





'''simple docstring'''






__UpperCamelCase					    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			__snake_case					:      Dict							      =     {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

			__snake_case					:      Stack[int]							      =     Stack()
			__snake_case					:      Stack[str]							      =     Stack()

			for i in equation:
						if i.isdigit():
									# RULE 1
									operand_stack.push(int(_lowerCamelCase ) )
						elif i in operators:
									# RULE 2
									operator_stack.push(_lowerCamelCase )
						elif i == ")":
									# RULE 4
									__snake_case					:      Dict							      =     operator_stack.peek()
									operator_stack.pop()
									__snake_case					:      Dict							      =     operand_stack.peek()
									operand_stack.pop()
									__snake_case					:      Tuple							      =     operand_stack.peek()
									operand_stack.pop()

									__snake_case					:      Optional[Any]							      =     operators[opr](_lowerCamelCase    ,	_lowerCamelCase )
									operand_stack.push(_lowerCamelCase )

    # RULE 5
			return operand_stack.peek()


if __name__ == "__main__":
	__UpperCamelCase					    =	"(5 + ((4 * 2) * (2 + 3)))"
	# answer = 45
	print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")









'''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					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

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

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__snake_case					:      Tuple							      =     1001
			__snake_case					:      Any							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[Any]							      =     """huggingface/label-files"""
			__snake_case					:      List[Any]							      =     json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Dict							      =     {int(_lowerCamelCase ) + 1: v for k, v in idalabel.items()}
			__snake_case					:      List[str]							      =     """background"""
			__snake_case					:      List[str]							      =     idalabel
			__snake_case					:      List[Any]							      =     {v: k for k, v in idalabel.items()}

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     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
			__snake_case					:      Optional[int]							      =     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}    ,	size={"""shortest_edge""": config.image_size + 32}    ,	)
			__snake_case					:      Tuple							      =     image_processor(images=prepare_img()    ,	return_tensors="""pt""" )
			__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
			__snake_case					:      List[Any]							      =     outputs.logits

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     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...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	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					    =	parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


