





import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_lowerCamelCase   				=			open  # noqa: we just need to have a builtin inside this module to test it properly






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)