






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()
lowercase  							= logging.get_logger(__name__)





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							MobileNetVaConfig(layer_norm_eps=0.0_01)

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

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

							if model_name.startswith('deeplabv3_'):
														snake_case_           =							8
														snake_case_           =							21
														snake_case_           =							'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".
														snake_case_           =							10_01
														snake_case_           =							'imagenet-1k-id2label.json'

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

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

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

							return config





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





@torch.no_grad()
def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_=False):
							snake_case_           =							get_mobilenet_va_config(a_)

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

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

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

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

														if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																					snake_case_           =							torch.tensor(
																					    [
																					        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
																					        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
																					        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
																					    ])

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

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

														if model_name == "mobilenet_v2_1.4_224":
																					snake_case_           =							torch.tensor([0.01_81, -1.00_15, 0.46_88])
														elif model_name == "mobilenet_v2_1.0_224":
																					snake_case_           =							torch.tensor([0.24_45, -1.19_93, 0.19_05])
														elif model_name == "mobilenet_v2_0.75_160":
																					snake_case_           =							torch.tensor([0.24_82, 0.41_36, 0.66_69])
														elif model_name == "mobilenet_v2_0.35_96":
																					snake_case_           =							torch.tensor([0.14_51, -0.46_24, 0.71_92])
														else:
																					snake_case_           =							None

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

							Path(a_).mkdir(exist_ok=a_)
							print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''')
							model.save_pretrained(a_)
							print(f'''Saving image processor to {pytorch_dump_folder_path}''')
							image_processor.save_pretrained(a_)

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


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

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













from __future__ import annotations





def 						__UpperCAmelCase      (					a_):
							for r in range(1							,					a_):
														snake_case_           =							[]
														for x in range(n - 1):
																					snake_case_           =							pow(a_							,					a_							,					a_)
																					if val in li:
																												break
																					li.append(a_)
														else:
																					return r
							return None


if __name__ == "__main__":
	lowercase  							= int(input("Enter a prime number q: "))
	lowercase  							= find_primitive(q)
	if a is None:
		print(f'Cannot find the primitive for the value: {a!r}')
	else:
		lowercase  							= int(input("Enter private key of A: "))
		lowercase  							= pow(a, a_private, q)
		lowercase  							= int(input("Enter private key of B: "))
		lowercase  							= pow(a, b_private, q)

		lowercase  							= pow(b_public, a_private, q)
		lowercase  							= pow(a_public, b_private, q)

		print("The key value generated by A is: ", a_secret)
		print("The key value generated by B is: ", b_secret)





