





"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__     			=			logging.get_logger(__name__)


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	"huggingface/label-files"
		UpperCamelCase   =	"imagenet-1k-id2label.json"
		UpperCamelCase   =	json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"    )  ,	"r"    )    )
		UpperCamelCase   =	{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
		UpperCamelCase   =	{v: k for k, v in idalabel.items()}

		UpperCamelCase   =	"std_conv" if "bit" in model_name else False

		# note that when using BiT as backbone for ViT-hybrid checkpoints,
		# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
		# config.conv_layer = "std_conv_same"
		UpperCamelCase   =	BitConfig(
		    conv_layer=_SCREAMING_SNAKE_CASE  ,	num_labels=1_000  ,	idalabel=_SCREAMING_SNAKE_CASE  ,	labelaid=_SCREAMING_SNAKE_CASE  ,	)

		return config


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if "stem.conv" in name:
				UpperCamelCase   =	name.replace("stem.conv"  ,	"bit.embedder.convolution"    )
		if "blocks" in name:
				UpperCamelCase   =	name.replace("blocks"  ,	"layers"    )
		if "head.fc" in name:
				UpperCamelCase   =	name.replace("head.fc"  ,	"classifier.1"    )
		if name.startswith("norm"    ):
				UpperCamelCase   =	"bit." + name
		if "bit" not in name and "classifier" not in name:
				UpperCamelCase   =	"bit.encoder." + name

		return name


def       a__					(					):




		"""simple docstring"""


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


@torch.no_grad()
def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False    ):




		"""simple docstring"""


		UpperCamelCase   =	get_config(_SCREAMING_SNAKE_CASE    )

		# load original model from timm
		UpperCamelCase   =	create_model(_SCREAMING_SNAKE_CASE  ,	pretrained=_SCREAMING_SNAKE_CASE    )
		timm_model.eval()

		# load state_dict of original model
		UpperCamelCase   =	timm_model.state_dict()
		for key in state_dict.copy().keys():
				UpperCamelCase   =	state_dict.pop(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	val.squeeze() if "head" in key else val

		# load HuggingFace model
		UpperCamelCase   =	BitForImageClassification(_SCREAMING_SNAKE_CASE    )
		model.eval()
		model.load_state_dict(_SCREAMING_SNAKE_CASE    )

		# create image processor
		UpperCamelCase   =	create_transform(**resolve_data_config({}  ,	model=_SCREAMING_SNAKE_CASE    )    )
		UpperCamelCase   =	transform.transforms

		UpperCamelCase   =	{
		    "bilinear": PILImageResampling.BILINEAR,
		    "bicubic": PILImageResampling.BICUBIC,
		    "nearest": PILImageResampling.NEAREST,
		}

		UpperCamelCase   =	BitImageProcessor(
		    do_resize=_SCREAMING_SNAKE_CASE  ,	size={"shortest_edge": timm_transforms[0].size}  ,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,	do_center_crop=_SCREAMING_SNAKE_CASE  ,	crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}  ,	do_normalize=_SCREAMING_SNAKE_CASE  ,	image_mean=timm_transforms[-1].mean.tolist()  ,	image_std=timm_transforms[-1].std.tolist()  ,	)

		UpperCamelCase   =	prepare_img()
		UpperCamelCase   =	transform(_SCREAMING_SNAKE_CASE    ).unsqueeze(0    )
		UpperCamelCase   =	processor(_SCREAMING_SNAKE_CASE  ,	return_tensors="pt"    ).pixel_values

		# verify pixel values
		assert torch.allclose(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		# verify logits
		with torch.no_grad():
				UpperCamelCase   =	model(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	outputs.logits

		print("Logits:"  ,	logits[0, :3]    )
		print("Predicted class:"  ,	model.config.idalabel[logits.argmax(-1    ).item()]    )
		UpperCamelCase   =	timm_model(_SCREAMING_SNAKE_CASE    )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_SCREAMING_SNAKE_CASE  ,	outputs.logits  ,	atol=1e-3    )
		print("Looks ok!"    )

		if pytorch_dump_folder_path is not None:
				Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
				print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"    )
				model.save_pretrained(_SCREAMING_SNAKE_CASE    )
				processor.save_pretrained(_SCREAMING_SNAKE_CASE    )

		if push_to_hub:
				print(F"Pushing model {model_name} and processor to the hub"    )
				model.push_to_hub(F"ybelkada/{model_name}"    )
				processor.push_to_hub(F"ybelkada/{model_name}"    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default='''resnetv2_50x1_bitm''',
						    type=str,
						    help='''Name of the BiT timm model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						    help='''Whether to push the model to the hub.''',
						)

						lowerCAmelCase__     			=			parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase__     			=			{'''tokenization_bertweet''': ['''BertweetTokenizer''']}


if TYPE_CHECKING:
						from .tokenization_bertweet import BertweetTokenizer

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






