

'''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		       =    logging.get_logger(__name__)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Union[str, Any]:
       A_						=      MobileNetVaConfig(layer_norm_eps=0.001				)

       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_						=      10_01
       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       UpperCAmelCase__    (      )			->			int:
       A_						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_						=      Image.open(requests.get(UpperCAmelCase__,       stream=UpperCAmelCase__				).raw				)
       return im
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=False				)			->			Optional[Any]:
       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, 10_01)

       if model_name == "mobilenet_v1_1.0_224":
              A_						=      torch.tensor([-4.1_739, -1.1_233, 3.1_205]				)
       elif model_name == "mobilenet_v1_0.75_192":
              A_						=      torch.tensor([-3.9_440, -2.3_141, -0.3_333]				)
       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__":
   __lowerCamelCase		       =    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		       =    parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )



'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):



    def __init__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      None:





           '''simple docstring'''
           warnings.warn(
               """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
               """ use BeitImageProcessor instead."""		,				UpperCamelCase__		,				)
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
