


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     __UpperCamelCase    							=MobileNetVaConfig(layer_norm_eps=0.001				)

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

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

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     __UpperCamelCase    							=10_01
     __UpperCamelCase    							='imagenet-1k-id2label.json'
     __UpperCamelCase    							='huggingface/label-files'
     __UpperCamelCase    							=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     repo_type='dataset'				)			,     'r'				)				)
     __UpperCamelCase    							={int(SCREAMING_SNAKE_CASE__				) + 1: v for k, v in idalabel.items()}
     __UpperCamelCase    							='background'
     __UpperCamelCase    							=idalabel
     __UpperCamelCase    							={v: k for k, v in idalabel.items()}

     return config

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

@torch.no_grad()
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int=False				):
     __UpperCamelCase    							=get_mobilenet_va_config(SCREAMING_SNAKE_CASE__				)

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

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

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

     assert logits.shape == (1, 10_01)

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

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

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

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









import random
from typing import Any



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						list[Any]:
    for _ in range(len(lowerCamelCase_ ) ):
        _lowercase       :							Optional[int]      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase       :							str      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase		,				_lowercase       :							Optional[int]      		=		data[b], data[a]
    return data


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      str												= [0, 1, 2, 3, 4, 5, 6, 7]
 SCREAMING_SNAKE_CASE       :      int												= ["python", "says", "hello", "!"]
 print("Fisher-Yates Shuffle:")
 print("List", integers, strings)
 print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


