'''simple docstring'''




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case   :      Dict						    =  logging.get_logger(__name__)





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any   )	->      Optional[Any]:
    A_     =   MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

    if "quant" in model_name:
        raise ValueError('''Quantized models are not supported.'''   )

    A_     =   re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$''',   _UpperCamelCase   )
    if matches:
        A_     =   float(matches[1]   )
        A_     =   int(matches[2]   )

    if model_name.startswith('''deeplabv3_'''   ):
        A_     =   8
        A_     =   21
        A_     =   '''pascal-voc-id2label.json'''
    else:
        # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
        # of the usual 1000. The first class (index 0) is "background".
        A_     =   10_01
        A_     =   '''imagenet-1k-id2label.json'''

    A_     =   '''huggingface/label-files'''
    A_     =   json.load(open(hf_hub_download(_UpperCamelCase,   _UpperCamelCase,   repo_type='''dataset'''   ),   '''r'''   )   )

    if config.num_labels == 10_01:
        A_     =   {int(_UpperCamelCase   ) + 1: v for k, v in idalabel.items()}
        A_     =   '''background'''
    else:
        A_     =   {int(_UpperCamelCase   ): v for k, v in idalabel.items()}

    A_     =   idalabel
    A_     =   {v: k for k, v in idalabel.items()}

    return config





def      _UpperCAmelCase    (							)	->      Optional[Any]:
    A_     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A_     =   Image.open(requests.get(_UpperCamelCase,   stream=_UpperCamelCase   ).raw   )
    return im





@torch.no_grad()
def      _UpperCAmelCase    (							_UpperCamelCase    :	Any,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	int,   _UpperCamelCase    :	List[Any]=False   )	->      List[Any]:
    A_     =   get_mobilenet_va_config(_UpperCamelCase   )

    # Load 🤗 model
    if model_name.startswith('''deeplabv3_'''   ):
        A_     =   MobileNetVaForSemanticSegmentation(_UpperCamelCase   ).eval()
    else:
        A_     =   MobileNetVaForImageClassification(_UpperCamelCase   ).eval()

    # Load weights from TensorFlow checkpoint
    load_tf_weights_in_mobilenet_va(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    A_     =   MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size},   size={'''shortest_edge''': config.image_size + 32},   )
    A_     =   image_processor(images=prepare_img(),   return_tensors='''pt'''   )
    A_     =   model(**_UpperCamelCase   )
    A_     =   outputs.logits

    if model_name.startswith('''deeplabv3_'''   ):
        assert logits.shape == (1, 21, 65, 65)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            A_     =   torch.tensor(
                [
                    [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                    [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                    [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                ]   )

        else:
            raise ValueError(F'''Unknown model name: {model_name}'''   )

        assert torch.allclose(logits[0, :3, :3, :3],   _UpperCamelCase,   atol=1E-4   )
    else:
        assert logits.shape == (1, 10_01)

        if model_name == "mobilenet_v2_1.4_224":
            A_     =   torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]   )
        elif model_name == "mobilenet_v2_1.0_224":
            A_     =   torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]   )
        elif model_name == "mobilenet_v2_0.75_160":
            A_     =   torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]   )
        elif model_name == "mobilenet_v2_0.35_96":
            A_     =   torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]   )
        else:
            A_     =   None

        if expected_logits is not None:
            assert torch.allclose(logits[0, :3],   _UpperCamelCase,   atol=1E-4   )

    Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
    print(F'''Saving model {model_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:
        print('''Pushing to the hub...'''   )
        A_     =   '''google/''' + model_name
        image_processor.push_to_hub(_UpperCamelCase   )
        model.push_to_hub(_UpperCamelCase   )


if __name__ == "__main__":
  __snake_case   :      str						    =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='mobilenet_v2_1.0_224',
      type=str,
      help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.',
  )
  parser.add_argument(
      '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt 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   :      List[str]						    =  parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )







'''simple docstring'''




__snake_case   :      List[Any]						    =  [chr(i) for i in range(32, 126)]
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      List[Any]						    =  list(reversed(range(len(alphabets))))
__snake_case   :      Optional[int]						    =  []
__snake_case   :      List[Any]						    =  0





def      _UpperCAmelCase    (							)	->      Any:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    A_     =   gear_one[0]
    gear_one.append(_UpperCamelCase   )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(_UpperCamelCase   )   ) == 0:
        A_     =   gear_two[0]
        gear_two.append(_UpperCamelCase   )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(_UpperCamelCase   )   ) == 0:
            A_     =   gear_three[0]
            gear_three.append(_UpperCamelCase   )
            del gear_three[0]
            gear_three_pos += 1





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      Any:
    A_     =   alphabets.index(_UpperCamelCase   )
    A_     =   gear_one[target]
    A_     =   gear_two[target]
    A_     =   gear_three[target]
    A_     =   reflector[target]
    A_     =   gear_three.index(_UpperCamelCase   )
    A_     =   gear_two.index(_UpperCamelCase   )
    A_     =   gear_one.index(_UpperCamelCase   )
    code.append(alphabets[target]   )
    rotator()


if __name__ == "__main__":
  __snake_case   :      List[str]						    =  list(input('Type your message:\n'))
  while True:
    try:
      __snake_case   :      Any						    =  int(input('Please set token:(must be only digits)\n'))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print('\n' + ''.join(code))
  print(
      F"""\nYour Token is {token} please write it down.\nIf you want to decode """
      'this message again you should input same digits as token!'
  )






