


'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Optional[int]=False       ):
					__a      :	int    =  []
					for i in range(config.num_hidden_layers       ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")       )
										rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")       )
										rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")       )
										rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")       )
										rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")       )
										rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")       )
										rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")       )
										rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")       )
										rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")       )
										rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")       )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ('cls_token', 'deit.embeddings.cls_token'),
					        ('dist_token', 'deit.embeddings.distillation_token'),
					        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
					        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
					        ('pos_embed', 'deit.embeddings.position_embeddings'),
					    ]       )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ('norm.weight', 'layernorm.weight'),
										        ('norm.bias', 'layernorm.bias'),
										        ('pre_logits.fc.weight', 'pooler.dense.weight'),
										        ('pre_logits.fc.bias', 'pooler.dense.bias'),
										    ]       )

										# if just the base model, we should remove "deit" from all keys that start with "deit"
										__a      :	Union[str, Any]    =  [(pair[0], pair[1][4:]) if pair[1].startswith('deit'       ) else pair for pair in rename_keys]
					else:
										# layernorm + classification heads
										rename_keys.extend(
										    [
										        ('norm.weight', 'deit.layernorm.weight'),
										        ('norm.bias', 'deit.layernorm.bias'),
										        ('head.weight', 'cls_classifier.weight'),
										        ('head.bias', 'cls_classifier.bias'),
										        ('head_dist.weight', 'distillation_classifier.weight'),
										        ('head_dist.bias', 'distillation_classifier.bias'),
										    ]       )

					return rename_keys






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       Any=False       ):
					for i in range(config.num_hidden_layers       ):
										if base_model:
															__a      :	List[Any]    =  ''
										else:
															__a      :	Union[str, Any]    =  'deit.'
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										__a      :	List[Any]    =  state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""       )
										__a      :	Union[str, Any]    =  state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""       )
										# next, add query, keys and values (in that order) to the state dict
										__a      :	List[Any]    =  in_proj_weight[
										    : config.hidden_size, :
										]
										__a      :	List[Any]    =  in_proj_bias[: config.hidden_size]
										__a      :	str    =  in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__a      :	Union[str, Any]    =  in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__a      :	Optional[int]    =  in_proj_weight[
										    -config.hidden_size :, :
										]
										__a      :	Tuple    =  in_proj_bias[-config.hidden_size :]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[int]						,				_SCREAMING_SNAKE_CASE				:       str       ):
					__a      :	Union[str, Any]    =  dct.pop(_SCREAMING_SNAKE_CASE       )
					__a      :	Tuple    =  val






def   lowerCamelCase							():
					__a      :	Tuple    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a      :	Optional[Any]    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,				stream=_SCREAMING_SNAKE_CASE       ).raw       )
					return im






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       int       ):
					__a      :	Optional[Any]    =  DeiTConfig()
					# all deit models have fine-tuned heads
					__a      :	int    =  False
					# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
					__a      :	str    =  1_000
					__a      :	str    =  'huggingface/label-files'
					__a      :	Union[str, Any]    =  'imagenet-1k-id2label.json'
					__a      :	Dict    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				repo_type='dataset'       )						,				'r'       )       )
					__a      :	List[Any]    =  {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
					__a      :	List[str]    =  idalabel
					__a      :	Optional[int]    =  {v: k for k, v in idalabel.items()}
					__a      :	Union[str, Any]    =  int(deit_name[-6:-4]       )
					__a      :	List[str]    =  int(deit_name[-3:]       )
					# size of the architecture
					if deit_name[9:].startswith('tiny'       ):
										__a      :	Dict    =  192
										__a      :	List[Any]    =  768
										__a      :	Optional[int]    =  12
										__a      :	Any    =  3
					elif deit_name[9:].startswith('small'       ):
										__a      :	Union[str, Any]    =  384
										__a      :	List[str]    =  1_536
										__a      :	List[str]    =  12
										__a      :	int    =  6
					if deit_name[9:].startswith('base'       ):
										pass
					elif deit_name[4:].startswith('large'       ):
										__a      :	str    =  1_024
										__a      :	Tuple    =  4_096
										__a      :	Any    =  24
										__a      :	Any    =  16

					# load original model from timm
					__a      :	List[Any]    =  timm.create_model(_SCREAMING_SNAKE_CASE						,				pretrained=_SCREAMING_SNAKE_CASE       )
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					__a      :	Union[str, Any]    =  timm_model.state_dict()
					__a      :	str    =  create_rename_keys(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					for src, dest in rename_keys:
										rename_key(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					read_in_q_k_v(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )

					# load HuggingFace model
					__a      :	Optional[Any]    =  DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE       ).eval()
					model.load_state_dict(_SCREAMING_SNAKE_CASE       )

					# Check outputs on an image, prepared by DeiTImageProcessor
					__a      :	Optional[Any]    =  int(
					    (256 / 224) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
					__a      :	List[Any]    =  DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE						,				crop_size=config.image_size       )
					__a      :	List[Any]    =  image_processor(images=prepare_img()						,				return_tensors='pt'       )
					__a      :	Dict    =  encoding['pixel_values']
					__a      :	str    =  model(_SCREAMING_SNAKE_CASE       )

					__a      :	Dict    =  timm_model(_SCREAMING_SNAKE_CASE       )
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(_SCREAMING_SNAKE_CASE						,				outputs.logits						,				atol=1e-3       )

					Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
					print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""       )
					model.save_pretrained(_SCREAMING_SNAKE_CASE       )
					print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
					image_processor.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	__lowercase     :						List[Any]	=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--deit_name',
	    default='vit_deit_base_distilled_patch16_224',
	    type=str,
	    help='Name of the DeiT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	__lowercase     :						Optional[Any]	=     parser.parse_args()
	convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







'''simple docstring'''



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__lowercase     :						str	=     {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str = "dhaka"						,				_SCREAMING_SNAKE_CASE				:       int = 5       ):
					__a      :	Optional[Any]    =  min(_SCREAMING_SNAKE_CASE						,				50       )  # Prevent abuse!
					__a      :	Optional[Any]    =  {
					    'q': query,
					    'tbm': 'isch',
					    'hl': 'en',
					    'ijn': '0',
					}

					__a      :	Tuple    =  requests.get('https://www.google.com/search'						,				params=_SCREAMING_SNAKE_CASE						,				headers=_SCREAMING_SNAKE_CASE       )
					__a      :	Dict    =  BeautifulSoup(html.text						,				'html.parser'       )
					__a      :	List[str]    =  ''.join(
					    re.findall(r'AF_initDataCallback\(([^<]+)\);'						,				str(soup.select('script'       )       )       )       )

					__a      :	Optional[Any]    =  json.dumps(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  json.loads(_SCREAMING_SNAKE_CASE       )

					__a      :	List[Any]    =  re.findall(
					    r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'						,				_SCREAMING_SNAKE_CASE						,				)
					if not matched_google_image_data:
										return 0

					__a      :	Tuple    =  re.sub(
					    r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'						,				''						,				str(_SCREAMING_SNAKE_CASE       )						,				)

					__a      :	Optional[Any]    =  re.findall(
					    r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'						,				_SCREAMING_SNAKE_CASE						,				)
					for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE       ):
										if index >= max_images:
															return index
										__a      :	List[str]    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Tuple    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Dict    =  urllib.request.build_opener()
										__a      :	Union[str, Any]    =  [
										    (
										        'User-Agent',
										        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
										        ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
										    )
										]
										urllib.request.install_opener(_SCREAMING_SNAKE_CASE       )
										__a      :	List[Any]    =  F"""query_{query.replace(" "						,				"_"       )}"""
										if not os.path.exists(_SCREAMING_SNAKE_CASE       ):
															os.makedirs(_SCREAMING_SNAKE_CASE       )
										urllib.request.urlretrieve(  # noqa: S310
										    _SCREAMING_SNAKE_CASE						,				F"""{path_name}/original_size_img_{index}.jpg"""       )
					return index


if __name__ == "__main__":
	try:
		__lowercase     :						Optional[int]	=     download_images_from_google_query(sys.argv[1])
		print(f'''{image_count} images were downloaded to disk.''')
	except IndexError:
		print('Please provide a search term.')
		raise



