






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()
lowerCamelCase     :							List[Any]    =logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[Any]:
     UpperCamelCase__      :       Union[str, Any]	    =  MobileViTConfig()

     # size of the architecture
     if "mobilevit_s" in mobilevit_name:
          UpperCamelCase__      :       Optional[Any]	    =  [144, 192, 240]
          UpperCamelCase__      :       str	    =  [16, 32, 64, 96, 128, 160, 640]
     elif "mobilevit_xs" in mobilevit_name:
          UpperCamelCase__      :       str	    =  [96, 120, 144]
          UpperCamelCase__      :       Any	    =  [16, 32, 48, 64, 80, 96, 384]
     elif "mobilevit_xxs" in mobilevit_name:
          UpperCamelCase__      :       Tuple	    =  [64, 80, 96]
          UpperCamelCase__      :       List[str]	    =  [16, 16, 24, 48, 64, 80, 320]
          UpperCamelCase__      :       Optional[Any]	    =  0.0_5
          UpperCamelCase__      :       List[Any]	    =  2.0

     if mobilevit_name.startswith("deeplabv3_"      ):
          UpperCamelCase__      :       str	    =  512
          UpperCamelCase__      :       Optional[Any]	    =  16
          UpperCamelCase__      :       Any	    =  21
          UpperCamelCase__      :       List[str]	    =  "pascal-voc-id2label.json"
     else:
          UpperCamelCase__      :       Any	    =  1000
          UpperCamelCase__      :       Optional[int]	    =  "imagenet-1k-id2label.json"

     UpperCamelCase__      :       str	    =  "huggingface/label-files"
     UpperCamelCase__      :       Union[str, Any]	    =  json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   repo_type="dataset"      ) ,   "r"      )      )
     UpperCamelCase__      :       Optional[Any]	    =  {int(SCREAMING_SNAKE_CASE_      ): v for k, v in idalabel.items()}
     UpperCamelCase__      :       Dict	    =  idalabel
     UpperCamelCase__      :       Dict	    =  {v: k for k, v in idalabel.items()}

     return config







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=False      )			->     Union[str, Any]:
     for i in range(1 ,   6      ):
          if f'layer_{i}.' in name:
               UpperCamelCase__      :       List[Any]	    =  name.replace(f'layer_{i}.' ,   f'encoder.layer.{i - 1}.'      )

     if "conv_1." in name:
          UpperCamelCase__      :       Optional[Any]	    =  name.replace("conv_1." ,   "conv_stem."      )
     if ".block." in name:
          UpperCamelCase__      :       str	    =  name.replace(".block." ,   "."      )

     if "exp_1x1" in name:
          UpperCamelCase__      :       Optional[int]	    =  name.replace("exp_1x1" ,   "expand_1x1"      )
     if "red_1x1" in name:
          UpperCamelCase__      :       Union[str, Any]	    =  name.replace("red_1x1" ,   "reduce_1x1"      )
     if ".local_rep.conv_3x3." in name:
          UpperCamelCase__      :       Tuple	    =  name.replace(".local_rep.conv_3x3." ,   ".conv_kxk."      )
     if ".local_rep.conv_1x1." in name:
          UpperCamelCase__      :       Tuple	    =  name.replace(".local_rep.conv_1x1." ,   ".conv_1x1."      )
     if ".norm." in name:
          UpperCamelCase__      :       List[str]	    =  name.replace(".norm." ,   ".normalization."      )
     if ".conv." in name:
          UpperCamelCase__      :       Any	    =  name.replace(".conv." ,   ".convolution."      )
     if ".conv_proj." in name:
          UpperCamelCase__      :       Dict	    =  name.replace(".conv_proj." ,   ".conv_projection."      )

     for i in range(0 ,   2      ):
          for j in range(0 ,   4      ):
               if f'.{i}.{j}.' in name:
                    UpperCamelCase__      :       List[str]	    =  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:
                    UpperCamelCase__      :       List[Any]	    =  name.replace(f'.{i}.{j}.' ,   f'.{i}.'      )
                    if "expand_1x1" in name:
                         UpperCamelCase__      :       int	    =  name.replace("expand_1x1" ,   "downsampling_layer.expand_1x1"      )
                    if "conv_3x3" in name:
                         UpperCamelCase__      :       Union[str, Any]	    =  name.replace("conv_3x3" ,   "downsampling_layer.conv_3x3"      )
                    if "reduce_1x1" in name:
                         UpperCamelCase__      :       List[str]	    =  name.replace("reduce_1x1" ,   "downsampling_layer.reduce_1x1"      )

     for i in range(2 ,   5      ):
          if f'.global_rep.{i}.weight' in name:
               UpperCamelCase__      :       str	    =  name.replace(f'.global_rep.{i}.weight' ,   ".layernorm.weight"      )
          if f'.global_rep.{i}.bias' in name:
               UpperCamelCase__      :       List[Any]	    =  name.replace(f'.global_rep.{i}.bias' ,   ".layernorm.bias"      )

     if ".global_rep." in name:
          UpperCamelCase__      :       Optional[int]	    =  name.replace(".global_rep." ,   ".transformer."      )
     if ".pre_norm_mha.0." in name:
          UpperCamelCase__      :       Optional[int]	    =  name.replace(".pre_norm_mha.0." ,   ".layernorm_before."      )
     if ".pre_norm_mha.1.out_proj." in name:
          UpperCamelCase__      :       List[str]	    =  name.replace(".pre_norm_mha.1.out_proj." ,   ".attention.output.dense."      )
     if ".pre_norm_ffn.0." in name:
          UpperCamelCase__      :       Tuple	    =  name.replace(".pre_norm_ffn.0." ,   ".layernorm_after."      )
     if ".pre_norm_ffn.1." in name:
          UpperCamelCase__      :       Optional[Any]	    =  name.replace(".pre_norm_ffn.1." ,   ".intermediate.dense."      )
     if ".pre_norm_ffn.4." in name:
          UpperCamelCase__      :       List[Any]	    =  name.replace(".pre_norm_ffn.4." ,   ".output.dense."      )
     if ".transformer." in name:
          UpperCamelCase__      :       List[Any]	    =  name.replace(".transformer." ,   ".transformer.layer."      )

     if ".aspp_layer." in name:
          UpperCamelCase__      :       Dict	    =  name.replace(".aspp_layer." ,   "."      )
     if ".aspp_pool." in name:
          UpperCamelCase__      :       Union[str, Any]	    =  name.replace(".aspp_pool." ,   "."      )
     if "seg_head." in name:
          UpperCamelCase__      :       Optional[Any]	    =  name.replace("seg_head." ,   "segmentation_head."      )
     if "segmentation_head.classifier.classifier." in name:
          UpperCamelCase__      :       List[Any]	    =  name.replace("segmentation_head.classifier.classifier." ,   "segmentation_head.classifier."      )

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

     return name







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=False      )			->     Any:
     if base_model:
          UpperCamelCase__      :       Tuple	    =  ""
     else:
          UpperCamelCase__      :       str	    =  "mobilevit."

     for key in orig_state_dict.copy().keys():
          UpperCamelCase__      :       Optional[Any]	    =  orig_state_dict.pop(SCREAMING_SNAKE_CASE_      )

          if key[:8] == "encoder.":
               UpperCamelCase__      :       int	    =  key[8:]

          if "qkv" in key:
               UpperCamelCase__      :       Optional[int]	    =  key.split("."      )
               UpperCamelCase__      :       Union[str, Any]	    =  int(key_split[0][6:]      ) - 1
               UpperCamelCase__      :       Any	    =  int(key_split[3]      )
               UpperCamelCase__      :       Any	    =  model.get_submodule(f'{model_prefix}encoder.layer.{layer_num}'      )
               UpperCamelCase__      :       Tuple	    =  layer.transformer.layer[transformer_num].attention.attention.all_head_size
               UpperCamelCase__      :       Dict	    =  (
                   f'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
               )
               if "weight" in key:
                    UpperCamelCase__      :       Optional[int]	    =  val[:dim, :]
                    UpperCamelCase__      :       List[str]	    =  val[dim : dim * 2, :]
                    UpperCamelCase__      :       int	    =  val[-dim:, :]
               else:
                    UpperCamelCase__      :       Dict	    =  val[:dim]
                    UpperCamelCase__      :       Optional[int]	    =  val[dim : dim * 2]
                    UpperCamelCase__      :       Optional[Any]	    =  val[-dim:]
          else:
               UpperCamelCase__      :       Optional[Any]	    =  val

     return orig_state_dict







def 		SCREAMING_SNAKE_CASE							( )			->     Optional[Any]:
     UpperCamelCase__      :       Union[str, Any]	    =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase__      :       Dict	    =  Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,   stream=SCREAMING_SNAKE_CASE_      ).raw      )
     return im







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=False      )			->     List[str]:
     UpperCamelCase__      :       Dict	    =  get_mobilevit_config(SCREAMING_SNAKE_CASE_      )

     # load original state_dict
     UpperCamelCase__      :       List[str]	    =  torch.load(SCREAMING_SNAKE_CASE_ ,   map_location="cpu"      )

     # load 🤗 model
     if mobilevit_name.startswith("deeplabv3_"      ):
          UpperCamelCase__      :       List[str]	    =  MobileViTForSemanticSegmentation(SCREAMING_SNAKE_CASE_      ).eval()
     else:
          UpperCamelCase__      :       int	    =  MobileViTForImageClassification(SCREAMING_SNAKE_CASE_      ).eval()

     UpperCamelCase__      :       Dict	    =  convert_state_dict(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_      )
     model.load_state_dict(SCREAMING_SNAKE_CASE_      )

     # Check outputs on an image, prepared by MobileViTImageProcessor
     UpperCamelCase__      :       Optional[Any]	    =  MobileViTImageProcessor(crop_size=config.image_size ,   size=config.image_size + 32      )
     UpperCamelCase__      :       int	    =  image_processor(images=prepare_img() ,   return_tensors="pt"      )
     UpperCamelCase__      :       str	    =  model(**SCREAMING_SNAKE_CASE_      )
     UpperCamelCase__      :       Union[str, Any]	    =  outputs.logits

     if mobilevit_name.startswith("deeplabv3_"      ):
          assert logits.shape == (1, 21, 32, 32)

          if mobilevit_name == "deeplabv3_mobilevit_s":
               UpperCamelCase__      :       str	    =  torch.tensor(
                   [
                       [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                       [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                       [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                   ]      )
          elif mobilevit_name == "deeplabv3_mobilevit_xs":
               UpperCamelCase__      :       List[str]	    =  torch.tensor(
                   [
                       [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                       [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                       [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                   ]      )
          elif mobilevit_name == "deeplabv3_mobilevit_xxs":
               UpperCamelCase__      :       str	    =  torch.tensor(
                   [
                       [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                       [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                       [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                   ]      )
          else:
               raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}'      )

          assert torch.allclose(logits[0, :3, :3, :3] ,   SCREAMING_SNAKE_CASE_ ,   atol=1e-4      )
     else:
          assert logits.shape == (1, 1000)

          if mobilevit_name == "mobilevit_s":
               UpperCamelCase__      :       Optional[int]	    =  torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]      )
          elif mobilevit_name == "mobilevit_xs":
               UpperCamelCase__      :       List[Any]	    =  torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]      )
          elif mobilevit_name == "mobilevit_xxs":
               UpperCamelCase__      :       List[str]	    =  torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]      )
          else:
               raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}'      )

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

     Path(SCREAMING_SNAKE_CASE_      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
     print(f'Saving model {mobilevit_name} to {pytorch_dump_folder_path}'      )
     model.save_pretrained(SCREAMING_SNAKE_CASE_      )
     print(f'Saving image processor to {pytorch_dump_folder_path}'      )
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE_      )

     if push_to_hub:
          UpperCamelCase__      :       Tuple	    =  {
              "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..."      )
          UpperCamelCase__      :       Tuple	    =  model_mapping[mobilevit_name]
          image_processor.push_to_hub(SCREAMING_SNAKE_CASE_ ,   organization="apple"      )
          model.push_to_hub(SCREAMING_SNAKE_CASE_ ,   organization="apple"      )


if __name__ == "__main__":
 lowerCamelCase     :							List[str]    =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.'''
 )

 lowerCamelCase     :							Optional[int]    =parser.parse_args()
 convert_movilevit_checkpoint(
     args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )







lowerCamelCase     :							Optional[int]    ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       Optional[Any]	    =  [[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

    # in case there's no path between the 2 nodes
     return []







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     UpperCamelCase__      :       Tuple	    =  [start]
     UpperCamelCase__      :       Optional[int]	    =  set(__lowerCAmelCase      )
     # Keep tab on distances from `start` node.
     UpperCamelCase__      :       str	    =  {start: 0, target: -1}
     while queue:
          UpperCamelCase__      :       Any	    =  queue.pop(0      )
          if node == target:
               UpperCamelCase__      :       Union[str, Any]	    =  (
                   dist[node] if dist[target] == -1 else min(dist[target] ,   dist[node]      )
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4