



"""simple docstring"""





import heapq
import sys

import numpy as np

lowerCamelCase      =		tuple[int, int]
class    lowercase__     :







       '''simple docstring'''


       def __init__(							self    :						Union[str, Any]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							[]
             UpperCAmelCase_    				=							set()



       def   lowercase__       (							self    :						Union[str, Any]					)				->		Optional[int]:


             '''simple docstring'''




             if not self.empty():
                   return self.elements[0][0]
             else:
                   return float("inf"					)



       def   lowercase__       (							self    :						str					)				->		List[Any]:


             '''simple docstring'''




             return len(self.elements					) == 0



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Optional[int]					)				->		Union[str, Any]:


             '''simple docstring'''




             if item not in self.set:
                   heapq.heappush(self.elements	,       (priority, item)					)
                   self.set.add(_UpperCAmelCase					)
             else:
                   # update
                   # print("update", item)
                   UpperCAmelCase_    				=							[]
                   ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heapq.heappop(self.elements					)
                   while x != item:
                         temp.append((pri, x)					)
                         ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heapq.heappop(self.elements					)
                   temp.append((priority, item)					)
                   for pro, xxx in temp:
                         heapq.heappush(self.elements	,       (pro, xxx)					)



       def   lowercase__       (							self    :						Optional[int]	,       _UpperCAmelCase    :						Dict					)				->		Optional[Any]:


             '''simple docstring'''




             if item in self.set:
                   self.set.remove(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							[]
                   ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heapq.heappop(self.elements					)
                   while x != item:
                         temp.append((pro, x)					)
                         ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heapq.heappop(self.elements					)
                   for prito, yyy in temp:
                         heapq.heappush(self.elements	,       (prito, yyy)					)



       def   lowercase__       (							self    :						int					)				->		Optional[Any]:


             '''simple docstring'''




             return self.elements[0][1]







       def   lowercase__       (							self    :						Optional[int]					)				->		Union[str, Any]:


             '''simple docstring'''




             ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heapq.heappop(self.elements					)
             self.set.remove(_UpperCAmelCase					)
             return (priority, item)



def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # euclidean distance
      UpperCAmelCase_    				=							np.array(lowerCAmelCase__   )
      UpperCAmelCase_    				=							np.array(lowerCAmelCase__   )
      return np.linalg.norm(a - b   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # integer division by time variable
      return consistent_heuristic(lowerCAmelCase__			,    lowerCAmelCase__   ) // t
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # manhattan distance
      return abs(p[0] - goal[0]   ) + abs(p[1] - goal[1]   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							g_function[start] + Wa * heuristics[i](lowerCAmelCase__			,    lowerCAmelCase__   )
      return ans
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							np.chararray((n, n)   )
      for i in range(lowerCAmelCase__   ):
            for j in range(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							"*"

      for i in range(lowerCAmelCase__   ):
            for j in range(lowerCAmelCase__   ):
                  if (j, (n - 1) - i) in blocks:
                        UpperCAmelCase_    				=							"#"

      UpperCAmelCase_    				=							"-"
      UpperCAmelCase_    				=							back_pointer[goal]
      while x != start:
            ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							x
            # print(x)
            UpperCAmelCase_    				=							"-"
            UpperCAmelCase_    				=							back_pointer[x]
      UpperCAmelCase_    				=							"-"

      for i in range(lowerCAmelCase__   ):
            for j in range(lowerCAmelCase__   ):
                  if (i, j) == (0, n - 1):
                        print(grid[i][j]			,    end=" "   )
                        print("<-- End position"			,    end=" "   )
                  else:
                        print(grid[i][j]			,    end=" "   )
            print()
      print("^"   )
      print("Start position"   )
      print()
      print("# is an obstacle"   )
      print("- is the path taken by algorithm"   )
      print("PATH TAKEN BY THE ALGORITHM IS:-"   )
      UpperCAmelCase_    				=							back_pointer[goal]
      while x != start:
            print(lowerCAmelCase__			,    end=" "   )
            UpperCAmelCase_    				=							back_pointer[x]
      print(lowerCAmelCase__   )
      sys.exit()
def      a__			(   lowerCAmelCase__   ):
      if p[0] < 0 or p[0] > n - 1:
            return False
      if p[1] < 0 or p[1] > n - 1:
            return False
      return True
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    ):
      for itera in range(lowerCAmelCase__   ):
            open_list[itera].remove_element(lowerCAmelCase__   )
      # print("s", s)
      # print("j", j)
      ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							s
      UpperCAmelCase_    				=							(x - 1, y)
      UpperCAmelCase_    				=							(x + 1, y)
      UpperCAmelCase_    				=							(x, y + 1)
      UpperCAmelCase_    				=							(x, y - 1)

      for neighbours in [left, right, up, down]:
            if neighbours not in blocks:
                  if valid(lowerCAmelCase__   ) and neighbours not in visited:
                        # print("neighbour", neighbours)
                        visited.add(lowerCAmelCase__   )
                        UpperCAmelCase_    				=							-1
                        UpperCAmelCase_    				=							float("inf"   )

                  if valid(lowerCAmelCase__   ) and g_function[neighbours] > g_function[s] + 1:
                        UpperCAmelCase_    				=							g_function[s] + 1
                        UpperCAmelCase_    				=							s
                        if neighbours not in close_list_anchor:
                              open_list[0].put(lowerCAmelCase__			,    key(lowerCAmelCase__			,    0			,    lowerCAmelCase__			,    lowerCAmelCase__   )   )
                              if neighbours not in close_list_inad:
                                    for var in range(1			,    lowerCAmelCase__   ):
                                          if key(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ) <= Wa * key(
                                              lowerCAmelCase__			,    0			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
                                                open_list[j].put(
                                                    lowerCAmelCase__			,    key(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )   )
def      a__			(   ):
      UpperCAmelCase_    				=							[]
      for x in range(1			,    5   ):
            for y in range(1			,    6   ):
                  some_list.append((x, y)   )

      for x in range(15			,    20   ):
            some_list.append((x, 17)   )

      for x in range(10			,    19   ):
            for y in range(1			,    15   ):
                  some_list.append((x, y)   )

    # L block
      for x in range(1			,    4   ):
            for y in range(12			,    19   ):
                  some_list.append((x, y)   )
      for x in range(3			,    13   ):
            for y in range(16			,    19   ):
                  some_list.append((x, y)   )
      return some_list


lowerCamelCase      =		{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowerCamelCase      =		[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowerCamelCase      =		make_common_ground()


lowerCamelCase      =		blocks_blk
# hyper parameters
lowerCamelCase      =		1
lowerCamelCase      =		1
lowerCamelCase      =		20
lowerCamelCase      =		3  # one consistent and two other inconsistent

# start and end destination
lowerCamelCase      =		(0, 0)
lowerCamelCase      =		(n - 1, n - 1)

lowerCamelCase      =		1
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							{start: 0, goal: float("inf"   )}
      UpperCAmelCase_    				=							{start: -1, goal: -1}
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							set()

      for i in range(lowerCAmelCase__   ):
            open_list.append(PriorityQueue()   )
            open_list[i].put(lowerCAmelCase__			,    key(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )   )

      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      while open_list[0].minkey() < float("inf"   ):
            for i in range(1			,    lowerCAmelCase__   ):
                  # print(open_list[0].minkey(), open_list[i].minkey())
                  if open_list[i].minkey() <= Wa * open_list[0].minkey():
                        global t
                        t += 1
                        if g_function[goal] <= open_list[i].minkey():
                              if g_function[goal] < float("inf"   ):
                                    do_something(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
                        else:
                              UpperCAmelCase_				,					UpperCAmelCase_    				=							open_list[i].top_show()
                              visited.add(lowerCAmelCase__   )
                              expand_state(
                                  lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    )
                              close_list_inad.append(lowerCAmelCase__   )
                  else:
                        if g_function[goal] <= open_list[0].minkey():
                              if g_function[goal] < float("inf"   ):
                                    do_something(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
                        else:
                              UpperCAmelCase_    				=							open_list[0].top_show()
                              visited.add(lowerCAmelCase__   )
                              expand_state(
                                  lowerCAmelCase__			,    0			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    )
                              close_list_anchor.append(lowerCAmelCase__   )
      print("No path found to goal"   )
      print()
      for i in range(n - 1			,    -1			,    -1   ):
            for j in range(lowerCAmelCase__   ):
                  if (j, i) in blocks:
                        print("#"			,    end=" "   )
                  elif (j, i) in back_pointer:
                        if (j, i) == (n - 1, n - 1):
                              print("*"			,    end=" "   )
                        else:
                              print("-"			,    end=" "   )
                  else:
                        print("*"			,    end=" "   )
                  if (j, i) == (n - 1, n - 1):
                        print("<-- End position"			,    end=" "   )
            print()
      print("^"   )
      print("Start position"   )
      print()
      print("# is an obstacle"   )
      print("- is the path taken by algorithm"   )


if __name__ == "__main__":
  multi_a_star(start, goal, n_heuristic)









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

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

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

      return config
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      # 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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dct.pop(lowerCAmelCase__   )
      UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
      for i in range(len(backbone_config.depths   )   ):
            UpperCAmelCase_    				=							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)
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""   )
                  UpperCAmelCase_    				=							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
                  UpperCAmelCase_    				=							in_proj_weight[:dim, :]
                  UpperCAmelCase_    				=							in_proj_bias[: dim]
                  UpperCAmelCase_    				=							in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_    				=							in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[-dim :]
                  # fmt: on
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # fmt: off
      UpperCAmelCase_    				=							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)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							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)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							in_proj_bias[-hidden_size :]
      # fmt: on
def      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   )
      return im
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							get_maskformer_config(lowerCAmelCase__   )

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

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

      # rename keys
      UpperCAmelCase_    				=							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():
            UpperCAmelCase_    				=							torch.from_numpy(lowerCAmelCase__   )

      # load 🤗 model
      UpperCAmelCase_    				=							MaskFormerForInstanceSegmentation(lowerCAmelCase__   )
      model.eval()

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

      UpperCAmelCase_				,					UpperCAmelCase_    				=							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
      UpperCAmelCase_    				=							prepare_img()
      if "vistas" in model_name:
            UpperCAmelCase_    				=							65
      elif "cityscapes" in model_name:
            UpperCAmelCase_    				=							65535
      else:
            UpperCAmelCase_    				=							255
      UpperCAmelCase_    				=							True if "ade" in model_name else False
      UpperCAmelCase_    				=							MaskFormerImageProcessor(ignore_index=lowerCAmelCase__			,    reduce_labels=lowerCAmelCase__   )

      UpperCAmelCase_    				=							image_processor(lowerCAmelCase__			,    return_tensors="pt"   )

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

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

      if model_name == "maskformer-swin-tiny-ade":
            UpperCAmelCase_    				=							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__":
  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
  )




