

'''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   :			List[str]         = logging.get_logger(__name__)





def       lowercase					(     __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							str   					=   MobileNetVaConfig(layer_norm_eps=0.0_0_1							)

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

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

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

					return config





def       lowercase					(     ):



					'''simple docstring'''







					UpperCAmelCase		:							Any   					=   'http://images.cocodataset.org/val2017/000000039769.jpg'
					UpperCAmelCase		:							Tuple   					=   Image.open(requests.get(lowerCAmelCase_ ,    stream=lowerCAmelCase_							).raw							)
					return im





@torch.no_grad()
def       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__=False							):



					'''simple docstring'''







					UpperCAmelCase		:							str   					=   get_mobilenet_va_config(lowerCAmelCase_							)

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

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										UpperCAmelCase		:							List[str]   					=   torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]							)
					elif model_name == "mobilenet_v1_0.75_192":
										UpperCAmelCase		:							Any   					=   torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]							)
					else:
										UpperCAmelCase		:							str   					=   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..."							)
										UpperCAmelCase		:							List[str]   					=   'google/' + model_name
										image_processor.push_to_hub(lowerCAmelCase_							)
										model.push_to_hub(lowerCAmelCase_							)


if __name__ == "__main__":
				a   :			Optional[Any]         = 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   :			Optional[int]         = parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)





'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




