




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()
UpperCamelCase__  =		logging.get_logger(__name__)






def       snake_case      (							A    )							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:	Any							     =							MobileNetVaConfig(layer_norm_eps=0.001    )

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

	lowerCamelCase_							:	Tuple							     =							re.match(R'^.*mobilenet_v2_([^_]*)_([^_]*)$'		,     A    )
	if matches:
		lowerCamelCase_							:	Any							     =							float(matches[1]    )
		lowerCamelCase_							:	Dict							     =							int(matches[2]    )

	if model_name.startswith('deeplabv3_'    ):
		lowerCamelCase_							:	List[Any]							     =							8
		lowerCamelCase_							:	str							     =							21
		lowerCamelCase_							:	Any							     =							'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".
		lowerCamelCase_							:	str							     =							1001
		lowerCamelCase_							:	Any							     =							'imagenet-1k-id2label.json'

	lowerCamelCase_							:	Tuple							     =							'huggingface/label-files'
	lowerCamelCase_							:	List[str]							     =							json.load(open(hf_hub_download(A		,     A		,     repo_type='dataset'    )		,     'r'    )    )

	if config.num_labels == 1001:
		lowerCamelCase_							:	Tuple							     =							{int(A    ) + 1: v for k, v in idalabel.items()}
		lowerCamelCase_							:	str							     =							'background'
	else:
		lowerCamelCase_							:	str							     =							{int(A    ): v for k, v in idalabel.items()}

	lowerCamelCase_							:	List[Any]							     =							idalabel
	lowerCamelCase_							:	Optional[int]							     =							{v: k for k, v in idalabel.items()}

	return config






def       snake_case      (							)							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:	Dict							     =							'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCamelCase_							:	Optional[int]							     =							Image.open(requests.get(A		,     stream=A    ).raw    )
	return im






@torch.no_grad()
def       snake_case      (							A		,     A		,     A		,     A=False    )							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:	List[str]							     =							get_mobilenet_va_config(A    )

	# Load 🤗 model
	if model_name.startswith('deeplabv3_'    ):
		lowerCamelCase_							:	Tuple							     =							MobileNetVaForSemanticSegmentation(A    ).eval()
	else:
		lowerCamelCase_							:	Dict							     =							MobileNetVaForImageClassification(A    ).eval()

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

	# Check outputs on an image, prepared by MobileNetV2ImageProcessor
	lowerCamelCase_							:	List[Any]							     =							MobileNetVaImageProcessor(
	    crop_size={'width': config.image_size, 'height': config.image_size}		,     size={'shortest_edge': config.image_size + 32}		,     )
	lowerCamelCase_							:	Dict							     =							image_processor(images=prepare_img()		,     return_tensors='pt'    )
	lowerCamelCase_							:	Union[str, Any]							     =							model(**A    )
	lowerCamelCase_							:	int							     =							outputs.logits

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

		if model_name == "deeplabv3_mobilenet_v2_1.0_513":
			lowerCamelCase_							:	Dict							     =							torch.tensor(
			    [
			        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
			        [[-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]		,     A		,     atol=1e-4    )
	else:
		assert logits.shape == (1, 1001)

		if model_name == "mobilenet_v2_1.4_224":
			lowerCamelCase_							:	List[Any]							     =							torch.tensor([0.0181, -1.0015, 0.4688]    )
		elif model_name == "mobilenet_v2_1.0_224":
			lowerCamelCase_							:	Union[str, Any]							     =							torch.tensor([0.2445, -1.1993, 0.1905]    )
		elif model_name == "mobilenet_v2_0.75_160":
			lowerCamelCase_							:	List[Any]							     =							torch.tensor([0.2482, 0.4136, 0.6669]    )
		elif model_name == "mobilenet_v2_0.35_96":
			lowerCamelCase_							:	Tuple							     =							torch.tensor([0.1451, -0.4624, 0.7192]    )
		else:
			lowerCamelCase_							:	str							     =							None

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

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

	if push_to_hub:
		print('Pushing to the hub...'    )
		lowerCamelCase_							:	List[str]							     =							'google/' + model_name
		image_processor.push_to_hub(A    )
		model.push_to_hub(A    )


if __name__ == "__main__":
							UpperCamelCase__  =		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.'
							)

							UpperCamelCase__  =		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .image_encoder import PaintByExampleImageEncoder
							from .pipeline_paint_by_example import PaintByExamplePipeline
