





'''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()
__SCREAMING_SNAKE_CASE     :Any       =				logging.get_logger(__name__)
def 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]       )		->       Any:


     '''simple docstring'''



     _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_([^_]*)_([^_]*)$"			,     __lowercase       )
     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     							=   1001
     _UpperCAmelCase     							=   "imagenet-1k-id2label.json"
     _UpperCAmelCase     							=   "huggingface/label-files"
     _UpperCAmelCase     							=   json.load(open(hf_hub_download(__lowercase			,     __lowercase			,     repo_type="dataset"       )			,     "r"       )       )
     _UpperCAmelCase     							=   {int(__lowercase       ) + 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_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   "http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase     							=   Image.open(requests.get(__lowercase			,     stream=__lowercase       ).raw       )
     return im
@torch.no_grad()
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      Optional[int]			,     __lowercase							:      int			,     __lowercase							:      Optional[Any]=False       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   get_mobilenet_va_config(__lowercase       )

     # Load 🤗 model
     _UpperCAmelCase     							=   MobileNetVaForImageClassification(__lowercase       ).eval()

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

     # 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(**__lowercase       )
     _UpperCAmelCase     							=   outputs.logits

     assert logits.shape == (1, 1001)

     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]			,     __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..."       )
          _UpperCAmelCase     							=   "google/" + model_name
          image_processor.push_to_hub(__lowercase       )
          model.push_to_hub(__lowercase       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :Dict       =				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.'''
  )

  __SCREAMING_SNAKE_CASE     :List[str]       =				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


__a          =		logging.get_logger(__name__)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def __init__(    self					:     List[str] ,     *snake_case_					:     str ,     **snake_case_					:     List[str]						):
          warnings.warn(
              """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
              """ use BeitImageProcessor instead.""" ,     snake_case_ ,     )
          super().__init__(*snake_case_ ,     **snake_case_						)
