




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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[Any]      )			->      Tuple:




				'''simple docstring'''





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

				A__        =			"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"
				A__        =			BitConfig(
				    conv_layer=SCREAMING_SNAKE_CASE_	,       num_labels=1_0_0_0	,       idalabel=SCREAMING_SNAKE_CASE_	,       labelaid=SCREAMING_SNAKE_CASE_	,       )

				return config



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[Any]      )			->      int:




				'''simple docstring'''





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

				return name



def 					lowerCAmelCase__							(	)			->      int:




				'''simple docstring'''





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



@torch.no_grad()
def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]=False      )			->      int:




				'''simple docstring'''





				A__        =			get_config(SCREAMING_SNAKE_CASE_      )

				# load original model from timm
				A__        =			create_model(SCREAMING_SNAKE_CASE_	,       pretrained=SCREAMING_SNAKE_CASE_      )
				timm_model.eval()

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

				# load HuggingFace model
				A__        =			BitForImageClassification(SCREAMING_SNAKE_CASE_      )
				model.eval()
				model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

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

				A__        =			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()	,       )

				A__        =			prepare_img()
				A__        =			transform(SCREAMING_SNAKE_CASE_      ).unsqueeze(0      )
				A__        =			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():
								A__        =			model(SCREAMING_SNAKE_CASE_      )
								A__        =			outputs.logits

				print("Logits:"	,       logits[0, :3]      )
				print("Predicted class:"	,       model.config.idalabel[logits.argmax(-1      ).item()]      )
				A__        =			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)




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(
			    metadata={'help': 'The output directory where the model will be written.'}							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The encoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train an encoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The decoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train a decoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}			)



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





				A__        =			HfArgumentParser((ModelArguments,)      )
				((A__)    ,				)        =			parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

				# Use explicit specified encoder config
				if model_args.encoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_config_name      )
				# Use pretrained encoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path      )

				# Use explicit specified decoder config
				if model_args.decoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_config_name      )
				# Use pretrained decoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path      )

				# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
				A__        =			True
				A__        =			True

				A__        =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
				    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path	,       decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path	,       encoder_config=SCREAMING_SNAKE_CASE_	,       decoder_config=SCREAMING_SNAKE_CASE_	,       )

				# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
				A__        =			decoder_config.decoder_start_token_id
				A__        =			decoder_config.pad_token_id
				if decoder_start_token_id is None:
								A__        =			decoder_config.bos_token_id
				if pad_token_id is None:
								A__        =			decoder_config.eos_token_id

				# This is necessary to make Flax's generate() work
				A__        =			decoder_config.eos_token_id
				A__        =			decoder_start_token_id
				A__        =			pad_token_id

				A__        =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

				A__        =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
				A__        =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id      )

				model.save_pretrained(model_args.output_dir      )
				image_processor.save_pretrained(model_args.output_dir      )
				tokenizer.save_pretrained(model_args.output_dir      )


if __name__ == "__main__":
	main()

