






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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Union[str, Any]         =       MobileNetVaConfig(layer_norm_eps=0.001      )

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

							lowerCamelCase__					:	Optional[int]         =       re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""				,			UpperCamelCase      )
							if matches:
														lowerCamelCase__					:	Optional[int]         =       float(matches[1]      )
														lowerCamelCase__					:	Optional[int]         =       int(matches[2]      )

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

							return config





def  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	int         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__					:	List[str]         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )
							return im





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase=False      )      ->       Tuple:
							lowerCamelCase__					:	Union[str, Any]         =       get_mobilenet_va_config(UpperCamelCase      )

							# Load 🤗 model
							lowerCamelCase__					:	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
							lowerCamelCase__					:	Optional[Any]         =       MobileNetVaImageProcessor(
							    crop_size={"""width""": config.image_size, """height""": config.image_size}				,			size={"""shortest_edge""": config.image_size + 32}				,			)
							lowerCamelCase__					:	Any         =       image_processor(images=prepare_img()				,			return_tensors="""pt"""      )
							lowerCamelCase__					:	str         =       model(**UpperCamelCase      )
							lowerCamelCase__					:	Dict         =       outputs.logits

							assert logits.shape == (1, 1001)

							if model_name == "mobilenet_v1_1.0_224":
														lowerCamelCase__					:	Optional[Any]         =       torch.tensor([-4.1739, -1.1233, 3.1205]      )
							elif model_name == "mobilenet_v1_0.75_192":
														lowerCamelCase__					:	Union[str, Any]         =       torch.tensor([-3.9440, -2.3141, -0.3333]      )
							else:
														lowerCamelCase__					:	Tuple         =       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..."""      )
														lowerCamelCase__					:	Optional[Any]         =       """google/""" + model_name
														image_processor.push_to_hub(UpperCamelCase      )
														model.push_to_hub(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Any         =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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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

