

"""simple docstring"""





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()
_a       =							logging.get_logger(__name__)




def   _A  (  UpperCamelCase_     :       List[str])	->						Optional[Any]:
   '''simple docstring'''


   __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(UpperCamelCase_,			UpperCamelCase_,			repo_type="dataset"),			"r"))
   __lowercase								=						{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase								=						idalabel
   __lowercase								=						{v: k for k, v in idalabel.items()}

   return config




def   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       Dict=False)	->						Optional[Any]:
   '''simple docstring'''


   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   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Any=False)	->						Tuple:
   '''simple docstring'''


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

   for key in orig_state_dict.copy().keys():
      __lowercase								=						orig_state_dict.pop(UpperCamelCase_)

      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   _A  (  )	->						Tuple:
   '''simple docstring'''


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




@torch.no_grad()
def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Any,			UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Any=False)	->						str:
   '''simple docstring'''


   __lowercase								=						get_mobilevit_config(UpperCamelCase_)

   # load original state_dict
   __lowercase								=						torch.load(UpperCamelCase_,			map_location="cpu")

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

   __lowercase								=						convert_state_dict(UpperCamelCase_,			UpperCamelCase_)
   model.load_state_dict(UpperCamelCase_)

   # 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(**UpperCamelCase_)
   __lowercase								=						outputs.logits

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         __lowercase								=						torch.tensor(
             [
                 [[6.2_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":
         __lowercase								=						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":
         __lowercase								=						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, 1000)

      if mobilevit_name == "mobilevit_s":
         __lowercase								=						torch.tensor([-0.9_866, 0.2_392, -1.1_241])
      elif mobilevit_name == "mobilevit_xs":
         __lowercase								=						torch.tensor([-2.4_761, -0.9_399, -1.9_587])
      elif mobilevit_name == "mobilevit_xxs":
         __lowercase								=						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:
      __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(UpperCamelCase_,			organization="apple")
      model.push_to_hub(UpperCamelCase_,			organization="apple")


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

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







"""simple docstring"""





def   _A  (  UpperCamelCase_     :       Any)	->						List[str]:
   '''simple docstring'''


   __lowercase		,__lowercase								=						[], []
   while len(UpperCamelCase_) > 1:
      __lowercase		,__lowercase								=						min(UpperCamelCase_), max(UpperCamelCase_)
      start.append(UpperCamelCase_)
      end.append(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
   end.reverse()
   return start + collection + end


if __name__ == "__main__":
   _a       =							input('Enter numbers separated by a comma:\n').strip()
   _a       =							[int(item) for item in user_input.split(',')]
   print(*merge_sort(unsorted), sep=',')




