





'''simple docstring'''


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()
lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]			):
   lowercase_      :List[Any]         =			MobileNetVaConfig(layer_norm_eps=0.001			)

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

   lowercase_      :int         =			re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"     ,__lowerCamelCase			)
   if matches:
      lowercase_      :int         =			float(matches[1]			)
      lowercase_      :Union[str, Any]         =			int(matches[2]			)

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

   return config
def 							UpperCAmelCase_  (						):
   lowercase_      :Any         =			"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_      :Optional[int]         =			Image.open(requests.get(__lowerCamelCase     ,stream=__lowerCamelCase			).raw			)
   return im
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : Tuple     ,__lowerCamelCase : str     ,__lowerCamelCase : Dict     ,__lowerCamelCase : Tuple=False			):
   lowercase_      :Optional[Any]         =			get_mobilenet_va_config(__lowerCamelCase			)

   # Load 🤗 model
   lowercase_      :Any         =			MobileNetVaForImageClassification(__lowerCamelCase			).eval()

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

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   lowercase_      :Union[str, Any]         =			MobileNetVaImageProcessor(
       crop_size={"width": config.image_size, "height": config.image_size}     ,size={"shortest_edge": config.image_size + 32}     ,)
   lowercase_      :Optional[int]         =			image_processor(images=prepare_img()     ,return_tensors="pt"			)
   lowercase_      :Optional[int]         =			model(**__lowerCamelCase			)
   lowercase_      :Optional[int]         =			outputs.logits

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

   if model_name == "mobilenet_v1_1.0_224":
      lowercase_      :str         =			torch.tensor([-4.1_739, -1.1_233, 3.1_205]			)
   elif model_name == "mobilenet_v1_0.75_192":
      lowercase_      :str         =			torch.tensor([-3.9_440, -2.3_141, -0.3_333]			)
   else:
      lowercase_      :Optional[Any]         =			None

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

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

   if push_to_hub:
      print("Pushing to the hub..."			)
      lowercase_      :Optional[Any]         =			"google/" + model_name
      image_processor.push_to_hub(__lowerCamelCase			)
      model.push_to_hub(__lowerCamelCase			)


if __name__ == "__main__":
 lowerCAmelCase :					Optional[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.'''
 )

 lowerCAmelCase :					Optional[Any] 							=parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )









'''simple docstring'''


import numpy as np
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : int     ,__lowerCamelCase : List[str]     ,__lowerCamelCase : List[Any]			):
   lowercase_      :Dict         =			int(np.ceil((x_end - xa) / h			)			)
   lowercase_      :Optional[int]         =			np.zeros((n + 1,)			)
   lowercase_      :Tuple         =			ya
   lowercase_      :List[str]         =			xa

   for k in range(__lowerCamelCase			):
      lowercase_      :Any         =			f(__lowerCamelCase     ,y[k]			)
      lowercase_      :Optional[Any]         =			f(x + 0.5 * h     ,y[k] + 0.5 * h * ka			)
      lowercase_      :Optional[Any]         =			f(x + 0.5 * h     ,y[k] + 0.5 * h * ka			)
      lowercase_      :Any         =			f(x + h     ,y[k] + h * ka			)
      lowercase_      :Tuple         =			y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
      x += h

   return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()


