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()
SCREAMING_SNAKE_CASE__		:					int						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[Any]			=		MobileViTConfig()

     # size of the architecture
     if "mobilevit_s" in mobilevit_name:
          SCREAMING_SNAKE_CASE__				:				str			=		[1_44, 1_92, 2_40]
          SCREAMING_SNAKE_CASE__				:				int			=		[16, 32, 64, 96, 1_28, 1_60, 6_40]
     elif "mobilevit_xs" in mobilevit_name:
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[96, 1_20, 1_44]
          SCREAMING_SNAKE_CASE__				:				Tuple			=		[16, 32, 48, 64, 80, 96, 3_84]
     elif "mobilevit_xxs" in mobilevit_name:
          SCREAMING_SNAKE_CASE__				:				int			=		[64, 80, 96]
          SCREAMING_SNAKE_CASE__				:				int			=		[16, 16, 24, 48, 64, 80, 3_20]
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		0.05
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		2.0

     if mobilevit_name.startswith('deeplabv3_' ):
          SCREAMING_SNAKE_CASE__				:				List[str]			=		5_12
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		16
          SCREAMING_SNAKE_CASE__				:				Dict			=		21
          SCREAMING_SNAKE_CASE__				:				Any			=		'pascal-voc-id2label.json'
     else:
          SCREAMING_SNAKE_CASE__				:				str			=		10_00
          SCREAMING_SNAKE_CASE__				:				Dict			=		'imagenet-1k-id2label.json'

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

     return config







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Dict=False ):







     '''simple docstring'''


     for i in range(1     ,  6 ):
          if f'''layer_{i}.''' in name:
               SCREAMING_SNAKE_CASE__				:				int			=		name.replace(f'''layer_{i}.'''     ,  f'''encoder.layer.{i - 1}.''' )

     if "conv_1." in name:
          SCREAMING_SNAKE_CASE__				:				str			=		name.replace('conv_1.'     ,  'conv_stem.' )
     if ".block." in name:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		name.replace('.block.'     ,  '.' )

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

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

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

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

     if ".aspp_layer." in name:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		name.replace('.aspp_layer.'     ,  '.' )
     if ".aspp_pool." in name:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		name.replace('.aspp_pool.'     ,  '.' )
     if "seg_head." in name:
          SCREAMING_SNAKE_CASE__				:				Any			=		name.replace('seg_head.'     ,  'segmentation_head.' )
     if "segmentation_head.classifier.classifier." in name:
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		name.replace('segmentation_head.classifier.classifier.'     ,  'segmentation_head.classifier.' )

     if "classifier.fc." in name:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		name.replace('classifier.fc.'     ,  'classifier.' )
     elif (not base_model) and ("segmentation_head." not in name):
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'mobilevit.' + name

     return name







def     _a				(      lowercase__  :			int     ,  lowercase__  :			Any     ,  lowercase__  :			Any=False ):







     '''simple docstring'''


     if base_model:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     else:
          SCREAMING_SNAKE_CASE__				:				Dict			=		'mobilevit.'

     for key in orig_state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__				:				Any			=		orig_state_dict.pop(lowercase__ )

          if key[:8] == "encoder.":
               SCREAMING_SNAKE_CASE__				:				Tuple			=		key[8:]

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

     return orig_state_dict







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE__				:				List[str]			=		Image.open(requests.get(lowercase__     ,  stream=lowercase__ ).raw )
     return im







@torch.no_grad()
def     _a				(      lowercase__  :			Any     ,  lowercase__  :			List[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Union[str, Any]=False ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		get_mobilevit_config(lowercase__ )

     # load original state_dict
     SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.load(lowercase__     ,  map_location='cpu' )

     # load 🤗 model
     if mobilevit_name.startswith('deeplabv3_' ):
          SCREAMING_SNAKE_CASE__				:				Dict			=		MobileViTForSemanticSegmentation(lowercase__ ).eval()
     else:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		MobileViTForImageClassification(lowercase__ ).eval()

     SCREAMING_SNAKE_CASE__				:				Any			=		convert_state_dict(lowercase__     ,  lowercase__ )
     model.load_state_dict(lowercase__ )

     # Check outputs on an image, prepared by MobileViTImageProcessor
     SCREAMING_SNAKE_CASE__				:				Tuple			=		MobileViTImageProcessor(crop_size=config.image_size     ,  size=config.image_size + 32 )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(images=prepare_img()     ,  return_tensors='pt' )
     SCREAMING_SNAKE_CASE__				:				Dict			=		model(**lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		outputs.logits

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

          if mobilevit_name == "deeplabv3_mobilevit_s":
               SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor(
                   [
                       [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                       [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                       [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                   ] )
          elif mobilevit_name == "deeplabv3_mobilevit_xs":
               SCREAMING_SNAKE_CASE__				:				Dict			=		torch.tensor(
                   [
                       [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                       [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                       [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                   ] )
          elif mobilevit_name == "deeplabv3_mobilevit_xxs":
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor(
                   [
                       [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                       [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                       [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                   ] )
          else:
               raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )

          assert torch.allclose(logits[0, :3, :3, :3]     ,  lowercase__     ,  atol=1E-4 )
     else:
          assert logits.shape == (1, 10_00)

          if mobilevit_name == "mobilevit_s":
               SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([-0.9866, 0.2392, -1.1241] )
          elif mobilevit_name == "mobilevit_xs":
               SCREAMING_SNAKE_CASE__				:				str			=		torch.tensor([-2.4761, -0.9399, -1.9587] )
          elif mobilevit_name == "mobilevit_xxs":
               SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([-1.9364, -1.2327, -0.4653] )
          else:
               raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )

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

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

     if push_to_hub:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{
              '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...' )
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model_mapping[mobilevit_name]
          image_processor.push_to_hub(lowercase__     ,  organization='apple' )
          model.push_to_hub(lowercase__     ,  organization='apple' )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					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."
      )

      SCREAMING_SNAKE_CASE__		:					int						=					parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )




import heapq as hq
import math
from collections.abc import Iterator

class       snake_case					:




  def __init__(      self     :	str     , a_     :	str				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		str(id_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		None
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
       SCREAMING_SNAKE_CASE__				:				Any			=		[]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}  # {vertex:distance}




  def __lt__(      self     :	int     , a_     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       return self.key < other.key




  def __repr__(      self     :	Any				)->       Dict:




       """simple docstring"""
       return self.id




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	int				)->       List[str]:




       """simple docstring"""
       self.neighbors.append(a_				)




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		weight







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Tuple     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     graph[a - 1].add_neighbor(graph[b - 1] )
     graph[b - 1].add_neighbor(graph[a - 1] )
     # add the edges:
     graph[a - 1].add_edge(graph[b - 1]     ,  lowercase__ )
     graph[b - 1].add_edge(graph[a - 1]     ,  lowercase__ )







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     for u in graph:
          SCREAMING_SNAKE_CASE__				:				Dict			=		math.inf
          SCREAMING_SNAKE_CASE__				:				str			=		None
     SCREAMING_SNAKE_CASE__				:				List[str]			=		0
     SCREAMING_SNAKE_CASE__				:				int			=		graph[:]
     while q:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		min(lowercase__ )
          q.remove(lowercase__ )
          for v in u.neighbors:
               if (v in q) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				int			=		u
                    SCREAMING_SNAKE_CASE__				:				Any			=		u.edges[v.id]
     for i in range(1     ,  len(lowercase__ ) ):
          a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
     return a







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     for u in graph:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		math.inf
          SCREAMING_SNAKE_CASE__				:				int			=		None
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0

     SCREAMING_SNAKE_CASE__				:				Tuple			=		list(lowercase__ )
     hq.heapify(lowercase__ )

     while h:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hq.heappop(lowercase__ )
          for v in u.neighbors:
               if (v in h) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				List[str]			=		u
                    SCREAMING_SNAKE_CASE__				:				Dict			=		u.edges[v.id]
                    hq.heapify(lowercase__ )

     for i in range(1     ,  len(lowercase__ ) ):
          yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def     _a				(      ):







     '''simple docstring'''




if __name__ == "__main__":
      import doctest

      doctest.testmod()



