



"""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()
__snake_case				:  Any =       logging.get_logger(__name__)


def      _lowercase							(    __snake_case      )		->  str:
			__lowerCAmelCase :    Union[str, Any]				=			"huggingface/label-files"
			__lowerCAmelCase :    Union[str, Any]				=			"imagenet-1k-id2label.json"
			__lowerCAmelCase :    str				=			json.load(open(hf_hub_download(__snake_case   ,__snake_case   ,repo_type="dataset"      )   ,"r"      )      )
			__lowerCAmelCase :    Dict				=			{int(__snake_case      ): v for k, v in idalabel.items()}
			__lowerCAmelCase :    List[Any]				=			{v: k for k, v in idalabel.items()}

			__lowerCAmelCase :    int				=			"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"
			__lowerCAmelCase :    Optional[int]				=			BitConfig(
			    conv_layer=__snake_case   ,num_labels=1_000   ,idalabel=__snake_case   ,labelaid=__snake_case   ,)

			return config


def      _lowercase							(    __snake_case      )		->  List[str]:
			if "stem.conv" in name:
						__lowerCAmelCase :    Any				=			name.replace("stem.conv"   ,"bit.embedder.convolution"      )
			if "blocks" in name:
						__lowerCAmelCase :    Optional[Any]				=			name.replace("blocks"   ,"layers"      )
			if "head.fc" in name:
						__lowerCAmelCase :    Union[str, Any]				=			name.replace("head.fc"   ,"classifier.1"      )
			if name.startswith("norm"      ):
						__lowerCAmelCase :    Optional[Any]				=			"bit." + name
			if "bit" not in name and "classifier" not in name:
						__lowerCAmelCase :    Any				=			"bit.encoder." + name

			return name


def      _lowercase							(    )		->  List[str]:
			__lowerCAmelCase :    str				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase :    Tuple				=			Image.open(requests.get(__snake_case   ,stream=__snake_case      ).raw      )
			return im


@torch.no_grad()
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case=False      )		->  Optional[Any]:
			__lowerCAmelCase :    Union[str, Any]				=			get_config(__snake_case      )

			# load original model from timm
			__lowerCAmelCase :    int				=			create_model(__snake_case   ,pretrained=__snake_case      )
			timm_model.eval()

			# load state_dict of original model
			__lowerCAmelCase :    List[Any]				=			timm_model.state_dict()
			for key in state_dict.copy().keys():
						__lowerCAmelCase :    str				=			state_dict.pop(__snake_case      )
						__lowerCAmelCase :    Tuple				=			val.squeeze() if "head" in key else val

			# load HuggingFace model
			__lowerCAmelCase :    List[Any]				=			BitForImageClassification(__snake_case      )
			model.eval()
			model.load_state_dict(__snake_case      )

			# create image processor
			__lowerCAmelCase :    Tuple				=			create_transform(**resolve_data_config({}   ,model=__snake_case      )      )
			__lowerCAmelCase :    List[Any]				=			transform.transforms

			__lowerCAmelCase :    Optional[Any]				=			{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

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

			__lowerCAmelCase :    Tuple				=			prepare_img()
			__lowerCAmelCase :    List[str]				=			transform(__snake_case      ).unsqueeze(0      )
			__lowerCAmelCase :    Any				=			processor(__snake_case   ,return_tensors="pt"      ).pixel_values

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

			# verify logits
			with torch.no_grad():
						__lowerCAmelCase :    Union[str, Any]				=			model(__snake_case      )
						__lowerCAmelCase :    int				=			outputs.logits

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

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

	__snake_case				:  List[Any] =       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 (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:  int =       {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       ['PLBartTokenizer']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  List[str] =       [
	    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'PLBartForCausalLM',
	    'PLBartForConditionalGeneration',
	    'PLBartForSequenceClassification',
	    'PLBartModel',
	    'PLBartPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	__snake_case				:  Any =       _LazyModule(__name__, globals()['__file__'], _import_structure)