskformer-swin-tiny-ade":
      lowerCamelCase__						=			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]	,    __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__":
     SCREAMING_SNAKE_CASE_     :			Union[str, 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.'''
     )

     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =						parser.parse_args()
     convert_maskformer_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )







"""simple docstring"""




import operator as op



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			lambda A__	,    A__   : int(x / y   )  # noqa: E731 integer division operation
   lowerCamelCase__						=			{
       "^": op.pow,
       "*": op.mul,
       "/": div,
       "+": op.add,
       "-": op.sub,
   }  # operators & their respective operation

   # print table header
   print("Symbol".center(8   )	,    "Action".center(12   )	,    "Stack"	,    sep=" | "   )
   print("-" * (30 + len(A__   ))   )

   for x in post_fix:
      if x.isdigit():  # if x in digit
         stack.append(A__   )  # append x to stack
         # output in tabular format
         print(x.rjust(8   )	,    ("push(" + x + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )
      else:
         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + a + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         stack.append(
             str(opr[x](int(A__   )	,    int(A__   )   )   )   )  # evaluate the 2 values popped from stack & push result to stack
         # output in tabular format
         print(
             x.rjust(8   )	,    ("push(" + a + x + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "	,    )

   return int(stack[0]   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
     print('''\n\tResult = ''', solve(Postfix))




