
'''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()
_lowercase				:						Optional[int]												= logging.get_logger(__name__)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[Any]							):



						"""simple docstring"""





						lowercase_			:  Dict									= MobileNetVaConfig(layer_norm_eps=0.001							)

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

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

						if model_name.startswith('''deeplabv3_'''							):
												lowercase_			:  Union[str, Any]									= 8
												lowercase_			:  Union[str, Any]									= 21
												lowercase_			:  Optional[Any]									= '''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".
												lowercase_			:  int									= 1001
												lowercase_			:  Dict									= '''imagenet-1k-id2label.json'''

						lowercase_			:  int									= '''huggingface/label-files'''
						lowercase_			:  List[str]									= json.load(open(hf_hub_download(_UpperCamelCase   ,   _UpperCamelCase   ,   repo_type='''dataset'''							)   ,   '''r'''							)							)

						if config.num_labels == 1001:
												lowercase_			:  str									= {int(_UpperCamelCase							) + 1: v for k, v in idalabel.items()}
												lowercase_			:  Any									= '''background'''
						else:
												lowercase_			:  int									= {int(_UpperCamelCase							): v for k, v in idalabel.items()}

						lowercase_			:  Any									= idalabel
						lowercase_			:  Optional[Any]									= {v: k for k, v in idalabel.items()}

						return config
def       snake_case_	(       ):



						"""simple docstring"""





						lowercase_			:  Any									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase_			:  int									= Image.open(requests.get(_UpperCamelCase   ,   stream=_UpperCamelCase							).raw							)
						return im
@torch.no_grad()
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				Optional[int]=False							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= get_mobilenet_va_config(_UpperCamelCase							)

						# Load 🤗 model
						if model_name.startswith('''deeplabv3_'''							):
												lowercase_			:  List[Any]									= MobileNetVaForSemanticSegmentation(_UpperCamelCase							).eval()
						else:
												lowercase_			:  List[str]									= 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
						lowercase_			:  List[Any]									= MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size}   ,   size={'''shortest_edge''': config.image_size + 32}   ,   )
						lowercase_			:  Union[str, Any]									= image_processor(images=prepare_img()   ,   return_tensors='''pt'''							)
						lowercase_			:  List[Any]									= model(**_UpperCamelCase							)
						lowercase_			:  Union[str, Any]									= outputs.logits

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

												if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																		lowercase_			:  Union[str, Any]									= 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]   ,   _UpperCamelCase   ,   atol=1E-4							)
						else:
												assert logits.shape == (1, 1001)

												if model_name == "mobilenet_v2_1.4_224":
																		lowercase_			:  Optional[Any]									= torch.tensor([0.0181, -1.0015, 0.4688]							)
												elif model_name == "mobilenet_v2_1.0_224":
																		lowercase_			:  str									= torch.tensor([0.2445, -1.1993, 0.1905]							)
												elif model_name == "mobilenet_v2_0.75_160":
																		lowercase_			:  Tuple									= torch.tensor([0.2482, 0.4136, 0.6669]							)
												elif model_name == "mobilenet_v2_0.35_96":
																		lowercase_			:  List[str]									= torch.tensor([0.1451, -0.4624, 0.7192]							)
												else:
																		lowercase_			:  Optional[int]									= 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...'''							)
												lowercase_			:  Tuple									= '''google/''' + model_name
												image_processor.push_to_hub(_UpperCamelCase							)
												model.push_to_hub(_UpperCamelCase							)


if __name__ == "__main__":
							_lowercase				:						Dict												= 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				:						Union[str, Any]												= parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)





'''simple docstring'''







import math
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5   ,   int(math.sqrt(__SCREAMING_SNAKE_CASE							) + 1							)   ,   6							):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 2000000							):



						"""simple docstring"""





						return sum(num for num in range(3   ,   __SCREAMING_SNAKE_CASE   ,   2							) if is_prime(__SCREAMING_SNAKE_CASE							)							) + 2 if n > 2 else 0


if __name__ == "__main__":
							print(f"""{solution() = }""")



