





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



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    Optional[Any]:
		__lowercase											=     original_name.split('.'							)[0]
		__lowercase											=     key.split('.'							)
		__lowercase											=     int(key_list[key_list.index(__UpperCAmelCase							) - 2]							)
		__lowercase											=     int(key_list[key_list.index(__UpperCAmelCase							) - 1]							)
		__lowercase											=     orig_block_num - offset

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



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



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

		return image



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Any:
		__lowercase											=     PoolFormerConfig()

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

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

		# load image processor
		__lowercase											=     PoolFormerImageProcessor(crop_pct=__UpperCAmelCase							)

		# Prepare image
		__lowercase											=     prepare_img()
		__lowercase											=     image_processor(images=__UpperCAmelCase						,   return_tensors='pt'							).pixel_values

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

		# load original state dict
		__lowercase											=     torch.load(__UpperCAmelCase						,   map_location=torch.device('cpu'							)							)

		# rename keys
		__lowercase											=     rename_keys(__UpperCAmelCase							)

		# create HuggingFace model and load state dict
		__lowercase											=     PoolFormerForImageClassification(__UpperCAmelCase							)
		model.load_state_dict(__UpperCAmelCase							)
		model.eval()

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

		# forward pass
		__lowercase											=     model(__UpperCAmelCase							)
		__lowercase											=     outputs.logits

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









from __future__ import annotations

import requests






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






def     __a					(		__UpperCAmelCase = 10       ):
 a__												= '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
 a__												= requests.get(__UpperCAmelCase       ).json()[:max_stories]
 return [get_hackernews_story(__UpperCAmelCase       ) for story_id in story_ids]






def     __a					(		__UpperCAmelCase = 10       ):
 a__												= hackernews_top_stories(__UpperCAmelCase       )
 return "\n".join('''* [{title}]({url})'''.format(**__UpperCAmelCase       ) for story in stories       )


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





