

"""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()
_lowercase											=			logging.get_logger(__name__)






def  _snake_case			(						snake_case__				:       Optional[int]       ):
	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_([^_]*)_([^_]*)$'		, snake_case__       )
	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  				=				1001
		A  				=				'imagenet-1k-id2label.json'

	A  				=				'huggingface/label-files'
	A  				=				json.load(open(hf_hub_download(snake_case__		, snake_case__		, repo_type='dataset'       )		, 'r'       )       )

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

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

	return config






def  _snake_case			(						):
	A  				=				'http://images.cocodataset.org/val2017/000000039769.jpg'
	A  				=				Image.open(requests.get(snake_case__		, stream=snake_case__       ).raw       )
	return im






@torch.no_grad()
def  _snake_case			(						snake_case__				:       int		, snake_case__				:       List[Any]		, snake_case__				:       Dict		, snake_case__				:       List[str]=False       ):
	A  				=				get_mobilenet_va_config(snake_case__       )

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

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

	# 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(**snake_case__       )
	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(
			    [
			        [[17.5790, 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]		, snake_case__		, atol=1e-4       )
	else:
		assert logits.shape == (1, 1001)

		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]		, snake_case__		, atol=1e-4       )

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

	if push_to_hub:
		print('Pushing to the hub...'       )
		A  				=				'google/' + model_name
		image_processor.push_to_hub(snake_case__       )
		model.push_to_hub(snake_case__       )


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

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

"""simple docstring"""


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