


"""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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_     :     Optional[int]    		=						logging.get_logger(__name__)
def _A		(		lowercase	):







    """simple docstring"""
    a					=MobileNetVaConfig(layer_norm_eps=0.001	)

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

    a					=re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$''' ,					_UpperCamelCase	)
    if matches:
        a					=float(matches[1]	)
        a					=int(matches[2]	)

    if model_name.startswith('''deeplabv3_'''	):
        a					=8
        a					=21
        a					='''pascal-voc-id2label.json'''
    else:
        # The TensorFlow version of MobileNetV2 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'''	)	)

    if config.num_labels == 10_01:
        a					={int(_UpperCamelCase	) + 1: v for k, v in idalabel.items()}
        a					='''background'''
    else:
        a					={int(_UpperCamelCase	): v for k, v in idalabel.items()}

    a					=idalabel
    a					={v: k for k, v in idalabel.items()}

    return config
def _A		(		):







    """simple docstring"""
    a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    a					=Image.open(requests.get(_UpperCamelCase ,					stream=_UpperCamelCase	).raw	)
    return im
@torch.no_grad()
def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase=False	):







    """simple docstring"""
    a					=get_mobilenet_va_config(_UpperCamelCase	)

    # Load 🤗 model
    if model_name.startswith('''deeplabv3_'''	):
        a					=MobileNetVaForSemanticSegmentation(_UpperCamelCase	).eval()
    else:
        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 MobileNetV2ImageProcessor
    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

    if model_name.startswith('''deeplabv3_'''	):
        assert logits.shape == (1, 21, 65, 65)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            a					=torch.tensor(
                [
                    [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]	)

        else:
            raise ValueError(f'''Unknown model name: {model_name}'''	)

        assert torch.allclose(logits[0, :3, :3, :3] ,					_UpperCamelCase ,					atol=1E-4	)
    else:
        assert logits.shape == (1, 10_01)

        if model_name == "mobilenet_v2_1.4_224":
            a					=torch.tensor([0.0181, -1.0015, 0.4688]	)
        elif model_name == "mobilenet_v2_1.0_224":
            a					=torch.tensor([0.2445, -1.1993, 0.1905]	)
        elif model_name == "mobilenet_v2_0.75_160":
            a					=torch.tensor([0.2482, 0.4136, 0.6669]	)
        elif model_name == "mobilenet_v2_0.35_96":
            a					=torch.tensor([0.1451, -0.4624, 0.7192]	)
        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_     :     Dict    		=						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""mobilenet_v2_1.0_224""",
           type=str,
           help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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_     :     Union[str, 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Optional[Any]    		=						{
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"vilt"


 def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
     super().__init__(tie_word_embeddings=__A				,					**__A		)

     a					=vocab_size
     a					=type_vocab_size
     a					=modality_type_vocab_size
     a					=max_position_embeddings

     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=initializer_range
     a					=layer_norm_eps

     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=qkv_bias
     a					=max_image_length
     a					=num_images