
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__:   Union[str, 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__:   List[Any]		,  snake_case__:   Optional[int]		,  snake_case__:   Tuple		,  snake_case__:   Dict=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
						)






from __future__ import annotations




def 							a		(				snake_case__:   list[list[int]]   ):


				'''simple docstring'''

				# preprocessing the first row
				for i in range(1		,  len(matrix[0]   )   ):
								matrix[0][i] += matrix[0][i - 1]

				# preprocessing the first column
				for i in range(1		,  len(snake_case__   )   ):
								matrix[i][0] += matrix[i - 1][0]

				# updating the path cost for current position
				for i in range(1		,  len(snake_case__   )   ):
								for j in range(1		,  len(matrix[0]   )   ):
												matrix[i][j] += min(matrix[i - 1][j]		,  matrix[i][j - 1]   )

				return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




