





'''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 : str											=			logging.get_logger(__name__)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = MobileNetVaConfig(layer_norm_eps=0.0_01							)

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

				lowercase         = re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"		,							lowerCAmelCase_							)
				if matches:
								lowercase         = float(matches[1]							)
								lowercase         = int(matches[2]							)

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

				return config







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = "http://images.cocodataset.org/val2017/000000039769.jpg"
				lowercase         = Image.open(requests.get(lowerCAmelCase_		,							stream=lowerCAmelCase_							).raw							)
				return im







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=False							):

				"""simple docstring"""






				lowercase         = get_mobilenet_va_config(lowerCAmelCase_							)

				# Load 🤗 model
				lowercase         = MobileNetVaForImageClassification(lowerCAmelCase_							).eval()

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

				# Check outputs on an image, prepared by MobileNetV1ImageProcessor
				lowercase         = MobileNetVaImageProcessor(
				    crop_size={"width": config.image_size, "height": config.image_size}		,							size={"shortest_edge": config.image_size + 32}		,							)
				lowercase         = image_processor(images=prepare_img()		,							return_tensors="pt"							)
				lowercase         = model(**lowerCAmelCase_							)
				lowercase         = outputs.logits

				assert logits.shape == (1, 1001)

				if model_name == "mobilenet_v1_1.0_224":
								lowercase         = torch.tensor([-4.17_39, -1.12_33, 3.12_05]							)
				elif model_name == "mobilenet_v1_0.75_192":
								lowercase         = torch.tensor([-3.94_40, -2.31_41, -0.33_33]							)
				else:
								lowercase         = None

				if expected_logits is not None:
								assert torch.allclose(logits[0, :3]		,							lowerCAmelCase_		,							atol=1E-4							)

				Path(lowerCAmelCase_							).mkdir(exist_ok=lowerCAmelCase_							)
				print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
				model.save_pretrained(lowerCAmelCase_							)
				print(f'Saving image processor to {pytorch_dump_folder_path}'							)
				image_processor.save_pretrained(lowerCAmelCase_							)

				if push_to_hub:
								print("Pushing to the hub..."							)
								lowercase         = "google/" + model_name
								image_processor.push_to_hub(lowerCAmelCase_							)
								model.push_to_hub(lowerCAmelCase_							)


if __name__ == "__main__":
			__lowerCamelCase : Optional[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."
			)

			__lowerCamelCase : Tuple											=			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 : Optional[Any]											=			logging.get_logger(__name__)

__lowerCamelCase : Optional[Any]											=			{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		'''vit_mae'''




		def __init__(self      :	Dict							,			A__      :	Tuple=7_6_8							,			A__      :	str=1_2							,			A__      :	Dict=1_2							,			A__      :	Optional[Any]=3_0_7_2							,			A__      :	Union[str, Any]="gelu"							,			A__      :	Optional[int]=0.0							,			A__      :	str=0.0							,			A__      :	Optional[Any]=0.0_2							,			A__      :	Optional[Any]=1e-12							,			A__      :	Tuple=2_2_4							,			A__      :	str=1_6							,			A__      :	Optional[int]=3							,			A__      :	List[Any]=True							,			A__      :	Dict=1_6							,			A__      :	Dict=5_1_2							,			A__      :	Dict=8							,			A__      :	Union[str, Any]=2_0_4_8							,			A__      :	str=0.7_5							,			A__      :	str=False							,			**A__      :	Any							,			)					-> Union[str, Any]:
						super().__init__(**A__				)

						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = image_size
						lowercase         = patch_size
						lowercase         = num_channels
						lowercase         = qkv_bias
						lowercase         = decoder_num_attention_heads
						lowercase         = decoder_hidden_size
						lowercase         = decoder_num_hidden_layers
						lowercase         = decoder_intermediate_size
						lowercase         = mask_ratio
						lowercase         = norm_pix_loss




