



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()
__snake_case		:List[Any]   	=  logging.get_logger(__name__)







def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       MobileNetVaConfig(layer_norm_eps=0.0_01       )

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

    __a		 =       re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''     ,				__UpperCamelCase       )
    if matches:
        __a		 =       float(matches[1]       )
        __a		 =       int(matches[2]       )

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

    return config







def   __snake_case			(    ):
    __a		 =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
    __a		 =       Image.open(requests.get(__UpperCamelCase     ,				stream=__UpperCamelCase       ).raw       )
    return im







@torch.no_grad()
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=False       ):
    __a		 =       get_mobilenet_va_config(__UpperCamelCase       )

    # Load 🤗 model
    __a		 =       MobileNetVaForImageClassification(__UpperCamelCase       ).eval()

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

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

    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v1_1.0_224":
        __a		 =       torch.tensor([-4.17_39, -1.12_33, 3.12_05]       )
    elif model_name == "mobilenet_v1_0.75_192":
        __a		 =       torch.tensor([-3.94_40, -2.31_41, -0.33_33]       )
    else:
        __a		 =       None

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

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

    if push_to_hub:
        print('''Pushing to the hub...'''       )
        __a		 =       """google/""" + model_name
        image_processor.push_to_hub(__UpperCamelCase       )
        model.push_to_hub(__UpperCamelCase       )


if __name__ == "__main__":
 __snake_case		:Any   	=  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.'''
 )

 __snake_case		:List[Any]   	=  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

__snake_case		:Optional[Any]   	=  []





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        if board[row][i] == 1:
            return False
    for i in range(len(_UpperCAmelCase       )       ):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				-1     ,				-1       )       ):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				len(_UpperCAmelCase       )       )       ):
        if board[i][j] == 1:
            return False
    return True





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    if row >= len(_UpperCAmelCase       ):
        solution.append(_UpperCAmelCase       )
        printboard(_UpperCAmelCase       )
        print()
        return True
    for i in range(len(_UpperCAmelCase       )       ):
        if is_safe(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
            __a		 =       1
            solve(_UpperCAmelCase     ,				row + 1       )
            __a		 =       0
    return False





def   __snake_case			(    _UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(_UpperCAmelCase       )       ):
            if board[i][j] == 1:
                print('''Q'''     ,				end=''' '''       )
            else:
                print('''.'''     ,				end=''' '''       )
        print()


# n=int(input("The no. of queens"))
__snake_case		:Optional[Any]   	=  8
__snake_case		:Tuple   	=  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))





