



"""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()
__a      =				logging.get_logger(__name__)
def    A_  (			_lowercase,					_lowercase=False	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			[]
						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"
												snake_case_ :List[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    A_  (			_lowercase,					_lowercase,					_lowercase=False	):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_ :int						=			dct.pop(_lowercase	)
						snake_case_ :Tuple						=			val
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Dict						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_ :Union[str, Any]						=			Image.open(requests.get(_lowercase,					stream=_lowercase	).raw	)
						return im
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			DeiTConfig()
						# all deit models have fine-tuned heads
						snake_case_ :Optional[int]						=			False
						# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
						snake_case_ :int						=			1000
						snake_case_ :Optional[int]						=			"""huggingface/label-files"""
						snake_case_ :List[Any]						=			"""imagenet-1k-id2label.json"""
						snake_case_ :str						=			json.load(open(hf_hub_download(_lowercase,					_lowercase,					repo_type="""dataset"""	),					"""r"""	)	)
						snake_case_ :Dict						=			{int(_lowercase	): v for k, v in idalabel.items()}
						snake_case_ :Optional[Any]						=			idalabel
						snake_case_ :Union[str, Any]						=			{v: k for k, v in idalabel.items()}
						snake_case_ :Any						=			int(deit_name[-6:-4]	)
						snake_case_ :Any						=			int(deit_name[-3:]	)
						# size of the architecture
						if deit_name[9:].startswith("""tiny"""	):
												snake_case_ :Tuple						=			192
												snake_case_ :Optional[int]						=			768
												snake_case_ :Tuple						=			12
												snake_case_ :Tuple						=			3
						elif deit_name[9:].startswith("""small"""	):
												snake_case_ :List[Any]						=			384
												snake_case_ :Dict						=			1536
												snake_case_ :Optional[int]						=			12
												snake_case_ :str						=			6
						if deit_name[9:].startswith("""base"""	):
												pass
						elif deit_name[4:].startswith("""large"""	):
												snake_case_ :int						=			1024
												snake_case_ :List[Any]						=			4096
												snake_case_ :Any						=			24
												snake_case_ :Optional[int]						=			16

						# load original model from timm
						snake_case_ :int						=			timm.create_model(_lowercase,					pretrained=_lowercase	)
						timm_model.eval()

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

						# load HuggingFace model
						snake_case_ :Union[str, Any]						=			DeiTForImageClassificationWithTeacher(_lowercase	).eval()
						model.load_state_dict(_lowercase	)

						# Check outputs on an image, prepared by DeiTImageProcessor
						snake_case_ :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
						snake_case_ :Any						=			DeiTImageProcessor(size=_lowercase,					crop_size=config.image_size	)
						snake_case_ :List[str]						=			image_processor(images=prepare_img(),					return_tensors="""pt"""	)
						snake_case_ :Optional[Any]						=			encoding["""pixel_values"""]
						snake_case_ :Optional[Any]						=			model(_lowercase	)

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

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


if __name__ == "__main__":
						__a      =				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."
						)

						__a      =				parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




