
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  			=					logging.get_logger(__name__)




def 				a						(							snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					MobileNetVaConfig(layer_norm_eps=0.0_0_1					)

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

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

				# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
				# the usual 1000. The first class (index 0) is "background".
				lowercase_       =					1_001
				lowercase_       =					'''imagenet-1k-id2label.json'''
				lowercase_       =					'''huggingface/label-files'''
				lowercase_       =					json.load(open(hf_hub_download(snake_case__						,   snake_case__						,   repo_type='''dataset'''					)						,   '''r'''					)					)
				lowercase_       =					{int(snake_case__					) + 1: v for k, v in idalabel.items()}
				lowercase_       =					'''background'''
				lowercase_       =					idalabel
				lowercase_       =					{v: k for k, v in idalabel.items()}

				return config




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase_       =					Image.open(requests.get(snake_case__						,   stream=snake_case__					).raw					)
				return im




@torch.no_grad()
def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					int						,   snake_case__:					Dict						,   snake_case__:					Optional[Any]=False					):


				'''simple docstring'''
				lowercase_       =					get_mobilenet_va_config(snake_case__					)

				# Load 🤗 model
				lowercase_       =					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
				lowercase_       =					MobileNetVaImageProcessor(
				    crop_size={'''width''': config.image_size, '''height''': config.image_size}						,   size={'''shortest_edge''': config.image_size + 32}						,   )
				lowercase_       =					image_processor(images=prepare_img()						,   return_tensors='''pt'''					)
				lowercase_       =					model(**snake_case__					)
				lowercase_       =					outputs.logits

				assert logits.shape == (1, 1_001)

				if model_name == "mobilenet_v1_1.0_224":
								lowercase_       =					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_       =					torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]					)
				else:
								lowercase_       =					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...'''					)
								lowercase_       =					'''google/''' + model_name
								image_processor.push_to_hub(snake_case__					)
								model.push_to_hub(snake_case__					)


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






def 				a						(							snake_case__:					list						,   snake_case__:					list						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if index == number_of_items:
								return 0
				lowercase_       =					0
				lowercase_       =					0
				lowercase_       =					knapsack(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   index + 1					)
				if weights[index] <= max_weight:
								lowercase_       =					values[index] + knapsack(
								    snake_case__						,   snake_case__						,   snake_case__						,   max_weight - weights[index]						,   index + 1					)
				return max(snake_case__						,   snake_case__					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




