


"""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()
a__    :							List[str]            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    original_name.split("."       )[0]
					__SCREAMING_SNAKE_CASE						       =    key.split("."       )
					__SCREAMING_SNAKE_CASE						       =    int(key_list[key_list.index(lowerCAmelCase_       ) - 2]       )
					__SCREAMING_SNAKE_CASE						       =    int(key_list[key_list.index(lowerCAmelCase_       ) - 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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__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(lowerCAmelCase_  ,						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(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"mlp.fc1"  ,						"output.conv1"       )
										if "mlp.fc2" in key:
															__SCREAMING_SNAKE_CASE						       =    replace_key_with_offset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"mlp.fc2"  ,						"output.conv2"       )
										if "norm1" in key:
															__SCREAMING_SNAKE_CASE						       =    replace_key_with_offset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"norm1"  ,						"before_norm"       )
										if "norm2" in key:
															__SCREAMING_SNAKE_CASE						       =    replace_key_with_offset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"norm2"  ,						"after_norm"       )
										if "layer_scale_1" in key:
															__SCREAMING_SNAKE_CASE						       =    replace_key_with_offset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"layer_scale_1"  ,						"layer_scale_1"       )
										if "layer_scale_2" in key:
															__SCREAMING_SNAKE_CASE						       =    replace_key_with_offset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"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        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    "http://images.cocodataset.org/val2017/000000039769.jpg"
					__SCREAMING_SNAKE_CASE						       =    Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_       ).raw       )

					return image




@torch.no_grad()
def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__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						       =    1000
					__SCREAMING_SNAKE_CASE						       =    "imagenet-1k-id2label.json"
					__SCREAMING_SNAKE_CASE						       =    (1, 1000)

					# set config attributes
					__SCREAMING_SNAKE_CASE						       =    json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type="dataset"       )  ,						"r"       )       )
					__SCREAMING_SNAKE_CASE						       =    {int(lowerCAmelCase_       ): 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						       =    0.95
					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						       =    0.95
					else:
										raise ValueError(f"""Size {size} not supported"""       )

					# load image processor
					__SCREAMING_SNAKE_CASE						       =    PoolFormerImageProcessor(crop_pct=lowerCAmelCase_       )

					# Prepare image
					__SCREAMING_SNAKE_CASE						       =    prepare_img()
					__SCREAMING_SNAKE_CASE						       =    image_processor(images=lowerCAmelCase_  ,						return_tensors="pt"       ).pixel_values

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

					# load original state dict
					__SCREAMING_SNAKE_CASE						       =    torch.load(lowerCAmelCase_  ,						map_location=torch.device("cpu"       )       )

					# rename keys
					__SCREAMING_SNAKE_CASE						       =    rename_keys(lowerCAmelCase_       )

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

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

					# forward pass
					__SCREAMING_SNAKE_CASE						       =    model(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    outputs.logits

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




"""simple docstring"""







from __future__ import annotations

import requests




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
					return requests.get(lowerCAmelCase_       ).json()




def        UpperCAmelCase__	(lowerCAmelCase_ = 10       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    "https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
					__SCREAMING_SNAKE_CASE						       =    requests.get(lowerCAmelCase_       ).json()[:max_stories]
					return [get_hackernews_story(lowerCAmelCase_       ) for story_id in story_ids]




def        UpperCAmelCase__	(lowerCAmelCase_ = 10       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    hackernews_top_stories(lowerCAmelCase_       )
					return "\n".join("* [{title}]({url})".format(**lowerCAmelCase_       ) for story in stories       )


if __name__ == "__main__":
		print(hackernews_top_stories_as_markdown())
