





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def _a (  SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						old_name

							if "patch_embed" in old_name:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						old_name.split("."       )

														if layer == "0":
																					SCREAMING_SNAKE_CASE__       :      int     =						old_name.replace("0"			,				"convolution1"       )
														elif layer == "1":
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						old_name.replace("1"			,				"batchnorm_before"       )
														elif layer == "3":
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						old_name.replace("3"			,				"convolution2"       )
														else:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						old_name.replace("4"			,				"batchnorm_after"       )

							if "network" in old_name and re.search(R"\d\.\d"			,				SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Any     =						R"\b\d{2}\b"
														if bool(re.search(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       ):
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						re.search(R"\d\.\d\d."			,				SCREAMING_SNAKE_CASE__       ).group()
														else:
																					SCREAMING_SNAKE_CASE__       :      str     =						re.search(R"\d\.\d."			,				SCREAMING_SNAKE_CASE__       ).group()
														if int(match[0]       ) < 6:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						old_name.replace(SCREAMING_SNAKE_CASE__			,				""       )
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						trimmed_name.replace("network"			,				match[0] + ".meta4D_layers.blocks." + match[2:-1]       )
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"intermediate_stages." + trimmed_name
														else:
																					SCREAMING_SNAKE_CASE__       :      str     =						old_name.replace(SCREAMING_SNAKE_CASE__			,				""       )
																					if int(match[2]       ) < num_meta4D_last_stage:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						trimmed_name.replace("network"			,				"meta4D_layers.blocks." + match[2]       )
																					else:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						str(int(match[2]       ) - num_meta4D_last_stage       )
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						trimmed_name.replace("network"			,				"meta3D_layers.blocks." + layer_index       )
																												if "norm1" in old_name:
																																			SCREAMING_SNAKE_CASE__       :      Dict     =						trimmed_name.replace("norm1"			,				"layernorm1"       )
																												elif "norm2" in old_name:
																																			SCREAMING_SNAKE_CASE__       :      List[str]     =						trimmed_name.replace("norm2"			,				"layernorm2"       )
																												elif "fc1" in old_name:
																																			SCREAMING_SNAKE_CASE__       :      str     =						trimmed_name.replace("fc1"			,				"linear_in"       )
																												elif "fc2" in old_name:
																																			SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						trimmed_name.replace("fc2"			,				"linear_out"       )

																					SCREAMING_SNAKE_CASE__       :      str     =						"last_stage." + trimmed_name

							elif "network" in old_name and re.search(R".\d."			,				SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      int     =						old_name.replace("network"			,				"intermediate_stages"       )

							if "fc" in new_name:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_name.replace("fc"			,				"convolution"       )
							elif ("norm1" in new_name) and ("layernorm1" not in new_name):
														SCREAMING_SNAKE_CASE__       :      str     =						new_name.replace("norm1"			,				"batchnorm_before"       )
							elif ("norm2" in new_name) and ("layernorm2" not in new_name):
														SCREAMING_SNAKE_CASE__       :      List[str]     =						new_name.replace("norm2"			,				"batchnorm_after"       )
							if "proj" in new_name:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						new_name.replace("proj"			,				"projection"       )
							if "dist_head" in new_name:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						new_name.replace("dist_head"			,				"distillation_classifier"       )
							elif "head" in new_name:
														SCREAMING_SNAKE_CASE__       :      Dict     =						new_name.replace("head"			,				"classifier"       )
							elif "patch_embed" in new_name:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"efficientformer." + new_name
							elif new_name == "norm.weight" or new_name == "norm.bias":
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						new_name.replace("norm"			,				"layernorm"       )
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"efficientformer." + new_name
							else:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"efficientformer.encoder." + new_name

							return new_name


def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  List[str]       )	->    int:

							'''simple docstring'''






							for key in checkpoint.copy().keys():
														SCREAMING_SNAKE_CASE__       :      int     =						checkpoint.pop(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						val

							return checkpoint


def _a (  )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"http://images.cocodataset.org/val2017/000000039769.jpg"
							SCREAMING_SNAKE_CASE__       :      List[str]     =						Image.open(requests.get(SCREAMING_SNAKE_CASE__			,				stream=SCREAMING_SNAKE_CASE__       ).raw       )

							return image


def _a (  SCREAMING_SNAKE_CASE__      :  Path			,				SCREAMING_SNAKE_CASE__      :  Path			,				SCREAMING_SNAKE_CASE__      :  Path			,				SCREAMING_SNAKE_CASE__      :  bool       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.load(SCREAMING_SNAKE_CASE__			,				map_location="cpu"       )["model"]
							SCREAMING_SNAKE_CASE__       :      Dict     =						EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Any     =						"_".join(checkpoint_path.split("/"       )[-1].split("."       )[0].split("_"       )[:-1]       )

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						config.depths[-1] - config.num_metaad_blocks + 1
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						convert_torch_checkpoint(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							model.load_state_dict(SCREAMING_SNAKE_CASE__       )
							model.eval()

							SCREAMING_SNAKE_CASE__       :      int     =						{
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							# prepare image
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						prepare_img()
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						2_56
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						2_24
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						EfficientFormerImageProcessor(
							    size={"shortest_edge": image_size}			,				crop_size={"height": crop_size, "width": crop_size}			,				resample=pillow_resamplings["bicubic"]			,				)
							SCREAMING_SNAKE_CASE__       :      int     =						processor(images=SCREAMING_SNAKE_CASE__			,				return_tensors="pt"       ).pixel_values

							# original processing pipeline
							SCREAMING_SNAKE_CASE__       :      List[str]     =						Compose(
							    [
							        Resize(SCREAMING_SNAKE_CASE__			,				interpolation=pillow_resamplings["bicubic"]       ),
							        CenterCrop(SCREAMING_SNAKE_CASE__       ),
							        ToTensor(),
							        Normalize(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ),
							    ]       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						image_transforms(SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )

							assert torch.allclose(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						outputs.logits

							SCREAMING_SNAKE_CASE__       :      int     =						(1, 10_00)

							if "l1" in model_name:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.Tensor(
														    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]       )
														assert torch.allclose(logits[0, :10]			,				SCREAMING_SNAKE_CASE__			,				atol=1E-3       )
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.Tensor(
														    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]       )
														assert torch.allclose(logits[0, :10]			,				SCREAMING_SNAKE_CASE__			,				atol=1E-3       )
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.Tensor(
														    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]       )
														assert logits.shape == expected_shape
							else:
														raise ValueError(
														    f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''       )

							# Save Checkpoints
							Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
							model.save_pretrained(SCREAMING_SNAKE_CASE__       )
							print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''       )
							processor.save_pretrained(SCREAMING_SNAKE_CASE__       )
							print(f'''Processor successfuly saved at {pytorch_dump_path}'''       )

							if push_to_hub:
														print("Pushing model to the hub..."       )

														model.push_to_hub(
														    repo_id=f'''Bearnardd/{pytorch_dump_path}'''			,				commit_message="Add model"			,				use_temp_dir=SCREAMING_SNAKE_CASE__			,				)
														processor.push_to_hub(
														    repo_id=f'''Bearnardd/{pytorch_dump_path}'''			,				commit_message="Add image processor"			,				use_temp_dir=SCREAMING_SNAKE_CASE__			,				)


if __name__ == "__main__":
		_lowerCamelCase :    List[Any]  							= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--pytorch_model_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to EfficientFormer pytorch checkpoint.''',
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The json file for EfficientFormer model config.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)

		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
		parser.add_argument(
		    '''--no-push_to_hub''',
		    dest='''push_to_hub''',
		    action='''store_false''',
		    help='''Do not push model and image processor to the hub''',
		)
		parser.set_defaults(push_to_hub=True)

		_lowerCamelCase :    Tuple  							= parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)













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__":
		_lowerCamelCase :    Union[str, Any]  							= '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

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

		_lowerCamelCase :    Any  							= f"https://www.google.com/search?q={query}&num=100"

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

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

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

		webbrowser.open(link)






