



'''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()
A__    :   Tuple           =					logging.get_logger(__name__)





def   a_   (		_UpperCAmelCase    :      Optional[int]				)    ->  Any:
      __snake_case :   Any													=    MobileNetVaConfig(layer_norm_eps=0.0_0_1				)

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

      __snake_case :   List[str]													=    re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$' ,_UpperCAmelCase				)
      if matches:
            __snake_case :   Dict													=    float(matches[1]				)
            __snake_case :   str													=    int(matches[2]				)

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

      return config





def   a_   (		)    ->  Any:
      __snake_case :   List[str]													=    'http://images.cocodataset.org/val2017/000000039769.jpg'
      __snake_case :   int													=    Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase				).raw				)
      return im





@torch.no_grad()
def   a_   (		_UpperCAmelCase    :      Dict ,_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      List[str]=False				)    ->  List[Any]:
      __snake_case :   List[Any]													=    get_mobilenet_va_config(_UpperCAmelCase				)

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

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

      if model_name == "mobilenet_v1_1.0_224":
            __snake_case :   str													=    torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]				)
      elif model_name == "mobilenet_v1_0.75_192":
            __snake_case :   int													=    torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]				)
      else:
            __snake_case :   List[str]													=    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...'				)
            __snake_case :   List[str]													=    'google/' + model_name
            image_processor.push_to_hub(_UpperCAmelCase				)
            model.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
    A__    :   int           =					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.'''
    )

    A__    :   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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[str]           =					{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Optional[Any]           =					[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    A__    :   Optional[int]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



