
'''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()
SCREAMING_SNAKE_CASE    =					logging.get_logger(__name__)







def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				Dict												=       MobileNetVaConfig(layer_norm_eps=0.001		)

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

  UpperCAmelCase__			:				List[str]												=       re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"      ,					lowercase__		)
  if matches:
    UpperCAmelCase__			:				Optional[int]												=       float(matches[1]		)
    UpperCAmelCase__			:				List[Any]												=       int(matches[2]		)

  if model_name.startswith("deeplabv3_"		):
    UpperCAmelCase__			:				int												=       8
    UpperCAmelCase__			:				List[Any]												=       2_1
    UpperCAmelCase__			:				Dict												=       "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".
    UpperCAmelCase__			:				int												=       1_0_0_1
    UpperCAmelCase__			:				str												=       "imagenet-1k-id2label.json"

  UpperCAmelCase__			:				Optional[Any]												=       "huggingface/label-files"
  UpperCAmelCase__			:				List[Any]												=       json.load(open(hf_hub_download(lowercase__      ,					lowercase__      ,					repo_type="dataset"		)      ,					"r"		)		)

  if config.num_labels == 1_0_0_1:
    UpperCAmelCase__			:				int												=       {int(lowercase__		) + 1: v for k, v in idalabel.items()}
    UpperCAmelCase__			:				List[str]												=       "background"
  else:
    UpperCAmelCase__			:				Optional[int]												=       {int(lowercase__		): v for k, v in idalabel.items()}

  UpperCAmelCase__			:				Tuple												=       idalabel
  UpperCAmelCase__			:				Optional[int]												=       {v: k for k, v in idalabel.items()}

  return config







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







@torch.no_grad()
def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__=False		):
  UpperCAmelCase__			:				int												=       get_mobilenet_va_config(lowercase__		)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"		):
    UpperCAmelCase__			:				List[str]												=       MobileNetVaForSemanticSegmentation(lowercase__		).eval()
  else:
    UpperCAmelCase__			:				Optional[int]												=       MobileNetVaForImageClassification(lowercase__		).eval()

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

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  UpperCAmelCase__			:				str												=       MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size}      ,					size={"shortest_edge": config.image_size + 3_2}      ,					)
  UpperCAmelCase__			:				List[str]												=       image_processor(images=prepare_img()      ,					return_tensors="pt"		)
  UpperCAmelCase__			:				int												=       model(**lowercase__		)
  UpperCAmelCase__			:				Any												=       outputs.logits

  if model_name.startswith("deeplabv3_"		):
    assert logits.shape == (1, 2_1, 6_5, 6_5)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      UpperCAmelCase__			:				str												=       torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
              [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
          ]		)

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

    assert torch.allclose(logits[0, :3, :3, :3]      ,					lowercase__      ,					atol=1E-4		)
  else:
    assert logits.shape == (1, 1_0_0_1)

    if model_name == "mobilenet_v2_1.4_224":
      UpperCAmelCase__			:				Optional[int]												=       torch.tensor([0.0181, -1.0015, 0.4688]		)
    elif model_name == "mobilenet_v2_1.0_224":
      UpperCAmelCase__			:				Dict												=       torch.tensor([0.2445, -1.1993, 0.1905]		)
    elif model_name == "mobilenet_v2_0.75_160":
      UpperCAmelCase__			:				Optional[Any]												=       torch.tensor([0.2482, 0.4136, 0.6669]		)
    elif model_name == "mobilenet_v2_0.35_96":
      UpperCAmelCase__			:				Tuple												=       torch.tensor([0.1451, -0.4624, 0.7192]		)
    else:
      UpperCAmelCase__			:				str												=       None

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

  Path(lowercase__		).mkdir(exist_ok=lowercase__		)
  print(F"""Saving model {model_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:
    print("Pushing to the hub..."		)
    UpperCAmelCase__			:				List[str]												=       "google/" + model_name
    image_processor.push_to_hub(lowercase__		)
    model.push_to_hub(lowercase__		)


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

      SCREAMING_SNAKE_CASE    =					parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )





'''simple docstring'''





def snake_case_    (				lowercase__      ,					lowercase__ = 1_3		):
  UpperCAmelCase__			:				Optional[Any]												=       ""
  for c in s:
    if "A" <= c <= "Z":
      out += chr(ord("A"		) + (ord(lowercase__		) - ord("A"		) + n) % 2_6		)
    elif "a" <= c <= "z":
      out += chr(ord("a"		) + (ord(lowercase__		) - ord("a"		) + n) % 2_6		)
    else:
      out += c
  return out







def snake_case_    (				):
  UpperCAmelCase__			:				Dict												=       input("Enter message: "		)

  UpperCAmelCase__			:				Optional[int]												=       dencrypt(lowercase__      ,					1_3		)
  print("Encryption:"      ,					lowercase__		)

  UpperCAmelCase__			:				Union[str, Any]												=       dencrypt(lowercase__      ,					1_3		)
  print("Decryption: "      ,					lowercase__		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



