

'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__											=	logging.get_logger(__name__)


def 		_A     (    A__     ,				A__=False      ):






 """simple docstring"""



 __lowercase							  = OrderedDict()
 for key, value in state_dict.items():
  if encoder_only and not key.startswith('''head'''      ):
   __lowercase							  = '''segformer.encoder.''' + key
  if key.startswith('''backbone'''      ):
   __lowercase							  = key.replace('''backbone'''     ,				'''segformer.encoder'''      )
  if "patch_embed" in key:
   # replace for example patch_embed1 by patch_embeddings.0
   __lowercase							  = key[key.find('''patch_embed'''      ) + len('''patch_embed'''      )]
   __lowercase							  = key.replace(F"patch_embed{idx}"     ,				F"patch_embeddings.{int(A__      )-1}"      )
  if "norm" in key:
   __lowercase							  = key.replace('''norm'''     ,				'''layer_norm'''      )
  if "segformer.encoder.layer_norm" in key:
   # replace for example layer_norm1 by layer_norm.0
   __lowercase							  = key[key.find('''segformer.encoder.layer_norm'''      ) + len('''segformer.encoder.layer_norm'''      )]
   __lowercase							  = key.replace(F"layer_norm{idx}"     ,				F"layer_norm.{int(A__      )-1}"      )
  if "layer_norm1" in key:
   __lowercase							  = key.replace('''layer_norm1'''     ,				'''layer_norm_1'''      )
  if "layer_norm2" in key:
   __lowercase							  = key.replace('''layer_norm2'''     ,				'''layer_norm_2'''      )
  if "block" in key:
   # replace for example block1 by block.0
   __lowercase							  = key[key.find('''block'''      ) + len('''block'''      )]
   __lowercase							  = key.replace(F"block{idx}"     ,				F"block.{int(A__      )-1}"      )
  if "attn.q" in key:
   __lowercase							  = key.replace('''attn.q'''     ,				'''attention.self.query'''      )
  if "attn.proj" in key:
   __lowercase							  = key.replace('''attn.proj'''     ,				'''attention.output.dense'''      )
  if "attn" in key:
   __lowercase							  = key.replace('''attn'''     ,				'''attention.self'''      )
  if "fc1" in key:
   __lowercase							  = key.replace('''fc1'''     ,				'''dense1'''      )
  if "fc2" in key:
   __lowercase							  = key.replace('''fc2'''     ,				'''dense2'''      )
  if "linear_pred" in key:
   __lowercase							  = key.replace('''linear_pred'''     ,				'''classifier'''      )
  if "linear_fuse" in key:
   __lowercase							  = key.replace('''linear_fuse.conv'''     ,				'''linear_fuse'''      )
   __lowercase							  = key.replace('''linear_fuse.bn'''     ,				'''batch_norm'''      )
  if "linear_c" in key:
   # replace for example linear_c4 by linear_c.3
   __lowercase							  = key[key.find('''linear_c'''      ) + len('''linear_c'''      )]
   __lowercase							  = key.replace(F"linear_c{idx}"     ,				F"linear_c.{int(A__      )-1}"      )
  if key.startswith('''head'''      ):
   __lowercase							  = key.replace('''head'''     ,				'''classifier'''      )
  __lowercase							  = value

 return new_state_dict


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 for i in range(config.num_encoder_blocks      ):
  for j in range(config.depths[i]      ):
   # read in weights + bias of keys and values (which is a single matrix in the original implementation)
   __lowercase							  = state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"      )
   __lowercase							  = state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.bias"      )
   # next, add keys and values (in that order) to the state dict
   __lowercase							  = kv_weight[
       : config.hidden_sizes[i], :
   ]
   __lowercase							  = kv_bias[: config.hidden_sizes[i]]
   __lowercase							  = kv_weight[
       config.hidden_sizes[i] :, :
   ]
   __lowercase							  = kv_bias[
       config.hidden_sizes[i] :
   ]


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __lowercase							  = Image.open(requests.get(A__     ,				stream=A__      ).raw      )

 return image


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = SegformerConfig()
 __lowercase							  = False

 # set attributes based on model_name
 __lowercase							  = '''huggingface/label-files'''
 if "segformer" in model_name:
  __lowercase							  = model_name[len('''segformer.'''      ) : len('''segformer.'''      ) + 2]
  if "ade" in model_name:
   __lowercase							  = 150
   __lowercase							  = '''ade20k-id2label.json'''
   __lowercase							  = (1, 150, 128, 128)
  elif "city" in model_name:
   __lowercase							  = 19
   __lowercase							  = '''cityscapes-id2label.json'''
   __lowercase							  = (1, 19, 128, 128)
  else:
   raise ValueError(F"Model {model_name} not supported"      )
 elif "mit" in model_name:
  __lowercase							  = True
  __lowercase							  = model_name[4:6]
  __lowercase							  = 1000
  __lowercase							  = '''imagenet-1k-id2label.json'''
  __lowercase							  = (1, 1000)
 else:
  raise ValueError(F"Model {model_name} not supported"      )

 # set config attributes
 __lowercase							  = json.load(open(hf_hub_download(A__     ,				A__     ,				repo_type='''dataset'''      )     ,				'''r'''      )      )
 __lowercase							  = {int(A__      ): v for k, v in idalabel.items()}
 __lowercase							  = idalabel
 __lowercase							  = {v: k for k, v in idalabel.items()}
 if size == "b0":
  pass
 elif size == "b1":
  __lowercase							  = [64, 128, 320, 512]
  __lowercase							  = 256
 elif size == "b2":
  __lowercase							  = [64, 128, 320, 512]
  __lowercase							  = 768
  __lowercase							  = [3, 4, 6, 3]
 elif size == "b3":
  __lowercase							  = [64, 128, 320, 512]
  __lowercase							  = 768
  __lowercase							  = [3, 4, 18, 3]
 elif size == "b4":
  __lowercase							  = [64, 128, 320, 512]
  __lowercase							  = 768
  __lowercase							  = [3, 8, 27, 3]
 elif size == "b5":
  __lowercase							  = [64, 128, 320, 512]
  __lowercase							  = 768
  __lowercase							  = [3, 6, 40, 3]
 else:
  raise ValueError(F"Size {size} not supported"      )

 # load image processor (only resize + normalize)
 __lowercase							  = SegformerImageProcessor(
     image_scale=(512, 512)     ,				keep_ratio=A__     ,				align=A__     ,				do_random_crop=A__      )

 # prepare image
 __lowercase							  = prepare_img()
 __lowercase							  = image_processor(images=A__     ,				return_tensors='''pt'''      ).pixel_values

 logger.info(F"Converting model {model_name}..."      )

 # load original state dict
 if encoder_only:
  __lowercase							  = torch.load(A__     ,				map_location=torch.device('''cpu'''      )      )
 else:
  __lowercase							  = torch.load(A__     ,				map_location=torch.device('''cpu'''      )      )['''state_dict''']

 # rename keys
 __lowercase							  = rename_keys(A__     ,				encoder_only=A__      )
 if not encoder_only:
  del state_dict["decode_head.conv_seg.weight"]
  del state_dict["decode_head.conv_seg.bias"]

 # key and value matrices need special treatment
 read_in_k_v(A__     ,				A__      )

 # create HuggingFace model and load state dict
 if encoder_only:
  __lowercase							  = False
  __lowercase							  = SegformerForImageClassification(A__      )
 else:
  __lowercase							  = SegformerForSemanticSegmentation(A__      )
 model.load_state_dict(A__      )
 model.eval()

 # forward pass
 __lowercase							  = model(A__      )
 __lowercase							  = outputs.logits

 # set expected_slice based on model name
 # ADE20k checkpoints
 if model_name == "segformer.b0.512x512.ade.160k":
  __lowercase							  = torch.tensor(
      [
          [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
          [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
          [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
      ]      )
 elif model_name == "segformer.b1.512x512.ade.160k":
  __lowercase							  = torch.tensor(
      [
          [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
          [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
          [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
      ]      )
 elif model_name == "segformer.b2.512x512.ade.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
          [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
          [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
      ]      )
 elif model_name == "segformer.b3.512x512.ade.160k":
  __lowercase							  = torch.tensor(
      [
          [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
          [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
          [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
      ]      )
 elif model_name == "segformer.b4.512x512.ade.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
          [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
          [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
      ]      )
 elif model_name == "segformer.b5.640x640.ade.160k":
  __lowercase							  = torch.tensor(
      [
          [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
          [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
          [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
      ]      )
 # Cityscapes checkpoints
 elif model_name == "segformer.b0.1024x1024.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
          [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
          [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
      ]      )
 elif model_name == "segformer.b0.512x1024.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
          [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
          [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
      ]      )
 elif model_name == "segformer.b0.640x1280.city.160k":
  __lowercase							  = torch.tensor(
      [
          [
              [-1.13_72e01, -1.27_87e01, -1.34_77e01],
              [-1.25_36e01, -1.41_94e01, -1.44_09e01],
              [-1.32_17e01, -1.48_88e01, -1.53_27e01],
          ],
          [
              [-1.47_91e01, -1.71_22e01, -1.82_77e01],
              [-1.71_63e01, -1.91_92e01, -1.95_33e01],
              [-1.78_97e01, -1.99_91e01, -2.03_15e01],
          ],
          [
              [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
              [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
              [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
          ],
      ]      )
 elif model_name == "segformer.b0.768x768.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
          [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
          [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
      ]      )
 elif model_name == "segformer.b1.1024x1024.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
          [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
          [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
      ]      )
 elif model_name == "segformer.b2.1024x1024.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
          [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
          [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
      ]      )
 elif model_name == "segformer.b3.1024x1024.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
          [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
          [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
      ]      )
 elif model_name == "segformer.b4.1024x1024.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
          [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
          [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
      ]      )
 elif model_name == "segformer.b5.1024x1024.city.160k":
  __lowercase							  = torch.tensor(
      [
          [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
          [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
          [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
      ]      )
 else:
  __lowercase							  = logits.argmax(-1      ).item()
  print('''Predicted class:'''     ,				model.config.idalabel[predicted_class_idx]      )

 # verify logits
 if not encoder_only:
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3, :3, :3]     ,				A__     ,				atol=1e-2      )

 # finally, save model and image processor
 logger.info(F"Saving PyTorch 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 __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''segformer.b0.512x512.ade.160k''',
     type=str,
     help='''Name of the model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 lowerCAmelCase__											=	parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''






from __future__ import annotations


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # We need to create solution object to save path.
 __lowercase							  = [[0 for _ in range(A__      )] for _ in range(A__      )]
 __lowercase							  = run_maze(A__     ,				0     ,				0     ,				A__      )
 if solved:
  print('''\n'''.join(str(A__      ) for row in solutions      )      )
 else:
  print('''No solution exists!'''      )
 return solved


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # Final check point.
 if i == j == (size - 1):
  __lowercase							  = 1
  return True

 __lowercase							  = (not i < 0) and (not j < 0)  # Check lower bounds
 __lowercase							  = (i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  __lowercase							  = (not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   __lowercase							  = 1

   # check for directions
   if (
       run_maze(A__     ,				i + 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j + 1     ,				A__      )
       or run_maze(A__     ,				i - 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j - 1     ,				A__      )
   ):
    return True

   __lowercase							  = 0
   return False
 return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()

