
'''simple docstring'''


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_   :   Union[str, Any]       =					logging.get_logger(__name__)







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Union[str, Any]     )   ->    Dict:
			a__     = original_name.split("."     )[0]
			a__     = key.split("."     )
			a__     = int(key_list[key_list.index(__lowerCamelCase     ) - 2]     )
			a__     = int(key_list[key_list.index(__lowerCamelCase     ) - 1]     )
			a__     = orig_block_num - offset

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







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[str]:
			a__     = OrderedDict()
			a__   ,				a__     = 0, 0
			for key, value in state_dict.items():
						if key.startswith("network"     ):
									a__     = 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
									a__     = key[: key.find("proj"     )]
									a__     = key.replace(__lowerCamelCase					,  f'''patch_embeddings.{total_embed_found}.'''     )
									a__     = key.replace("proj"					,  "projection"     )
									if key.endswith("bias"     ):
												total_embed_found += 1
						if "patch_embeddings" in key:
									a__     = "poolformer.encoder." + key
						if "mlp.fc1" in key:
									a__     = replace_key_with_offset(__lowerCamelCase					,  __lowerCamelCase					,  "mlp.fc1"					,  "output.conv1"     )
						if "mlp.fc2" in key:
									a__     = replace_key_with_offset(__lowerCamelCase					,  __lowerCamelCase					,  "mlp.fc2"					,  "output.conv2"     )
						if "norm1" in key:
									a__     = replace_key_with_offset(__lowerCamelCase					,  __lowerCamelCase					,  "norm1"					,  "before_norm"     )
						if "norm2" in key:
									a__     = replace_key_with_offset(__lowerCamelCase					,  __lowerCamelCase					,  "norm2"					,  "after_norm"     )
						if "layer_scale_1" in key:
									a__     = replace_key_with_offset(__lowerCamelCase					,  __lowerCamelCase					,  "layer_scale_1"					,  "layer_scale_1"     )
						if "layer_scale_2" in key:
									a__     = replace_key_with_offset(__lowerCamelCase					,  __lowerCamelCase					,  "layer_scale_2"					,  "layer_scale_2"     )
						if "head" in key:
									a__     = key.replace("head"					,  "classifier"     )
						a__     = value
			return new_state_dict







def    _lowerCamelCase  ()   ->    List[str]:
			a__     = "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__     = Image.open(requests.get(__lowerCamelCase					,  stream=__lowerCamelCase     ).raw     )

			return image







@torch.no_grad()
def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Any     )   ->    Optional[Any]:
			a__     = PoolFormerConfig()

			# set attributes based on model_name
			a__     = "huggingface/label-files"
			a__     = model_name[-3:]
			a__     = 1000
			a__     = "imagenet-1k-id2label.json"
			a__     = (1, 1000)

			# set config attributes
			a__     = json.load(open(hf_hub_download(__lowerCamelCase					,  __lowerCamelCase					,  repo_type="dataset"     )					,  "r"     )     )
			a__     = {int(__lowerCamelCase     ): v for k, v in idalabel.items()}
			a__     = idalabel
			a__     = {v: k for k, v in idalabel.items()}
			if size == "s12":
						a__     = [2, 2, 6, 2]
						a__     = [64, 128, 320, 512]
						a__     = 4.0
						a__     = 0.9
			elif size == "s24":
						a__     = [4, 4, 12, 4]
						a__     = [64, 128, 320, 512]
						a__     = 4.0
						a__     = 0.9
			elif size == "s36":
						a__     = [6, 6, 18, 6]
						a__     = [64, 128, 320, 512]
						a__     = 4.0
						a__     = 1e-6
						a__     = 0.9
			elif size == "m36":
						a__     = [6, 6, 18, 6]
						a__     = [96, 192, 384, 768]
						a__     = 4.0
						a__     = 1e-6
						a__     = 0.95
			elif size == "m48":
						a__     = [8, 8, 24, 8]
						a__     = [96, 192, 384, 768]
						a__     = 4.0
						a__     = 1e-6
						a__     = 0.95
			else:
						raise ValueError(f'''Size {size} not supported'''     )

			# load image processor
			a__     = PoolFormerImageProcessor(crop_pct=__lowerCamelCase     )

			# Prepare image
			a__     = prepare_img()
			a__     = image_processor(images=__lowerCamelCase					,  return_tensors="pt"     ).pixel_values

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

			# load original state dict
			a__     = torch.load(__lowerCamelCase					,  map_location=torch.device("cpu"     )     )

			# rename keys
			a__     = rename_keys(__lowerCamelCase     )

			# create HuggingFace model and load state dict
			a__     = PoolFormerForImageClassification(__lowerCamelCase     )
			model.load_state_dict(__lowerCamelCase     )
			model.eval()

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

			# forward pass
			a__     = model(__lowerCamelCase     )
			a__     = outputs.logits

			# define expected logit slices for different models
			if size == "s12":
						a__     = torch.tensor([-0.3_045, -0.6_758, -0.4_869]     )
			elif size == "s24":
						a__     = torch.tensor([0.4_402, -0.1_374, -0.8_045]     )
			elif size == "s36":
						a__     = torch.tensor([-0.6_080, -0.5_133, -0.5_898]     )
			elif size == "m36":
						a__     = torch.tensor([0.3_952, 0.2_263, -1.2_668]     )
			elif size == "m48":
						a__     = torch.tensor([0.1_167, -0.0_656, -0.3_423]     )
			else:
						raise ValueError(f'''Size {size} not supported'''     )

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

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


if __name__ == "__main__":
		lowerCAmelCase_   :   Any       =					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_   :   str       =					parser.parse_args()
		convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



