





'''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()
UpperCAmelCase_      							=     logging.get_logger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       int       ,    UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Optional[Any]						)			-> List[str]:



					'''simple docstring'''




					_snake_case									=				original_name.split('.'						)[0]
					_snake_case									=				key.split('.'						)
					_snake_case									=				int(key_list[key_list.index(UpperCamelCase__						) - 2]						)
					_snake_case									=				int(key_list[key_list.index(UpperCamelCase__						) - 1]						)
					_snake_case									=				orig_block_num - offset

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]						)			-> Any:



					'''simple docstring'''




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





def 						lowerCamelCase__     (     )			-> Tuple:



					'''simple docstring'''




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

					return image





@torch.no_grad()
def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       str						)			-> List[Any]:



					'''simple docstring'''




					_snake_case									=				PoolFormerConfig()

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

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

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

					# Prepare image
					_snake_case									=				prepare_img()
					_snake_case									=				image_processor(images=UpperCamelCase__       ,    return_tensors='pt'						).pixel_values

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

					# load original state dict
					_snake_case									=				torch.load(UpperCamelCase__       ,    map_location=torch.device('cpu'						)						)

					# rename keys
					_snake_case									=				rename_keys(UpperCamelCase__						)

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

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

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

					# define expected logit slices for different models
					if size == "s12":
										_snake_case									=				torch.tensor([-0.3045, -0.6758, -0.4869]						)
					elif size == "s24":
										_snake_case									=				torch.tensor([0.4402, -0.1374, -0.8045]						)
					elif size == "s36":
										_snake_case									=				torch.tensor([-0.6080, -0.5133, -0.5898]						)
					elif size == "m36":
										_snake_case									=				torch.tensor([0.3952, 0.2263, -1.2668]						)
					elif size == "m48":
										_snake_case									=				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]       ,    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__":
					UpperCAmelCase_      							=     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_      							=     parser.parse_args()
					convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










def 						lowerCamelCase__     (     UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       int       ,    UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       str						)			-> List[Any]:



					'''simple docstring'''




					if index == r:
										for j in range(UpperCamelCase__						):
															print(data[j]       ,    end=' '						)
										print(' '						)
										return
					#  When no more elements are there to put in data[]
					if i >= n:
										return
					# current is included, put next at next location
					_snake_case									=				arr[i]
					combination_util(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    index + 1       ,    UpperCamelCase__       ,    i + 1						)
					# current is excluded, replace it with
					# next (Note that i+1 is passed, but
					# index is not changed)
					combination_util(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    i + 1						)
					# The main function that prints all combinations
					# of size r in arr[] of size n. This function
					# mainly uses combinationUtil()





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       List[str]						)			-> Tuple:



					'''simple docstring'''




					_snake_case									=				[0] * r
					# Print all combination using temporary array 'data[]'
					combination_util(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    0       ,    UpperCamelCase__       ,    0						)


if __name__ == "__main__":
					# Driver code to check the function above
					UpperCAmelCase_      							=     [10, 20, 30, 40, 50]
					print_combination(arr, len(arr), 3)
					# This code is contributed by Ambuj sahu



