


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()
_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
					__UpperCamelCase   							=original_name.split('.'    )[0]
					__UpperCamelCase   							=key.split('.'    )
					__UpperCamelCase   							=int(key_list[key_list.index(lowercase__    ) - 2]    )
					__UpperCamelCase   							=int(key_list[key_list.index(lowercase__    ) - 1]    )
					__UpperCamelCase   							=orig_block_num - offset

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict    ):
					__UpperCamelCase   							=OrderedDict()
					__UpperCamelCase							,				__UpperCamelCase   							=0, 0
					for key, value in state_dict.items():
										if key.startswith('network'    ):
															__UpperCamelCase   							=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
															__UpperCamelCase   							=key[: key.find('proj'    )]
															__UpperCamelCase   							=key.replace(lowercase__      ,					F'patch_embeddings.{total_embed_found}.'    )
															__UpperCamelCase   							=key.replace('proj'      ,					'projection'    )
															if key.endswith('bias'    ):
																				total_embed_found += 1
										if "patch_embeddings" in key:
															__UpperCamelCase   							='poolformer.encoder.' + key
										if "mlp.fc1" in key:
															__UpperCamelCase   							=replace_key_with_offset(lowercase__      ,					lowercase__      ,					'mlp.fc1'      ,					'output.conv1'    )
										if "mlp.fc2" in key:
															__UpperCamelCase   							=replace_key_with_offset(lowercase__      ,					lowercase__      ,					'mlp.fc2'      ,					'output.conv2'    )
										if "norm1" in key:
															__UpperCamelCase   							=replace_key_with_offset(lowercase__      ,					lowercase__      ,					'norm1'      ,					'before_norm'    )
										if "norm2" in key:
															__UpperCamelCase   							=replace_key_with_offset(lowercase__      ,					lowercase__      ,					'norm2'      ,					'after_norm'    )
										if "layer_scale_1" in key:
															__UpperCamelCase   							=replace_key_with_offset(lowercase__      ,					lowercase__      ,					'layer_scale_1'      ,					'layer_scale_1'    )
										if "layer_scale_2" in key:
															__UpperCamelCase   							=replace_key_with_offset(lowercase__      ,					lowercase__      ,					'layer_scale_2'      ,					'layer_scale_2'    )
										if "head" in key:
															__UpperCamelCase   							=key.replace('head'      ,					'classifier'    )
										__UpperCamelCase   							=value
					return new_state_dict

def 						_UpperCAmelCase    (      ):
					__UpperCamelCase   							='http://images.cocodataset.org/val2017/000000039769.jpg'
					__UpperCamelCase   							=Image.open(requests.get(lowercase__      ,					stream=lowercase__    ).raw    )

					return image

@torch.no_grad()
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
					__UpperCamelCase   							=PoolFormerConfig()

					# set attributes based on model_name
					__UpperCamelCase   							='huggingface/label-files'
					__UpperCamelCase   							=model_name[-3:]
					__UpperCamelCase   							=10_00
					__UpperCamelCase   							='imagenet-1k-id2label.json'
					__UpperCamelCase   							=(1, 10_00)

					# set config attributes
					__UpperCamelCase   							=json.load(open(hf_hub_download(lowercase__      ,					lowercase__      ,					repo_type='dataset'    )      ,					'r'    )    )
					__UpperCamelCase   							={int(lowercase__    ): v for k, v in idalabel.items()}
					__UpperCamelCase   							=idalabel
					__UpperCamelCase   							={v: k for k, v in idalabel.items()}
					if size == "s12":
										__UpperCamelCase   							=[2, 2, 6, 2]
										__UpperCamelCase   							=[64, 1_28, 3_20, 5_12]
										__UpperCamelCase   							=4.0
										__UpperCamelCase   							=0.9
					elif size == "s24":
										__UpperCamelCase   							=[4, 4, 12, 4]
										__UpperCamelCase   							=[64, 1_28, 3_20, 5_12]
										__UpperCamelCase   							=4.0
										__UpperCamelCase   							=0.9
					elif size == "s36":
										__UpperCamelCase   							=[6, 6, 18, 6]
										__UpperCamelCase   							=[64, 1_28, 3_20, 5_12]
										__UpperCamelCase   							=4.0
										__UpperCamelCase   							=1E-6
										__UpperCamelCase   							=0.9
					elif size == "m36":
										__UpperCamelCase   							=[6, 6, 18, 6]
										__UpperCamelCase   							=[96, 1_92, 3_84, 7_68]
										__UpperCamelCase   							=4.0
										__UpperCamelCase   							=1E-6
										__UpperCamelCase   							=0.95
					elif size == "m48":
										__UpperCamelCase   							=[8, 8, 24, 8]
										__UpperCamelCase   							=[96, 1_92, 3_84, 7_68]
										__UpperCamelCase   							=4.0
										__UpperCamelCase   							=1E-6
										__UpperCamelCase   							=0.95
					else:
										raise ValueError(F'Size {size} not supported'    )

					# load image processor
					__UpperCamelCase   							=PoolFormerImageProcessor(crop_pct=lowercase__    )

					# Prepare image
					__UpperCamelCase   							=prepare_img()
					__UpperCamelCase   							=image_processor(images=lowercase__      ,					return_tensors='pt'    ).pixel_values

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

					# load original state dict
					__UpperCamelCase   							=torch.load(lowercase__      ,					map_location=torch.device('cpu'    )    )

					# rename keys
					__UpperCamelCase   							=rename_keys(lowercase__    )

					# create HuggingFace model and load state dict
					__UpperCamelCase   							=PoolFormerForImageClassification(lowercase__    )
					model.load_state_dict(lowercase__    )
					model.eval()

					# Define image processor
					__UpperCamelCase   							=PoolFormerImageProcessor(crop_pct=lowercase__    )
					__UpperCamelCase   							=image_processor(images=prepare_img()      ,					return_tensors='pt'    ).pixel_values

					# forward pass
					__UpperCamelCase   							=model(lowercase__    )
					__UpperCamelCase   							=outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										__UpperCamelCase   							=torch.tensor([-0.3045, -0.6758, -0.4869]    )
					elif size == "s24":
										__UpperCamelCase   							=torch.tensor([0.4402, -0.1374, -0.8045]    )
					elif size == "s36":
										__UpperCamelCase   							=torch.tensor([-0.6080, -0.5133, -0.5898]    )
					elif size == "m36":
										__UpperCamelCase   							=torch.tensor([0.3952, 0.2263, -1.2668]    )
					elif size == "m48":
										__UpperCamelCase   							=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]      ,					lowercase__      ,					atol=1E-2    )

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


if __name__ == "__main__":
			_A			=						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.'
			)
			_A			=						parser.parse_args()
			convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









from __future__ import annotations

from typing import Any

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]    ):
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					[]      ,					0    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
   _A			=						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['A', 'B', 'C'])
   generate_all_subsequences(seq)





