

"""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  :      str							      =		logging.get_logger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	original_name.split('''.''' )[0]
	lowerCAmelCase__	:						List[Any]  				=	key.split('''.''' )
	lowerCAmelCase__	:						Union[str, Any]  				=	int(key_list[key_list.index(A_ ) - 2] )
	lowerCAmelCase__	:						Any  				=	int(key_list[key_list.index(A_ ) - 1] )
	lowerCAmelCase__	:						Any  				=	orig_block_num - offset

	lowerCAmelCase__	:						Optional[int]  				=	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  (      A_ ):
	lowerCAmelCase__	:						Any  				=	OrderedDict()
	lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	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__	:						Optional[int]  				=	key[: key.find('''proj''' )]
			lowerCAmelCase__	:						Dict  				=	key.replace(A_						,     f'patch_embeddings.{total_embed_found}.' )
			lowerCAmelCase__	:						int  				=	key.replace('''proj'''						,     '''projection''' )
			if key.endswith('''bias''' ):
				total_embed_found += 1
		if "patch_embeddings" in key:
			lowerCAmelCase__	:						Dict  				=	'''poolformer.encoder.''' + key
		if "mlp.fc1" in key:
			lowerCAmelCase__	:						Tuple  				=	replace_key_with_offset(A_						,     A_						,     '''mlp.fc1'''						,     '''output.conv1''' )
		if "mlp.fc2" in key:
			lowerCAmelCase__	:						str  				=	replace_key_with_offset(A_						,     A_						,     '''mlp.fc2'''						,     '''output.conv2''' )
		if "norm1" in key:
			lowerCAmelCase__	:						int  				=	replace_key_with_offset(A_						,     A_						,     '''norm1'''						,     '''before_norm''' )
		if "norm2" in key:
			lowerCAmelCase__	:						Dict  				=	replace_key_with_offset(A_						,     A_						,     '''norm2'''						,     '''after_norm''' )
		if "layer_scale_1" in key:
			lowerCAmelCase__	:						Union[str, Any]  				=	replace_key_with_offset(A_						,     A_						,     '''layer_scale_1'''						,     '''layer_scale_1''' )
		if "layer_scale_2" in key:
			lowerCAmelCase__	:						Any  				=	replace_key_with_offset(A_						,     A_						,     '''layer_scale_2'''						,     '''layer_scale_2''' )
		if "head" in key:
			lowerCAmelCase__	:						str  				=	key.replace('''head'''						,     '''classifier''' )
		lowerCAmelCase__	:						str  				=	value
	return new_state_dict

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[Any]  				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase__	:						int  				=	Image.open(requests.get(A_						,     stream=A_ ).raw )

	return image

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Tuple  				=	PoolFormerConfig()

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

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

	# load image processor
	lowerCAmelCase__	:						Any  				=	PoolFormerImageProcessor(crop_pct=A_ )

	# Prepare image
	lowerCAmelCase__	:						Optional[Any]  				=	prepare_img()
	lowerCAmelCase__	:						List[str]  				=	image_processor(images=A_						,     return_tensors='''pt''' ).pixel_values

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

	# load original state dict
	lowerCAmelCase__	:						int  				=	torch.load(A_						,     map_location=torch.device('''cpu''' ) )

	# rename keys
	lowerCAmelCase__	:						Any  				=	rename_keys(A_ )

	# create HuggingFace model and load state dict
	lowerCAmelCase__	:						List[str]  				=	PoolFormerForImageClassification(A_ )
	model.load_state_dict(A_ )
	model.eval()

	# Define image processor
	lowerCAmelCase__	:						Union[str, Any]  				=	PoolFormerImageProcessor(crop_pct=A_ )
	lowerCAmelCase__	:						List[Any]  				=	image_processor(images=prepare_img()						,     return_tensors='''pt''' ).pixel_values

	# forward pass
	lowerCAmelCase__	:						Union[str, Any]  				=	model(A_ )
	lowerCAmelCase__	:						Dict  				=	outputs.logits

	# define expected logit slices for different models
	if size == "s12":
		lowerCAmelCase__	:						int  				=	torch.tensor([-0.3_045, -0.6_758, -0.4_869] )
	elif size == "s24":
		lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([0.4_402, -0.1_374, -0.8_045] )
	elif size == "s36":
		lowerCAmelCase__	:						Tuple  				=	torch.tensor([-0.6_080, -0.5_133, -0.5_898] )
	elif size == "m36":
		lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([0.3_952, 0.2_263, -1.2_668] )
	elif size == "m48":
		lowerCAmelCase__	:						int  				=	torch.tensor([0.1_167, -0.0_656, -0.3_423] )
	else:
		raise ValueError(f'Size {size} not supported' )

	# verify logits
	assert logits.shape == expected_shape
	assert torch.allclose(logits[0, :3]						,     A_						,     atol=1e-2 )

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


if __name__ == "__main__":
							__UpperCamelCase  :      List[str]							      =		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)







"""simple docstring"""





import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							__UpperCamelCase  :      Tuple							      =		'''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

							print('''Googling.....''')

							__UpperCamelCase  :      Optional[int]							      =		F'''https://www.google.com/search?q={query}&num=100'''

							__UpperCamelCase  :      Optional[Any]							      =		requests.get(
							    url,
							    headers={'''User-Agent''': str(UserAgent().random)},
							)

							try:
														__UpperCamelCase  :      Union[str, Any]							      =		(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''yuRUbf'''})
														    .find('''a''')
														    .get('''href''')
														)

							except AttributeError:
														__UpperCamelCase  :      str							      =		parse_qs(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''kCrYT'''})
														    .find('''a''')
														    .get('''href''')
														)['''url'''][0]

							webbrowser.open(link)




