

"""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 	lowercase      (   __UpperCamelCase						) ->      List[Any]:
 __magic_name__     =      MobileNetVaConfig(layer_norm_eps=0.001						)

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

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

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

 return config





def 	lowercase      (   ) ->      Dict:
 __magic_name__     =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __magic_name__     =      Image.open(requests.get(__UpperCamelCase      ,   stream=__UpperCamelCase						).raw						)
 return im





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=False						) ->      Tuple:
 __magic_name__     =      get_mobilenet_va_config(__UpperCamelCase						)

 # Load 🤗 model
 __magic_name__     =      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
 __magic_name__     =      MobileNetVaImageProcessor(
     crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,   size={'''shortest_edge''': config.image_size + 32}      ,   )
 __magic_name__     =      image_processor(images=prepare_img()      ,   return_tensors='''pt'''						)
 __magic_name__     =      model(**__UpperCamelCase						)
 __magic_name__     =      outputs.logits

 assert logits.shape == (1, 1001)

 if model_name == "mobilenet_v1_1.0_224":
  __magic_name__     =      torch.tensor([-4.1_739, -1.1_233, 3.1_205]						)
 elif model_name == "mobilenet_v1_0.75_192":
  __magic_name__     =      torch.tensor([-3.9_440, -2.3_141, -0.3_333]						)
 else:
  __magic_name__     =      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...'''						)
  __magic_name__     =      '''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 pytest

import datasets


# Import fixture modules as plugins
__lowerCamelCase										=		["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
 for item in items:
  if any(marker in item.keywords for marker in ['''integration''', '''unit''']						):
   continue
  item.add_marker(pytest.mark.unit						)





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 config.addinivalue_line('''markers'''      ,   '''torchaudio_latest: mark test to run with torchaudio>=0.12'''						)





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Dict:
 # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
 __magic_name__     =      tmp_path_factory.getbasetemp() / '''cache'''
 __magic_name__     =      test_hf_cache_home / '''datasets'''
 __magic_name__     =      test_hf_cache_home / '''metrics'''
 __magic_name__     =      test_hf_cache_home / '''modules'''
 monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads'''
 monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads''' / '''extracted'''
 monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)





@pytest.fixture(autouse=__UpperCamelCase      ,   scope='''session'''						)
def 	lowercase      (   ) ->      Any:
 datasets.disable_progress_bar()





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase						) ->      str:
 # don't take tests into account when counting downloads
 monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''      ,   __UpperCamelCase						)





@pytest.fixture
def 	lowercase      (   __UpperCamelCase						) ->      int:
 # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
 # To be removed once SQLAlchemy 2.0 supported
 monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''      ,   __UpperCamelCase						)

