
"""simple docstring"""





import math
import sys

import cva
import numpy as np



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						np.ndarray:


					"""simple docstring"""





					_UpperCamelCase									=    math.sqrt(__snake_case      )
					_UpperCamelCase									=    1 / (sigma * math.sqrt(2 * math.pi      ))
					return cons * np.exp(-((img / sigma) ** 2) * 0.5      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						np.ndarray:


					"""simple docstring"""





					_UpperCamelCase									=    kernel_size // 2
					return img[x - half : x + half + 1, y - half : y + half + 1]



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						np.ndarray:


					"""simple docstring"""





					_UpperCamelCase									=    np.zeros((kernel_size, kernel_size)      )
					for i in range(0,	__snake_case      ):
										for j in range(0,	__snake_case      ):
															_UpperCamelCase									=    math.sqrt(
															    abs(i - kernel_size // 2      ) ** 2 + abs(j - kernel_size // 2      ) ** 2      )
					return vec_gaussian(__snake_case,	__snake_case      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	)				->						np.ndarray:


					"""simple docstring"""





					_UpperCamelCase									=    np.zeros(img.shape      )
					_UpperCamelCase									=    get_gauss_kernel(__snake_case,	__snake_case      )
					_UpperCamelCase					,    _UpperCamelCase									=    img.shape
					for i in range(kernel_size // 2,	size_x - kernel_size // 2      ):
										for j in range(kernel_size // 2,	size_y - kernel_size // 2      ):
															_UpperCamelCase									=    get_slice(__snake_case,	__snake_case,	__snake_case,	__snake_case      )
															_UpperCamelCase									=    img_s - img_s[kernel_size // 2, kernel_size // 2]
															_UpperCamelCase									=    vec_gaussian(__snake_case,	__snake_case      )
															_UpperCamelCase									=    np.multiply(__snake_case,	__snake_case      )
															_UpperCamelCase									=    np.multiply(__snake_case,	__snake_case      )
															_UpperCamelCase									=    np.sum(__snake_case      ) / np.sum(__snake_case      )
															_UpperCamelCase									=    val
					return imga



def  lowerCamelCase__						(     __snake_case      )				->						tuple:


					"""simple docstring"""





					_UpperCamelCase									=    args[1] if args[1:] else '''../image_data/lena.jpg'''
					_UpperCamelCase									=    float(args[2]      ) if args[2:] else 1.0
					_UpperCamelCase									=    float(args[3]      ) if args[3:] else 1.0
					if args[4:]:
										_UpperCamelCase									=    int(args[4]      )
										_UpperCamelCase									=    kernel_size + abs(kernel_size % 2 - 1      )
					else:
										_UpperCamelCase									=    5
					return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
				_a				,				_a				,				_a				,				_a								=						parse_args(sys.argv)
				_a								=						cva.imread(filename, 0)
				cva.imshow("""input image""", img)

				_a								=						img / 255
				_a								=						out.astype("""float32""")
				_a								=						bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
				_a								=						out * 255
				_a								=						np.uinta(out)
				cva.imshow("""output image""", out)
				cva.waitKey(0)
				cva.destroyAllWindows()











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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

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

    A__					=       re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,							UpperCamelCase_						)
    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__					=       1_0_0_1
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       '''huggingface/label-files'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						) + 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__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       MobileNetVaForImageClassification(UpperCamelCase_						).eval()

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

    # 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 + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

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

    if model_name == "mobilenet_v1_1.0_224":
        A__					=       torch.tensor([-4.1739, -1.1233, 3.1205]						)
    elif model_name == "mobilenet_v1_0.75_192":
        A__					=       torch.tensor([-3.9440, -2.3141, -0.3333]						)
    else:
        A__					=       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...'''						)
        A__					=       '''google/''' + model_name
        image_processor.push_to_hub(UpperCamelCase_						)
        model.push_to_hub(UpperCamelCase_						)


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

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