

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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
a_     :    Any     		=   logging.get_logger(__name__)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      MobileNetVaConfig(layer_norm_eps=0.0_01						)

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

 SCREAMING_SNAKE_CASE		    =      re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'		,				_UpperCamelCase						)
 if matches:
  SCREAMING_SNAKE_CASE		    =      float(matches[1]						)
  SCREAMING_SNAKE_CASE		    =      int(matches[2]						)

 # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
 # the usual 1000. The first class (index 0) is "background".
 SCREAMING_SNAKE_CASE		    =      10_01
 SCREAMING_SNAKE_CASE		    =      'imagenet-1k-id2label.json'
 SCREAMING_SNAKE_CASE		    =      'huggingface/label-files'
 SCREAMING_SNAKE_CASE		    =      json.load(open(hf_hub_download(_UpperCamelCase		,				_UpperCamelCase		,				repo_type='dataset'						)		,				'r'						)						)
 SCREAMING_SNAKE_CASE		    =      {int(_UpperCamelCase						) + 1: v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE		    =      'background'
 SCREAMING_SNAKE_CASE		    =      idalabel
 SCREAMING_SNAKE_CASE		    =      {v: k for k, v in idalabel.items()}

 return config


def 							__lowerCAmelCase     (							)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE		    =      Image.open(requests.get(_UpperCamelCase		,				stream=_UpperCamelCase						).raw						)
 return im


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Dict=False						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      get_mobilenet_va_config(_UpperCamelCase						)

 # Load 🤗 model
 SCREAMING_SNAKE_CASE		    =      MobileNetVaForImageClassification(_UpperCamelCase						).eval()

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

 # Check outputs on an image, prepared by MobileNetV1ImageProcessor
 SCREAMING_SNAKE_CASE		    =      MobileNetVaImageProcessor(
     crop_size={'width': config.image_size, 'height': config.image_size}		,				size={'shortest_edge': config.image_size + 32}		,				)
 SCREAMING_SNAKE_CASE		    =      image_processor(images=prepare_img()		,				return_tensors='pt'						)
 SCREAMING_SNAKE_CASE		    =      model(**_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      outputs.logits

 assert logits.shape == (1, 10_01)

 if model_name == "mobilenet_v1_1.0_224":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([-4.17_39, -1.12_33, 3.12_05]						)
 elif model_name == "mobilenet_v1_0.75_192":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([-3.94_40, -2.31_41, -0.33_33]						)
 else:
  SCREAMING_SNAKE_CASE		    =      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...'						)
  SCREAMING_SNAKE_CASE		    =      'google/' + model_name
  image_processor.push_to_hub(_UpperCamelCase						)
  model.push_to_hub(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    Optional[Any]     		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="mobilenet_v1_1.0_224",
          type=str,
          help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<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."
      )

      a_     :    List[str]     		=   parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )




import string


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ''
 for i in sequence:
  SCREAMING_SNAKE_CASE		    =      ord(_UpperCamelCase						)
  if 65 <= extract <= 90:
   output += chr(1_55 - extract						)
  elif 97 <= extract <= 1_22:
   output += chr(2_19 - extract						)
  else:
   output += i
 return output


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      string.ascii_letters
 SCREAMING_SNAKE_CASE		    =      string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
 return "".join(
     letters_reversed[letters.index(_UpperCamelCase						)] if c in letters else c for c in sequence						)


def 							__lowerCAmelCase     (							)		->				None:
 '''simple docstring'''


 from timeit import timeit

 print('Running performance benchmarks...'						)
 SCREAMING_SNAKE_CASE		    =      'from string import printable ; from __main__ import atbash, atbash_slow'
 print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)
 print(f""">      atbash(): {timeit('atbash(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)


if __name__ == "__main__":
      for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
            print(F"""{example} encrypted in atbash: {atbash(example)}""")
      benchmark()

