"""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()
lowerCAmelCase__  :  Optional[int]						     =	logging.get_logger(__name__)



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							MobileNetVaConfig(layer_norm_eps=0.001       )

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

				UpperCAmelCase__             =							re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'					,			lowerCamelCase       )
				if matches:
								UpperCAmelCase__             =							float(matches[1]       )
								UpperCAmelCase__             =							int(matches[2]       )

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

				return config



def   a_						(					):
				UpperCAmelCase__             =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__             =							Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase       ).raw       )
				return im



@torch.no_grad()
def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=False       ):
				UpperCAmelCase__             =							get_mobilenet_va_config(lowerCamelCase       )

				# Load 🤗 model
				UpperCAmelCase__             =							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__             =							MobileNetVaImageProcessor(
				    crop_size={'width': config.image_size, 'height': config.image_size}					,			size={'shortest_edge': config.image_size + 3_2}					,			)
				UpperCAmelCase__             =							image_processor(images=prepare_img()					,			return_tensors='pt'       )
				UpperCAmelCase__             =							model(**lowerCamelCase       )
				UpperCAmelCase__             =							outputs.logits

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

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


if __name__ == "__main__":
						lowerCAmelCase__  :  int						     =	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.'
						)

						lowerCAmelCase__  :  Dict						     =	parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)




"""simple docstring"""






import argparse


lowerCAmelCase__  :  List[str]						     =	'docs/source/_static/js/custom.js'



def   a_						(					lowerCamelCase       ):
				with open(lowerCamelCase					,			encoding='utf-8'					,			newline='\n'       ) as f:
								UpperCAmelCase__             =							f.readlines()
				UpperCAmelCase__             =							0

				# First let's put the right version
				while not lines[index].startswith('const stableVersion ='       ):
								index += 1
				UpperCAmelCase__             =							f'''const stableVersion = "v{version}"\n'''

				# Then update the dictionary
				while not lines[index].startswith('const versionMapping = {'       ):
								index += 1

				# We go until the end
				while not lines[index].startswith('}'       ):
								index += 1
				# We add the new version at the end
				lines[index - 1] += f'''    "v{version}": "v{version}",\n'''

				with open(lowerCamelCase					,			'w'					,			encoding='utf-8'					,			newline='\n'       ) as f:
								f.writelines(lowerCamelCase       )


if __name__ == "__main__":
						lowerCAmelCase__  :  List[Any]						     =	argparse.ArgumentParser()
						parser.add_argument('--version', help='Release version.')
						lowerCAmelCase__  :  Optional[int]						     =	parser.parse_args()
						update_custom_js(args.version)



