


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


logging.set_verbosity_info()
_UpperCAmelCase				: Optional[int]							    =				logging.get_logger(__name__)

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				MobileNetVaConfig(layer_norm_eps=0.0_01)

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

						__lowerCAmelCase							       =				re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$''',				lowerCamelCase)
						if matches:
												__lowerCAmelCase							       =				float(matches[1])
												__lowerCAmelCase							       =				int(matches[2])

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						__lowerCAmelCase							       =				1_0_0_1
						__lowerCAmelCase							       =				'''imagenet-1k-id2label.json'''
						__lowerCAmelCase							       =				'''huggingface/label-files'''
						__lowerCAmelCase							       =				json.load(open(hf_hub_download(lowerCamelCase,				lowerCamelCase,				repo_type='''dataset'''),				'''r'''))
						__lowerCAmelCase							       =				{int(lowerCamelCase) + 1: v for k, v in idalabel.items()}
						__lowerCAmelCase							       =				'''background'''
						__lowerCAmelCase							       =				idalabel
						__lowerCAmelCase							       =				{v: k for k, v in idalabel.items()}

						return config

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__lowerCAmelCase							       =				Image.open(requests.get(lowerCamelCase,				stream=lowerCamelCase).raw)
						return im

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				get_mobilenet_va_config(lowerCamelCase)

						# Load 🤗 model
						__lowerCAmelCase							       =				MobileNetVaForImageClassification(lowerCamelCase).eval()

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

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						__lowerCAmelCase							       =				MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size},				size={'''shortest_edge''': config.image_size + 3_2},				)
						__lowerCAmelCase							       =				image_processor(images=prepare_img(),				return_tensors='''pt''')
						__lowerCAmelCase							       =				model(**lowerCamelCase)
						__lowerCAmelCase							       =				outputs.logits

						assert logits.shape == (1, 1_0_0_1)

						if model_name == "mobilenet_v1_1.0_224":
												__lowerCAmelCase							       =				torch.tensor([-4.17_39, -1.12_33, 3.12_05])
						elif model_name == "mobilenet_v1_0.75_192":
												__lowerCAmelCase							       =				torch.tensor([-3.94_40, -2.31_41, -0.33_33])
						else:
												__lowerCAmelCase							       =				None

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

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

						if push_to_hub:
												print('''Pushing to the hub...''')
												__lowerCAmelCase							       =				'''google/''' + model_name
												image_processor.push_to_hub(lowerCamelCase)
												model.push_to_hub(lowerCamelCase)


if __name__ == "__main__":
	_UpperCAmelCase				: int							    =				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."""
	)

	_UpperCAmelCase				: Tuple							    =				parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)





'''simple docstring'''

from string import ascii_uppercase

_UpperCAmelCase				: Optional[int]							    =				{char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase				: Optional[int]							    =				dict(enumerate(ascii_uppercase))

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				len(lowerCamelCase)
						__lowerCAmelCase							       =				0
						while True:
												if x == i:
																		__lowerCAmelCase							       =				0
												if len(lowerCamelCase) == len(lowerCamelCase):
																		break
												key += key[i]
												i += 1
						return key

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] - dicta[key_new[i]]) % 2_6
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																		i += 1
																		or_txt += dicta[x]
						return or_txt

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''THE GERMAN ATTACK'''
						__lowerCAmelCase							       =				'''SECRET'''
						__lowerCAmelCase							       =				generate_key(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				cipher_text(lowerCamelCase,				lowerCamelCase)
						print(F"""Encrypted Text = {s}""")
						print(F"""Original Text = {original_text(lowerCamelCase,				lowerCamelCase)}""")


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

