

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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase		   =    logging.get_logger(__name__)







def 	lowerCamelCase__			(   __A						:Dict		):


       """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_v2_([^_]*)_([^_]*)$"""  ,lowercase_		)
       if matches:
              __snake_case					  =       float(matches[1]		)
              __snake_case					  =       int(matches[2]		)

       if model_name.startswith("""deeplabv3_"""		):
              __snake_case					  =       8
              __snake_case					  =       2_1
              __snake_case					  =       'pascal-voc-id2label.json'
       else:
              # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
              # of the usual 1000. The first class (index 0) is "background".
              __snake_case					  =       1_0_0_1
              __snake_case					  =       'imagenet-1k-id2label.json'

       __snake_case					  =       'huggingface/label-files'
       __snake_case					  =       json.load(open(hf_hub_download(lowercase_  ,lowercase_  ,repo_type="""dataset"""		)  ,"""r"""		)		)

       if config.num_labels == 1_0_0_1:
              __snake_case					  =       {int(lowercase_		) + 1: v for k, v in idalabel.items()}
              __snake_case					  =       'background'
       else:
              __snake_case					  =       {int(lowercase_		): v for k, v in idalabel.items()}

       __snake_case					  =       idalabel
       __snake_case					  =       {v: k for k, v in idalabel.items()}

       return config







def 	lowerCamelCase__			(   ):


       """simple docstring"""




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







@torch.no_grad()
def 	lowerCamelCase__			(   __A						:int  ,__A						:Any  ,__A						:Optional[Any]  ,__A						:Union[str, Any]=False		):


       """simple docstring"""




       __snake_case					  =       get_mobilenet_va_config(lowercase_		)

       # Load 🤗 model
       if model_name.startswith("""deeplabv3_"""		):
              __snake_case					  =       MobileNetVaForSemanticSegmentation(lowercase_		).eval()
       else:
              __snake_case					  =       MobileNetVaForImageClassification(lowercase_		).eval()

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

       # Check outputs on an image, prepared by MobileNetV2ImageProcessor
       __snake_case					  =       MobileNetVaImageProcessor(
           crop_size={"""width""": config.image_size, """height""": config.image_size}  ,size={"""shortest_edge""": config.image_size + 3_2}  ,)
       __snake_case					  =       image_processor(images=prepare_img()  ,return_tensors="""pt"""		)
       __snake_case					  =       model(**lowercase_		)
       __snake_case					  =       outputs.logits

       if model_name.startswith("""deeplabv3_"""		):
              assert logits.shape == (1, 2_1, 6_5, 6_5)

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     __snake_case					  =       torch.tensor(
                         [
                             [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                             [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
                             [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
                         ]		)

              else:
                     raise ValueError(F'Unknown model name: {model_name}'		)

              assert torch.allclose(logits[0, :3, :3, :3]  ,lowercase_  ,atol=1e-4		)
       else:
              assert logits.shape == (1, 1_0_0_1)

              if model_name == "mobilenet_v2_1.4_224":
                     __snake_case					  =       torch.tensor([0.0_181, -1.0_015, 0.4_688]		)
              elif model_name == "mobilenet_v2_1.0_224":
                     __snake_case					  =       torch.tensor([0.2_445, -1.1_993, 0.1_905]		)
              elif model_name == "mobilenet_v2_0.75_160":
                     __snake_case					  =       torch.tensor([0.2_482, 0.4_136, 0.6_669]		)
              elif model_name == "mobilenet_v2_0.35_96":
                     __snake_case					  =       torch.tensor([0.1_451, -0.4_624, 0.7_192]		)
              else:
                     __snake_case					  =       None

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

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

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


if __name__ == "__main__":
    __lowerCamelCase		   =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''mobilenet_v2_1.0_224''',
        type=str,
        help='''Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'''
    )

    __lowerCamelCase		   =    parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase__		   =    {'''UserAgent''': UserAgent().random}
UpperCamelCase__		   =    '''https://www.mywaifulist.moe/random'''







def 	lowerCamelCase__			(   __A						:str  ,__A						:str		):


       """simple docstring"""




       __snake_case					  =       requests.get(__A  ,headers=__A		)
       with open(__A  ,"""wb"""		) as file:
              file.write(image.content		)







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       BeautifulSoup(requests.get(__A  ,headers=__A		).text  ,"""html.parser"""		)
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:title"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:image"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""p"""  ,id="""description"""		).get_text()
       __snake_case  ,						__snake_case					  =       os.path.splitext(os.path.basename(__A		)		)
       __snake_case					  =       title.strip().replace(""" """  ,"""_"""		)
       __snake_case					  =       F'{image_title}{image_extension}'
       save_image(__A  ,__A		)
       return (title, description, image_title)


if __name__ == "__main__":
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__		   =    random_anime_character()
    print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')

