



"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__ = 10				,      lowerCamelCase__ = 1000				,      lowerCamelCase__ = True			) ->							int:
							assert (
							    isinstance(lowerCamelCase__				,      lowerCamelCase__			)
							    and isinstance(lowerCamelCase__				,      lowerCamelCase__			)
							    and isinstance(lowerCamelCase__				,      lowerCamelCase__			)
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'			)
							return min_val if option else max_val

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							int:
							return int((number_a + number_a) / 2			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							None:
							assert (
							    isinstance(lowerCamelCase__				,      lowerCamelCase__			) and isinstance(lowerCamelCase__				,      lowerCamelCase__			) and isinstance(lowerCamelCase__				,      lowerCamelCase__			)
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError('argument value for lower and higher must be(lower > higher)'			)

							if not lower < to_guess < higher:
														raise ValueError(
														    'guess value must be within the range of lower and higher value'			)

							def answer(lowerCamelCase__			) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print('started...'			)

							A         =					lower
							A         =					higher

							A         =					[]

							while True:
														A         =					get_avg(lowerCamelCase__				,      lowerCamelCase__			)
														last_numbers.append(lowerCamelCase__			)

														if answer(lowerCamelCase__			) == "low":
																					A         =					number
														elif answer(lowerCamelCase__			) == "high":
																					A         =					number
														else:
																					break

							print(f"""guess the number : {last_numbers[-1]}"""			)
							print(f"""details : {last_numbers!s}"""			)

def   lowerCAmelCase__			(     ) ->							None:
							A         =					int(input('Enter lower value : '			).strip()			)
							A         =					int(input('Enter high value : '			).strip()			)
							A         =					int(input('Enter value to guess : '			).strip()			)
							guess_the_number(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)


if __name__ == "__main__":
							main()








"""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()
A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							A         =					MobileNetVaConfig(layer_norm_eps=0.0_01			)

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

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

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

							return config

def   lowerCAmelCase__			(     ) ->							List[Any]:
							A         =					'http://images.cocodataset.org/val2017/000000039769.jpg'
							A         =					Image.open(requests.get(lowerCamelCase__				,      stream=lowerCamelCase__			).raw			)
							return im

@torch.no_grad()
def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=False			) ->							Dict:
							A         =					get_mobilenet_va_config(lowerCamelCase__			)

							# Load 🤗 model
							A         =					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
							A         =					MobileNetVaImageProcessor(
							    crop_size={'width': config.image_size, 'height': config.image_size}				,      size={'shortest_edge': config.image_size + 32}				,      )
							A         =					image_processor(images=prepare_img()				,      return_tensors='pt'			)
							A         =					model(**lowerCamelCase__			)
							A         =					outputs.logits

							assert logits.shape == (1, 1001)

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


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



