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()
__magic_name__    							=							logging.get_logger(__name__)






def 							UpperCAmelCase__(					__UpperCAmelCase			:     int       ):
   __snake_case   :			List[Any]										=						MobileViTConfig()

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

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

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

   return config






def 							UpperCAmelCase__(					__UpperCAmelCase			:     Optional[Any]     ,				__UpperCAmelCase			:     Union[str, Any]=False       ):



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

   if "conv_1." in name:
      __snake_case   :			List[str]										=						name.replace('conv_1.'     ,				'conv_stem.'       )
   if ".block." in name:
      __snake_case   :			int										=						name.replace('.block.'     ,				'.'       )

   if "exp_1x1" in name:
      __snake_case   :			Optional[int]										=						name.replace('exp_1x1'     ,				'expand_1x1'       )
   if "red_1x1" in name:
      __snake_case   :			Any										=						name.replace('red_1x1'     ,				'reduce_1x1'       )
   if ".local_rep.conv_3x3." in name:
      __snake_case   :			str										=						name.replace('.local_rep.conv_3x3.'     ,				'.conv_kxk.'       )
   if ".local_rep.conv_1x1." in name:
      __snake_case   :			Union[str, Any]										=						name.replace('.local_rep.conv_1x1.'     ,				'.conv_1x1.'       )
   if ".norm." in name:
      __snake_case   :			Dict										=						name.replace('.norm.'     ,				'.normalization.'       )
   if ".conv." in name:
      __snake_case   :			List[Any]										=						name.replace('.conv.'     ,				'.convolution.'       )
   if ".conv_proj." in name:
      __snake_case   :			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:
            __snake_case   :			Any										=						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:
            __snake_case   :			Dict										=						name.replace(F""".{i}.{j}."""     ,				F""".{i}."""       )
            if "expand_1x1" in name:
               __snake_case   :			str										=						name.replace('expand_1x1'     ,				'downsampling_layer.expand_1x1'       )
            if "conv_3x3" in name:
               __snake_case   :			Dict										=						name.replace('conv_3x3'     ,				'downsampling_layer.conv_3x3'       )
            if "reduce_1x1" in name:
               __snake_case   :			Any										=						name.replace('reduce_1x1'     ,				'downsampling_layer.reduce_1x1'       )

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

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

   if ".aspp_layer." in name:
      __snake_case   :			Optional[int]										=						name.replace('.aspp_layer.'     ,				'.'       )
   if ".aspp_pool." in name:
      __snake_case   :			List[Any]										=						name.replace('.aspp_pool.'     ,				'.'       )
   if "seg_head." in name:
      __snake_case   :			int										=						name.replace('seg_head.'     ,				'segmentation_head.'       )
   if "segmentation_head.classifier.classifier." in name:
      __snake_case   :			Any										=						name.replace('segmentation_head.classifier.classifier.'     ,				'segmentation_head.classifier.'       )

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

   return name






def 							UpperCAmelCase__(					__UpperCAmelCase			:     Dict     ,				__UpperCAmelCase			:     Optional[Any]     ,				__UpperCAmelCase			:     Optional[int]=False       ):



   if base_model:
      __snake_case   :			List[str]										=						''
   else:
      __snake_case   :			Dict										=						'mobilevit.'

   for key in orig_state_dict.copy().keys():
      __snake_case   :			Tuple										=						orig_state_dict.pop(UpperCAmelCase__       )

      if key[:8] == "encoder.":
         __snake_case   :			Dict										=						key[8:]

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

   return orig_state_dict






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






@torch.no_grad()
def 							UpperCAmelCase__(					__UpperCAmelCase			:     Union[str, Any]     ,				__UpperCAmelCase			:     Dict     ,				__UpperCAmelCase			:     Tuple     ,				__UpperCAmelCase			:     Union[str, Any]=False       ):
   __snake_case   :			Optional[int]										=						get_mobilevit_config(UpperCAmelCase__       )

   # load original state_dict
   __snake_case   :			Tuple										=						torch.load(UpperCAmelCase__     ,				map_location='cpu'       )

   # load 🤗 model
   if mobilevit_name.startswith('deeplabv3_'       ):
      __snake_case   :			int										=						MobileViTForSemanticSegmentation(UpperCAmelCase__       ).eval()
   else:
      __snake_case   :			Dict										=						MobileViTForImageClassification(UpperCAmelCase__       ).eval()

   __snake_case   :			Optional[int]										=						convert_state_dict(UpperCAmelCase__     ,				UpperCAmelCase__       )
   model.load_state_dict(UpperCAmelCase__       )

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

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         __snake_case   :			Union[str, Any]										=						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":
         __snake_case   :			Union[str, Any]										=						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":
         __snake_case   :			int										=						torch.tensor(
             [
                 [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                 [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.8624, -9.5964], [-10.88_40, -10.81_58, -10.66_59]],
                 [[-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]     ,				UpperCAmelCase__     ,				atol=1E-4       )
   else:
      assert logits.shape == (1, 10_00)

      if mobilevit_name == "mobilevit_s":
         __snake_case   :			Any										=						torch.tensor([-0.9866, 0.2392, -1.1241]       )
      elif mobilevit_name == "mobilevit_xs":
         __snake_case   :			str										=						torch.tensor([-2.4761, -0.9399, -1.9587]       )
      elif mobilevit_name == "mobilevit_xxs":
         __snake_case   :			Union[str, Any]										=						torch.tensor([-1.9364, -1.2327, -0.4653]       )
      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:
      __snake_case   :			Optional[Any]										=						{
          '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...'       )
      __snake_case   :			int										=						model_mapping[mobilevit_name]
      image_processor.push_to_hub(UpperCAmelCase__     ,				organization='apple'       )
      model.push_to_hub(UpperCAmelCase__     ,				organization='apple'       )


if __name__ == "__main__":
 __magic_name__    							=							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.'''
 )

 __magic_name__    							=							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

from math import pow, sqrt

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if (resistance, reactance, impedance).count(0			) != 1:
            raise ValueError("One and only one argument must be 0"			)
      if resistance == 0:
            return {"resistance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif reactance == 0:
            return {"reactance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif impedance == 0:
            return {"impedance": sqrt(pow(UpperCAmelCase__ , 2			) + pow(UpperCAmelCase__ , 2			)			)}
      else:
            raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()