






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	(  __lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

						__magic_name__            =     re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$" ,						snake_case_   )
						if matches:
												__magic_name__            =     float(matches[1]   )
												__magic_name__            =     int(matches[2]   )

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						__magic_name__            =     1_0_0_1
						__magic_name__            =     """imagenet-1k-id2label.json"""
						__magic_name__            =     """huggingface/label-files"""
						__magic_name__            =     json.load(open(hf_hub_download(snake_case_ ,						snake_case_ ,						repo_type="dataset"   ) ,						"r"   )   )
						__magic_name__            =     {int(snake_case_   ) + 1: v for k, v in idalabel.items()}
						__magic_name__            =     """background"""
						__magic_name__            =     idalabel
						__magic_name__            =     {v: k for k, v in idalabel.items()}

						return config






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
						__magic_name__            =     Image.open(requests.get(snake_case_ ,						stream=snake_case_   ).raw   )
						return im






@torch.no_grad()
def _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:List[str]=False   ):

						'''simple docstring'''






						__magic_name__            =     get_mobilenet_va_config(snake_case_   )

						# Load 🤗 model
						__magic_name__            =     MobileNetVaForImageClassification(snake_case_   ).eval()

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

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						__magic_name__            =     MobileNetVaImageProcessor(
						    crop_size={"width": config.image_size, "height": config.image_size} ,						size={"shortest_edge": config.image_size + 3_2} ,						)
						__magic_name__            =     image_processor(images=prepare_img() ,						return_tensors="pt"   )
						__magic_name__            =     model(**snake_case_   )
						__magic_name__            =     outputs.logits

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

						if model_name == "mobilenet_v1_1.0_224":
												__magic_name__            =     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":
												__magic_name__            =     torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
						else:
												__magic_name__            =     None

						if expected_logits is not None:
												assert torch.allclose(logits[0, :3] ,						snake_case_ ,						atol=1E-4   )

						Path(snake_case_   ).mkdir(exist_ok=snake_case_   )
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''   )
						model.save_pretrained(snake_case_   )
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
						image_processor.save_pretrained(snake_case_   )

						if push_to_hub:
												print("Pushing to the hub..."   )
												__magic_name__            =     """google/""" + model_name
												image_processor.push_to_hub(snake_case_   )
												model.push_to_hub(snake_case_   )


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
			)











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return (2_5_5, 2_5_5, 2_5_5)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(__lowerCamelCase ,						1 ,						1   )   )






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     img.load()

						# loop through the image-coordinates
						for image_x in range(__lowerCamelCase   ):
												for image_y in range(__lowerCamelCase   ):
																		# determine the figure-coordinates based on the image-coordinates
																		__magic_name__            =     figure_width / image_width * image_height
																		__magic_name__            =     figure_center_x + (image_x / image_width - 0.5) * figure_width
																		__magic_name__            =     figure_center_y + (image_y / image_height - 0.5) * figure_height

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								__magic_name__            =     get_color_coded_rgb(__lowerCamelCase   )
																		else:
																								__magic_name__            =     get_black_and_white_rgb(__lowerCamelCase   )

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       get_image()

			# uncomment for colored version, different section, zoomed in
			# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
			# figure_width = 0.8)

			# uncomment for black and white version, full figure
			# img = get_image(use_distance_color_coding = False)

			# uncomment to save the image
			# img.save("mandelbrot.png")

			img.show()



