




"""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 :	str       =   logging.get_logger(__name__)

def     snake_case__							(       _lowerCamelCase					)      ->Optional[Any]:






		"""simple docstring"""


		__lowercase					:		Dict        =					MobileNetVaConfig(layer_norm_eps=0.0_0_1					)

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

		__lowercase					:		Any        =					re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$",	SCREAMING_SNAKE_CASE__					)
		if matches:
				__lowercase					:		Optional[Any]        =					float(matches[1]					)
				__lowercase					:		Optional[Any]        =					int(matches[2]					)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase					:		Optional[Any]        =					10_01
		__lowercase					:		Union[str, Any]        =					"imagenet-1k-id2label.json"
		__lowercase					:		str        =					"huggingface/label-files"
		__lowercase					:		Tuple        =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	repo_type="dataset"					),	"r"					)					)
		__lowercase					:		str        =					{int(SCREAMING_SNAKE_CASE__					) + 1: v for k, v in idalabel.items()}
		__lowercase					:		List[Any]        =					"background"
		__lowercase					:		List[Any]        =					idalabel
		__lowercase					:		Dict        =					{v: k for k, v in idalabel.items()}

		return config

def     snake_case__							(       )      ->Optional[int]:






		"""simple docstring"""


		__lowercase					:		Dict        =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowercase					:		List[str]        =					Image.open(requests.get(SCREAMING_SNAKE_CASE__,	stream=SCREAMING_SNAKE_CASE__					).raw					)
		return im

@torch.no_grad()
def     snake_case__							(       _lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase=False					)      ->Union[str, Any]:






		"""simple docstring"""


		__lowercase					:		Optional[Any]        =					get_mobilenet_va_config(SCREAMING_SNAKE_CASE__					)

		# Load 🤗 model
		__lowercase					:		Union[str, Any]        =					MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__					).eval()

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

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase					:		List[Any]        =					MobileNetVaImageProcessor(
		    crop_size={"width": config.image_size, "height": config.image_size},	size={"shortest_edge": config.image_size + 32},	)
		__lowercase					:		List[Any]        =					image_processor(images=prepare_img(),	return_tensors="pt"					)
		__lowercase					:		Optional[int]        =					model(**SCREAMING_SNAKE_CASE__					)
		__lowercase					:		int        =					outputs.logits

		assert logits.shape == (1, 10_01)

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

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

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

		if push_to_hub:
				print("Pushing to the hub..."					)
				__lowercase					:		Optional[Any]        =					"google/" + model_name
				image_processor.push_to_hub(SCREAMING_SNAKE_CASE__					)
				model.push_to_hub(SCREAMING_SNAKE_CASE__					)


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





"""simple docstring"""




import requests

_lowerCAmelCase							:   List[Any]				=				"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  None:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       requests.get(_NEWS_API + bbc_news_api_key							).json()
     # each article in the list is a dict
     for i, article in enumerate(bbc_news_page["articles"]		,    1							):
          print(f'{i}.) {article["title"]}'							)


if __name__ == "__main__":
 fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
