
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 		lowercase_    (     A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      MobileNetVaConfig(layer_norm_eps=0.001	)

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

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

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

    return config



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

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



@torch.no_grad()
def 		lowercase_    (     A__				,	A__				,	A__				,	A__=False	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      get_mobilenet_va_config(A__	)

    # Load 🤗 model
    snake_case				  =      MobileNetVaForImageClassification(A__	).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    snake_case				  =      MobileNetVaImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size}				,	size={"shortest_edge": config.image_size + 32}				,	)
    snake_case				  =      image_processor(images=prepare_img()				,	return_tensors="pt"	)
    snake_case				  =      model(**A__	)
    snake_case				  =      outputs.logits

    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v1_1.0_224":
        snake_case				  =      torch.tensor([-4.1739, -1.1233, 3.1205]	)
    elif model_name == "mobilenet_v1_0.75_192":
        snake_case				  =      torch.tensor([-3.9440, -2.3141, -0.3333]	)
    else:
        snake_case				  =      None

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

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

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


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
   )



import sys

_A						      =				(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)



def 		lowercase_    (     A__	)							->							int:
    """simple docstring"""

    snake_case				  =      1
    for digit in s:
        product *= int(A__	)
    return product



def 		lowercase_    (     A__ = N	)							->							int:
    """simple docstring"""

    snake_case				  =      -sys.maxsize - 1
    snake_case				  =      n[:13]
    snake_case				  =      13
    while cur_index < len(A__	) - 13:
        if int(n[cur_index]	) >= int(substr[0]	):
            snake_case				  =      substr[1:] + n[cur_index]
            cur_index += 1
        else:
            snake_case				  =      max(A__				,	str_eval(A__	)	)
            snake_case				  =      n[cur_index : cur_index + 13]
            cur_index += 13
    return largest_product


if __name__ == "__main__":
   print(f"{solution() = }")

