





_lowerCamelCase   				=			[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      original_name.split("""."""    )[0]
		SCREAMING_SNAKE_CASE__			=      key.split("""."""    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 2]    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 1]    )
		SCREAMING_SNAKE_CASE__			=      orig_block_num - offset

		SCREAMING_SNAKE_CASE__			=      key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''					,   f'''block.{new_block_num}.{layer_num}.{new_name}'''    )
		return key






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      OrderedDict()
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      0, 0
		for key, value in state_dict.items():
				if key.startswith("""network"""    ):
						SCREAMING_SNAKE_CASE__			=      key.replace("""network"""					,   """poolformer.encoder"""    )
				if "proj" in key:
						# Works for the first embedding as well as the internal embedding layers
						if key.endswith("""bias"""    ) and "patch_embed" not in key:
								patch_emb_offset += 1
						SCREAMING_SNAKE_CASE__			=      key[: key.find("""proj"""    )]
						SCREAMING_SNAKE_CASE__			=      key.replace(UpperCamelCase__					,   f'''patch_embeddings.{total_embed_found}.'''    )
						SCREAMING_SNAKE_CASE__			=      key.replace("""proj"""					,   """projection"""    )
						if key.endswith("""bias"""    ):
								total_embed_found += 1
				if "patch_embeddings" in key:
						SCREAMING_SNAKE_CASE__			=      """poolformer.encoder.""" + key
				if "mlp.fc1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc1"""					,   """output.conv1"""    )
				if "mlp.fc2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc2"""					,   """output.conv2"""    )
				if "norm1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm1"""					,   """before_norm"""    )
				if "norm2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm2"""					,   """after_norm"""    )
				if "layer_scale_1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_1"""					,   """layer_scale_1"""    )
				if "layer_scale_2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_2"""					,   """layer_scale_2"""    )
				if "head" in key:
						SCREAMING_SNAKE_CASE__			=      key.replace("""head"""					,   """classifier"""    )
				SCREAMING_SNAKE_CASE__			=      value
		return new_state_dict






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__			=      Image.open(requests.get(UpperCamelCase__					,   stream=UpperCamelCase__    ).raw    )

		return image






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      PoolFormerConfig()

		# set attributes based on model_name
		SCREAMING_SNAKE_CASE__			=      """huggingface/label-files"""
		SCREAMING_SNAKE_CASE__			=      model_name[-3:]
		SCREAMING_SNAKE_CASE__			=      1_000
		SCREAMING_SNAKE_CASE__			=      """imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE__			=      (1, 1_000)

		# set config attributes
		SCREAMING_SNAKE_CASE__			=      json.load(open(hf_hub_download(UpperCamelCase__					,   UpperCamelCase__					,   repo_type="""dataset"""    )					,   """r"""    )    )
		SCREAMING_SNAKE_CASE__			=      {int(UpperCamelCase__    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__			=      idalabel
		SCREAMING_SNAKE_CASE__			=      {v: k for k, v in idalabel.items()}
		if size == "s12":
				SCREAMING_SNAKE_CASE__			=      [2, 2, 6, 2]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s24":
				SCREAMING_SNAKE_CASE__			=      [4, 4, 12, 4]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s36":
				SCREAMING_SNAKE_CASE__			=      [6, 6, 18, 6]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "m36":
				SCREAMING_SNAKE_CASE__			=      [6, 6, 18, 6]
				SCREAMING_SNAKE_CASE__			=      [96, 192, 384, 768]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9_5
		elif size == "m48":
				SCREAMING_SNAKE_CASE__			=      [8, 8, 24, 8]
				SCREAMING_SNAKE_CASE__			=      [96, 192, 384, 768]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9_5
		else:
				raise ValueError(f'''Size {size} not supported'''    )

		# load image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )

		# Prepare image
		SCREAMING_SNAKE_CASE__			=      prepare_img()
		SCREAMING_SNAKE_CASE__			=      image_processor(images=UpperCamelCase__					,   return_tensors="""pt"""    ).pixel_values

		logger.info(f'''Converting model {model_name}...'''    )

		# load original state dict
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__					,   map_location=torch.device("""cpu"""    )    )

		# rename keys
		SCREAMING_SNAKE_CASE__			=      rename_keys(UpperCamelCase__    )

		# create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE__			=      PoolFormerForImageClassification(UpperCamelCase__    )
		model.load_state_dict(UpperCamelCase__    )
		model.eval()

		# Define image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      image_processor(images=prepare_img()					,   return_tensors="""pt"""    ).pixel_values

		# forward pass
		SCREAMING_SNAKE_CASE__			=      model(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]    )
		elif size == "s24":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]    )
		elif size == "s36":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]    )
		elif size == "m36":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]    )
		elif size == "m48":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]    )
		else:
				raise ValueError(f'''Size {size} not supported'''    )

		# verify logits
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3]					,   UpperCamelCase__					,   atol=1e-2    )

		# finally, save model and image processor
		logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''    )
		Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
		model.save_pretrained(UpperCamelCase__    )
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
		image_processor.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()

	parser.add_argument(
	    '--model_name',
	    default='poolformer_s12',
	    type=str,
	    help='Name of the model you\'d like to convert.',
	)
	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)