





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()
snake_case								=							logging.get_logger(__name__)

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict  )   ->    Optional[int]:
  _lowercase						    =			MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    _lowercase						    =			[144, 192, 240]
    _lowercase						    =			[16, 32, 64, 96, 128, 160, 640]
  elif "mobilevit_xs" in mobilevit_name:
    _lowercase						    =			[96, 120, 144]
    _lowercase						    =			[16, 32, 48, 64, 80, 96, 384]
  elif "mobilevit_xxs" in mobilevit_name:
    _lowercase						    =			[64, 80, 96]
    _lowercase						    =			[16, 16, 24, 48, 64, 80, 320]
    _lowercase						    =			0.05
    _lowercase						    =			2.0

  if mobilevit_name.startswith('deeplabv3_'  ):
    _lowercase						    =			512
    _lowercase						    =			16
    _lowercase						    =			21
    _lowercase						    =			'pascal-voc-id2label.json'
  else:
    _lowercase						    =			1000
    _lowercase						    =			'imagenet-1k-id2label.json'

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

  return config

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Dict=False  )   ->    List[str]:
  for i in range(1      ,				6  ):
    if F"""layer_{i}.""" in name:
      _lowercase						    =			name.replace(F"""layer_{i}."""      ,				F"""encoder.layer.{i - 1}."""  )

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

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

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

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

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

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

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

  return name

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Dict      ,				snake_case__	:Tuple=False  )   ->    Optional[int]:
  if base_model:
    _lowercase						    =			''
  else:
    _lowercase						    =			'mobilevit.'

  for key in orig_state_dict.copy().keys():
    _lowercase						    =			orig_state_dict.pop(snake_case__  )

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

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

  return orig_state_dict

def 				SCREAMING_SNAKE_CASE__							(	)   ->    Tuple:
  _lowercase						    =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase						    =			Image.open(requests.get(snake_case__      ,				stream=snake_case__  ).raw  )
  return im

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Tuple      ,				snake_case__	:List[str]      ,				snake_case__	:Tuple=False  )   ->    Optional[Any]:
  _lowercase						    =			get_mobilevit_config(snake_case__  )

  # load original state_dict
  _lowercase						    =			torch.load(snake_case__      ,				map_location='cpu'  )

  # load 🤗 model
  if mobilevit_name.startswith('deeplabv3_'  ):
    _lowercase						    =			MobileViTForSemanticSegmentation(snake_case__  ).eval()
  else:
    _lowercase						    =			MobileViTForImageClassification(snake_case__  ).eval()

  _lowercase						    =			convert_state_dict(snake_case__      ,				snake_case__  )
  model.load_state_dict(snake_case__  )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  _lowercase						    =			MobileViTImageProcessor(crop_size=config.image_size      ,				size=config.image_size + 32  )
  _lowercase						    =			image_processor(images=prepare_img()      ,				return_tensors='pt'  )
  _lowercase						    =			model(**snake_case__  )
  _lowercase						    =			outputs.logits

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

    if mobilevit_name == "deeplabv3_mobilevit_s":
      _lowercase						    =			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":
      _lowercase						    =			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":
      _lowercase						    =			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]      ,				snake_case__      ,				atol=1E-4  )
  else:
    assert logits.shape == (1, 1000)

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

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

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

  if push_to_hub:
    _lowercase						    =			{
        '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...'  )
    _lowercase						    =			model_mapping[mobilevit_name]
    image_processor.push_to_hub(snake_case__      ,				organization='apple'  )
    model.push_to_hub(snake_case__      ,				organization='apple'  )


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

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




'''simple docstring'''
from math import ceil






def A__       (	UpperCAmelCase_ = 1_0_0_1				):
						_UpperCamelCase   :						int												=  1

						for i in range(1    ,    int(ceil(n / 2.0				)				)				):
												_UpperCamelCase   :						Dict												=  2 * i + 1
												_UpperCamelCase   :						Tuple												=  2 * i
												_UpperCamelCase   :						Any												=  total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															snake_case_					:      Tuple    			=    int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')


