
import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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






def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]  ):






 """simple docstring"""


 __lowerCAmelCase         =							MobileViTConfig()

 # size of the architecture
 if "mobilevit_s" in mobilevit_name:
  __lowerCAmelCase         =							[1_4_4, 1_9_2, 2_4_0]
  __lowerCAmelCase         =							[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
 elif "mobilevit_xs" in mobilevit_name:
  __lowerCAmelCase         =							[9_6, 1_2_0, 1_4_4]
  __lowerCAmelCase         =							[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
 elif "mobilevit_xxs" in mobilevit_name:
  __lowerCAmelCase         =							[6_4, 8_0, 9_6]
  __lowerCAmelCase         =							[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
  __lowerCAmelCase         =							0.05
  __lowerCAmelCase         =							2.0

 if mobilevit_name.startswith("deeplabv3_"  ):
  __lowerCAmelCase         =							5_1_2
  __lowerCAmelCase         =							1_6
  __lowerCAmelCase         =							2_1
  __lowerCAmelCase         =							"pascal-voc-id2label.json"
 else:
  __lowerCAmelCase         =							1_0_0_0
  __lowerCAmelCase         =							"imagenet-1k-id2label.json"

 __lowerCAmelCase         =							"huggingface/label-files"
 __lowerCAmelCase         =							json.load(open(hf_hub_download(UpperCamelCase			,							UpperCamelCase			,							repo_type="dataset"  )			,							"r"  )  )
 __lowerCAmelCase         =							{int(UpperCamelCase  ): v for k, v in idalabel.items()}
 __lowerCAmelCase         =							idalabel
 __lowerCAmelCase         =							{v: k for k, v in idalabel.items()}

 return config






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     str=False  ):






 """simple docstring"""


 for i in range(1			,							6  ):
  if F"layer_{i}." in name:
   __lowerCAmelCase         =							name.replace(F"layer_{i}."			,							F"encoder.layer.{i - 1}."  )

 if "conv_1." in name:
  __lowerCAmelCase         =							name.replace("conv_1."			,							"conv_stem."  )
 if ".block." in name:
  __lowerCAmelCase         =							name.replace(".block."			,							"."  )

 if "exp_1x1" in name:
  __lowerCAmelCase         =							name.replace("exp_1x1"			,							"expand_1x1"  )
 if "red_1x1" in name:
  __lowerCAmelCase         =							name.replace("red_1x1"			,							"reduce_1x1"  )
 if ".local_rep.conv_3x3." in name:
  __lowerCAmelCase         =							name.replace(".local_rep.conv_3x3."			,							".conv_kxk."  )
 if ".local_rep.conv_1x1." in name:
  __lowerCAmelCase         =							name.replace(".local_rep.conv_1x1."			,							".conv_1x1."  )
 if ".norm." in name:
  __lowerCAmelCase         =							name.replace(".norm."			,							".normalization."  )
 if ".conv." in name:
  __lowerCAmelCase         =							name.replace(".conv."			,							".convolution."  )
 if ".conv_proj." in name:
  __lowerCAmelCase         =							name.replace(".conv_proj."			,							".conv_projection."  )

 for i in range(0			,							2  ):
  for j in range(0			,							4  ):
   if F".{i}.{j}." in name:
    __lowerCAmelCase         =							name.replace(F".{i}.{j}."			,							F".{i}.layer.{j}."  )

 for i in range(2			,							6  ):
  for j in range(0			,							4  ):
   if F".{i}.{j}." in name:
    __lowerCAmelCase         =							name.replace(F".{i}.{j}."			,							F".{i}."  )
    if "expand_1x1" in name:
     __lowerCAmelCase         =							name.replace("expand_1x1"			,							"downsampling_layer.expand_1x1"  )
    if "conv_3x3" in name:
     __lowerCAmelCase         =							name.replace("conv_3x3"			,							"downsampling_layer.conv_3x3"  )
    if "reduce_1x1" in name:
     __lowerCAmelCase         =							name.replace("reduce_1x1"			,							"downsampling_layer.reduce_1x1"  )

 for i in range(2			,							5  ):
  if F".global_rep.{i}.weight" in name:
   __lowerCAmelCase         =							name.replace(F".global_rep.{i}.weight"			,							".layernorm.weight"  )
  if F".global_rep.{i}.bias" in name:
   __lowerCAmelCase         =							name.replace(F".global_rep.{i}.bias"			,							".layernorm.bias"  )

 if ".global_rep." in name:
  __lowerCAmelCase         =							name.replace(".global_rep."			,							".transformer."  )
 if ".pre_norm_mha.0." in name:
  __lowerCAmelCase         =							name.replace(".pre_norm_mha.0."			,							".layernorm_before."  )
 if ".pre_norm_mha.1.out_proj." in name:
  __lowerCAmelCase         =							name.replace(".pre_norm_mha.1.out_proj."			,							".attention.output.dense."  )
 if ".pre_norm_ffn.0." in name:
  __lowerCAmelCase         =							name.replace(".pre_norm_ffn.0."			,							".layernorm_after."  )
 if ".pre_norm_ffn.1." in name:
  __lowerCAmelCase         =							name.replace(".pre_norm_ffn.1."			,							".intermediate.dense."  )
 if ".pre_norm_ffn.4." in name:
  __lowerCAmelCase         =							name.replace(".pre_norm_ffn.4."			,							".output.dense."  )
 if ".transformer." in name:
  __lowerCAmelCase         =							name.replace(".transformer."			,							".transformer.layer."  )

 if ".aspp_layer." in name:
  __lowerCAmelCase         =							name.replace(".aspp_layer."			,							"."  )
 if ".aspp_pool." in name:
  __lowerCAmelCase         =							name.replace(".aspp_pool."			,							"."  )
 if "seg_head." in name:
  __lowerCAmelCase         =							name.replace("seg_head."			,							"segmentation_head."  )
 if "segmentation_head.classifier.classifier." in name:
  __lowerCAmelCase         =							name.replace("segmentation_head.classifier.classifier."			,							"segmentation_head.classifier."  )

 if "classifier.fc." in name:
  __lowerCAmelCase         =							name.replace("classifier.fc."			,							"classifier."  )
 elif (not base_model) and ("segmentation_head." not in name):
  __lowerCAmelCase         =							"mobilevit." + name

 return name






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Any			,							UpperCamelCase:     Dict=False  ):






 """simple docstring"""


 if base_model:
  __lowerCAmelCase         =							""
 else:
  __lowerCAmelCase         =							"mobilevit."

 for key in orig_state_dict.copy().keys():
  __lowerCAmelCase         =							orig_state_dict.pop(UpperCamelCase  )

  if key[:8] == "encoder.":
   __lowerCAmelCase         =							key[8:]

  if "qkv" in key:
   __lowerCAmelCase         =							key.split("."  )
   __lowerCAmelCase         =							int(key_split[0][6:]  ) - 1
   __lowerCAmelCase         =							int(key_split[3]  )
   __lowerCAmelCase         =							model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}"  )
   __lowerCAmelCase         =							layer.transformer.layer[transformer_num].attention.attention.all_head_size
   __lowerCAmelCase         =							(
       F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
   )
   if "weight" in key:
    __lowerCAmelCase         =							val[:dim, :]
    __lowerCAmelCase         =							val[dim : dim * 2, :]
    __lowerCAmelCase         =							val[-dim:, :]
   else:
    __lowerCAmelCase         =							val[:dim]
    __lowerCAmelCase         =							val[dim : dim * 2]
    __lowerCAmelCase         =							val[-dim:]
  else:
   __lowerCAmelCase         =							val

 return orig_state_dict






def 		_UpperCAmelCase   (	):






 """simple docstring"""


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






@torch.no_grad()
def 		_UpperCAmelCase   (	UpperCamelCase:     int			,							UpperCamelCase:     Dict			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Any=False  ):






 """simple docstring"""


 __lowerCAmelCase         =							get_mobilevit_config(UpperCamelCase  )

 # load original state_dict
 __lowerCAmelCase         =							torch.load(UpperCamelCase			,							map_location="cpu"  )

 # load 🤗 model
 if mobilevit_name.startswith("deeplabv3_"  ):
  __lowerCAmelCase         =							MobileViTForSemanticSegmentation(UpperCamelCase  ).eval()
 else:
  __lowerCAmelCase         =							MobileViTForImageClassification(UpperCamelCase  ).eval()

 __lowerCAmelCase         =							convert_state_dict(UpperCamelCase			,							UpperCamelCase  )
 model.load_state_dict(UpperCamelCase  )

 # Check outputs on an image, prepared by MobileViTImageProcessor
 __lowerCAmelCase         =							MobileViTImageProcessor(crop_size=config.image_size			,							size=config.image_size + 3_2  )
 __lowerCAmelCase         =							image_processor(images=prepare_img()			,							return_tensors="pt"  )
 __lowerCAmelCase         =							model(**UpperCamelCase  )
 __lowerCAmelCase         =							outputs.logits

 if mobilevit_name.startswith("deeplabv3_"  ):
  assert logits.shape == (1, 2_1, 3_2, 3_2)

  if mobilevit_name == "deeplabv3_mobilevit_s":
   __lowerCAmelCase         =							torch.tensor(
       [
           [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
           [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
           [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
       ]  )
  elif mobilevit_name == "deeplabv3_mobilevit_xs":
   __lowerCAmelCase         =							torch.tensor(
       [
           [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
           [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
           [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
       ]  )
  elif mobilevit_name == "deeplabv3_mobilevit_xxs":
   __lowerCAmelCase         =							torch.tensor(
       [
           [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
           [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
           [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
       ]  )
  else:
   raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"  )

  assert torch.allclose(logits[0, :3, :3, :3]			,							UpperCamelCase			,							atol=1e-4  )
 else:
  assert logits.shape == (1, 1_0_0_0)

  if mobilevit_name == "mobilevit_s":
   __lowerCAmelCase         =							torch.tensor([-0.9_866, 0.2_392, -1.1_241]  )
  elif mobilevit_name == "mobilevit_xs":
   __lowerCAmelCase         =							torch.tensor([-2.4_761, -0.9_399, -1.9_587]  )
  elif mobilevit_name == "mobilevit_xxs":
   __lowerCAmelCase         =							torch.tensor([-1.9_364, -1.2_327, -0.4_653]  )
  else:
   raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"  )

  assert torch.allclose(logits[0, :3]			,							UpperCamelCase			,							atol=1e-4  )

 Path(UpperCamelCase  ).mkdir(exist_ok=UpperCamelCase  )
 print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"  )
 model.save_pretrained(UpperCamelCase  )
 print(F"Saving image processor to {pytorch_dump_folder_path}"  )
 image_processor.save_pretrained(UpperCamelCase  )

 if push_to_hub:
  __lowerCAmelCase         =							{
      "mobilevit_s": "mobilevit-small",
      "mobilevit_xs": "mobilevit-x-small",
      "mobilevit_xxs": "mobilevit-xx-small",
      "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
      "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
      "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
  }

  print("Pushing to the hub..."  )
  __lowerCAmelCase         =							model_mapping[mobilevit_name]
  image_processor.push_to_hub(UpperCamelCase			,							organization="apple"  )
  model.push_to_hub(UpperCamelCase			,							organization="apple"  )


if __name__ == "__main__":
     UpperCamelCase_   	=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--mobilevit_name",
         default="mobilevit_s",
         type=str,
         help=(
             "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
             " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
         ),
     )
     parser.add_argument(
         "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", required=True, 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."
     )

     UpperCamelCase_   	=	parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )








from __future__ import annotations

UpperCamelCase_   	=	list[tuple[int, int]]

UpperCamelCase_   	=	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCamelCase_   	=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class       a		:







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  float      ,				snake_case__  :  Node | None      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							pos_x
								__lowerCAmelCase         =							pos_y
								__lowerCAmelCase         =							(pos_y, pos_x)
								__lowerCAmelCase         =							goal_x
								__lowerCAmelCase         =							goal_y
								__lowerCAmelCase         =							g_cost
								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							self.calculate_heuristic()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							abs(self.pos_x - self.goal_x )
								__lowerCAmelCase         =							abs(self.pos_y - self.goal_y )
								return dx + dy







							def __lt__(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								return self.f_cost < other.f_cost

class       a		:







							def __init__(						self  :  Dict      ,				snake_case__  :  tuple[int, int]      ,				snake_case__  :  tuple[int, int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Node(start[1]      ,				start[0]      ,				goal[1]      ,				goal[0]      ,				0      ,				snake_case__ )
								__lowerCAmelCase         =							Node(goal[1]      ,				goal[0]      ,				goal[1]      ,				goal[0]      ,				99_999      ,				snake_case__ )

								__lowerCAmelCase         =							[self.start]
								__lowerCAmelCase         =							[]

								__lowerCAmelCase         =							False







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__lowerCAmelCase         =							self.open_nodes.pop(0 )

									if current_node.pos == self.target.pos:
										__lowerCAmelCase         =							True
										return self.retrace_path(snake_case__ )

									self.closed_nodes.append(snake_case__ )
									__lowerCAmelCase         =							self.get_successors(snake_case__ )

									for child_node in successors:
										if child_node in self.closed_nodes:
											continue

										if child_node not in self.open_nodes:
											self.open_nodes.append(snake_case__ )
										else:
											# retrieve the best current path
											__lowerCAmelCase         =							self.open_nodes.pop(self.open_nodes.index(snake_case__ ) )

											if child_node.g_cost < better_node.g_cost:
												self.open_nodes.append(snake_case__ )
											else:
												self.open_nodes.append(snake_case__ )

								if not self.reached:
									return [self.start.pos]
								return None







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Node ):
								"""simple docstring"""







								__lowerCAmelCase         =							[]
								for action in delta:
									__lowerCAmelCase         =							parent.pos_x + action[1]
									__lowerCAmelCase         =							parent.pos_y + action[0]

									if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case__ ) - 1):
										continue

									if grid[pos_y][pos_x] != 0:
										continue

									successors.append(
									    Node(
									        snake_case__      ,				snake_case__      ,				self.target.pos_y      ,				self.target.pos_x      ,				parent.g_cost + 1      ,				snake_case__      ,				) )
								return successors







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Node | None ):
								"""simple docstring"""







								__lowerCAmelCase         =							node
								__lowerCAmelCase         =							[]
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x) )
									__lowerCAmelCase         =							current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
					UpperCamelCase_   	=	(0, 0)
					UpperCamelCase_   	=	(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("------")

					UpperCamelCase_   	=	GreedyBestFirst(init, goal)
					UpperCamelCase_   	=	greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															UpperCamelCase_   	=	2

										for elem in grid:
															print(elem)






