


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()
_UpperCAmelCase			:  Optional[Any]     =logging.get_logger(__name__)



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Any:
				lowerCAmelCase_ :      int    				=				original_name.split('''.''' )[0]
				lowerCAmelCase_ :      Optional[Any]    				=				key.split('''.''' )
				lowerCAmelCase_ :      Dict    				=				int(key_list[key_list.index(lowerCAmelCase_ ) - 2] )
				lowerCAmelCase_ :      List[str]    				=				int(key_list[key_list.index(lowerCAmelCase_ ) - 1] )
				lowerCAmelCase_ :      List[Any]    				=				orig_block_num - offset

				lowerCAmelCase_ :      List[Any]    				=				key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" ,   f"""block.{new_block_num}.{layer_num}.{new_name}""" )
				return key



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



def 			lowerCAmelCase (		)-> Union[str, Any]:
				lowerCAmelCase_ :      List[Any]    				=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowerCAmelCase_ :      List[str]    				=				Image.open(requests.get(lowerCAmelCase_ ,   stream=lowerCAmelCase_ ).raw )

				return image



@torch.no_grad()
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Tuple:
				lowerCAmelCase_ :      Optional[int]    				=				PoolFormerConfig()

				# set attributes based on model_name
				lowerCAmelCase_ :      Any    				=				'''huggingface/label-files'''
				lowerCAmelCase_ :      int    				=				model_name[-3:]
				lowerCAmelCase_ :      Optional[Any]    				=				1_000
				lowerCAmelCase_ :      Union[str, Any]    				=				'''imagenet-1k-id2label.json'''
				lowerCAmelCase_ :      Optional[int]    				=				(1, 1_000)

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

				# load image processor
				lowerCAmelCase_ :      Dict    				=				PoolFormerImageProcessor(crop_pct=lowerCAmelCase_ )

				# Prepare image
				lowerCAmelCase_ :      str    				=				prepare_img()
				lowerCAmelCase_ :      Optional[int]    				=				image_processor(images=lowerCAmelCase_ ,   return_tensors='''pt''' ).pixel_values

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

				# load original state dict
				lowerCAmelCase_ :      Dict    				=				torch.load(lowerCAmelCase_ ,   map_location=torch.device('''cpu''' ) )

				# rename keys
				lowerCAmelCase_ :      Any    				=				rename_keys(lowerCAmelCase_ )

				# create HuggingFace model and load state dict
				lowerCAmelCase_ :      Any    				=				PoolFormerForImageClassification(lowerCAmelCase_ )
				model.load_state_dict(lowerCAmelCase_ )
				model.eval()

				# Define image processor
				lowerCAmelCase_ :      Dict    				=				PoolFormerImageProcessor(crop_pct=lowerCAmelCase_ )
				lowerCAmelCase_ :      Dict    				=				image_processor(images=prepare_img() ,   return_tensors='''pt''' ).pixel_values

				# forward pass
				lowerCAmelCase_ :      List[str]    				=				model(lowerCAmelCase_ )
				lowerCAmelCase_ :      int    				=				outputs.logits

				# define expected logit slices for different models
				if size == "s12":
								lowerCAmelCase_ :      List[Any]    				=				torch.tensor([-0.3045, -0.6758, -0.4869] )
				elif size == "s24":
								lowerCAmelCase_ :      str    				=				torch.tensor([0.4402, -0.1374, -0.8045] )
				elif size == "s36":
								lowerCAmelCase_ :      str    				=				torch.tensor([-0.6080, -0.5133, -0.5898] )
				elif size == "m36":
								lowerCAmelCase_ :      Dict    				=				torch.tensor([0.3952, 0.2263, -1.2668] )
				elif size == "m48":
								lowerCAmelCase_ :      Tuple    				=				torch.tensor([0.1167, -0.0656, -0.3423] )
				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__":
						_UpperCAmelCase			:  int     =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."""
						)
						_UpperCAmelCase			:  Optional[Any]     =parser.parse_args()
						convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase			:  Tuple     =10



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				for i in range(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								if array[i] == target:
												return i
				return -1



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				0
				lowerCAmelCase_ :      int    				=				len(lowerCAmelCase_ )
				while left <= right:
								if right - left < precision:
												return lin_search(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )

								lowerCAmelCase_ :      List[Any]    				=				(left + right) // 3 + 1
								lowerCAmelCase_ :      List[str]    				=				2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												lowerCAmelCase_ :      Dict    				=				one_third - 1
								elif array[two_third] < target:
												lowerCAmelCase_ :      List[Any]    				=				two_third + 1

								else:
												lowerCAmelCase_ :      Union[str, Any]    				=				one_third + 1
												lowerCAmelCase_ :      Tuple    				=				two_third - 1
				else:
								return -1



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				if left < right:
								if right - left < precision:
												return lin_search(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								lowerCAmelCase_ :      Union[str, Any]    				=				(left + right) // 3 + 1
								lowerCAmelCase_ :      Optional[int]    				=				2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												return rec_ternary_search(lowerCAmelCase_ ,   one_third - 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								elif array[two_third] < target:
												return rec_ternary_search(two_third + 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								else:
												return rec_ternary_search(one_third + 1 ,   two_third - 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
				else:
								return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCAmelCase			:  Tuple     =input("""Enter numbers separated by comma:\n""").strip()
						_UpperCAmelCase			:  Union[str, Any]     =[int(item.strip()) for item in user_input.split(""",""")]
						assert collection == sorted(collection), f"List must be ordered.\n{collection}."
						_UpperCAmelCase			:  int     =int(input("""Enter the number to be found in the list:\n""").strip())
						_UpperCAmelCase			:  Optional[Any]     =ite_ternary_search(collection, target)
						_UpperCAmelCase			:  List[str]     =rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(f"""Iterative search: {target} found at positions: {resulta}""")
												print(f"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("""Not found""")