






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



def       _A    (__a							)				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	MobileNetVaConfig(layer_norm_eps=0.0_01							)

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

    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''					,       lowerCamelCase__							)
    if matches:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	float(matches[1]							)
        SCREAMING_SNAKE_CASE_   :						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".
    SCREAMING_SNAKE_CASE_   :						List[str]							=	10_01
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE_   :						str							=	'''huggingface/label-files'''
    SCREAMING_SNAKE_CASE_   :						Dict							=	json.load(open(hf_hub_download(lowerCamelCase__					,       lowerCamelCase__					,       repo_type='''dataset'''							)					,       '''r'''							)							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	{int(lowerCamelCase__							) + 1: v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE_   :						List[str]							=	'''background'''
    SCREAMING_SNAKE_CASE_   :						str							=	idalabel
    SCREAMING_SNAKE_CASE_   :						Any							=	{v: k for k, v in idalabel.items()}

    return config



def       _A    ()				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	Image.open(requests.get(lowerCamelCase__					,       stream=lowerCamelCase__							).raw							)
    return im



@torch.no_grad()
def       _A    (__a					,       __a					,       __a					,       __a=False							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	get_mobilenet_va_config(lowerCamelCase__							)

    # Load 🤗 model
    SCREAMING_SNAKE_CASE_   :						int							=	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
    SCREAMING_SNAKE_CASE_   :						Dict							=	MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}					,       size={'''shortest_edge''': config.image_size + 32}					,       )
    SCREAMING_SNAKE_CASE_   :						Dict							=	image_processor(images=prepare_img()					,       return_tensors='''pt'''							)
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	model(**lowerCamelCase__							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	outputs.logits

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

    if model_name == "mobilenet_v1_1.0_224":
        SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor([-4.17_39, -1.12_33, 3.12_05]							)
    elif model_name == "mobilenet_v1_0.75_192":
        SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor([-3.94_40, -2.31_41, -0.33_33]							)
    else:
        SCREAMING_SNAKE_CASE_   :						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...'''							)
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''google/''' + model_name
        image_processor.push_to_hub(lowerCamelCase__							)
        model.push_to_hub(lowerCamelCase__							)


if __name__ == "__main__":
  UpperCAmelCase_     :				Tuple         =				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."""
  )

  UpperCAmelCase_     :				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"""






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray



def       _A    (__a					,       __a					,       __a = False							)				-> list[float]:
    """simple docstring"""






    if radian_mode:
        return [magnitude * cos(__a							), magnitude * sin(__a							)]
    return [magnitude * cos(radians(__a							)							), magnitude * sin(radians(__a							)							)]



def       _A    (__a					,       __a					,       __a = 10**-1							)				-> bool:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						NDArray[floataa]							=	cross(__a					,       __a							)
    SCREAMING_SNAKE_CASE_   :						float							=	sum(__a							)
    return abs(__a							) < eps


if __name__ == "__main__":
  # Test to check if it works
  UpperCAmelCase_     :				Union[str, Any]         =				array(
      [
          polar_force(7_1_8.4, 180 - 30),
          polar_force(8_7_9.5_4, 45),
          polar_force(100, -90),
      ]
  )

  UpperCAmelCase_     :				NDArray[floataa]         =				array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  UpperCAmelCase_     :				Union[str, Any]         =				array(
      [
          polar_force(30 * 9.8_1, 15),
          polar_force(215, 180 - 45),
          polar_force(264, 90 - 30),
      ]
  )

  UpperCAmelCase_     :				Optional[int]         =				array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  UpperCAmelCase_     :				List[str]         =				array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

  UpperCAmelCase_     :				Any         =				array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()


