





import argparse
import json
from pathlib import Path

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

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase :    Tuple  							= logging.get_logger(__name__)




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=False			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=False			,				SCREAMING_SNAKE_CASE__      :  List[str]=False       )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						[]
							for i in range(config.num_hidden_layers       ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''')       )
														rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''')       )
														rename_keys.append(
														    (f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''')       )
														rename_keys.append(
														    (f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''')       )
														rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''')       )
														rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''')       )
														rename_keys.append(
														    (f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''')       )
														rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''')       )
														rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''')       )
														rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''')       )

							# embeddings
							rename_keys.extend(
							    [
							        # text embeddings
							        ("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"),
							        (
							            "text_embeddings.position_embeddings.weight",
							            "vilt.embeddings.text_embeddings.position_embeddings.weight",
							        ),
							        ("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"),
							        (
							            "text_embeddings.token_type_embeddings.weight",
							            "vilt.embeddings.text_embeddings.token_type_embeddings.weight",
							        ),
							        ("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"),
							        ("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"),
							        # patch embeddings
							        ("transformer.cls_token", "vilt.embeddings.cls_token"),
							        ("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"),
							        ("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"),
							        ("transformer.pos_embed", "vilt.embeddings.position_embeddings"),
							        # token type embeddings
							        ("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"),
							    ]       )

							# final layernorm + pooler
							rename_keys.extend(
							    [
							        ("transformer.norm.weight", "vilt.layernorm.weight"),
							        ("transformer.norm.bias", "vilt.layernorm.bias"),
							        ("pooler.dense.weight", "vilt.pooler.dense.weight"),
							        ("pooler.dense.bias", "vilt.pooler.dense.bias"),
							    ]       )

							# classifier head(s)
							if vqa_model:
														# classification head
														rename_keys.extend(
														    [
														        ("vqa_classifier.0.weight", "classifier.0.weight"),
														        ("vqa_classifier.0.bias", "classifier.0.bias"),
														        ("vqa_classifier.1.weight", "classifier.1.weight"),
														        ("vqa_classifier.1.bias", "classifier.1.bias"),
														        ("vqa_classifier.3.weight", "classifier.3.weight"),
														        ("vqa_classifier.3.bias", "classifier.3.bias"),
														    ]       )
							elif nlvr_model:
														# classification head
														rename_keys.extend(
														    [
														        ("nlvr2_classifier.0.weight", "classifier.0.weight"),
														        ("nlvr2_classifier.0.bias", "classifier.0.bias"),
														        ("nlvr2_classifier.1.weight", "classifier.1.weight"),
														        ("nlvr2_classifier.1.bias", "classifier.1.bias"),
														        ("nlvr2_classifier.3.weight", "classifier.3.weight"),
														        ("nlvr2_classifier.3.bias", "classifier.3.bias"),
														    ]       )
							else:
														pass

							return rename_keys




def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    List[Any]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    Any:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      int     =						["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str       )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      int     =						dct.pop(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						val




@torch.no_grad()
def _a (  SCREAMING_SNAKE_CASE__      :  Dict			,				SCREAMING_SNAKE_CASE__      :  Optional[int]       )	->    Any:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						ViltConfig(image_size=3_84			,				patch_size=32			,				tie_word_embeddings=SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						False
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						False
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						False
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						False
							if "vqa" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Any     =						True
														SCREAMING_SNAKE_CASE__       :      int     =						31_29
														SCREAMING_SNAKE_CASE__       :      Tuple     =						"huggingface/label-files"
														SCREAMING_SNAKE_CASE__       :      Any     =						"vqa2-id2label.json"
														SCREAMING_SNAKE_CASE__       :      Tuple     =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				repo_type="dataset"       )			,				"r"       )       )
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						idalabel
														SCREAMING_SNAKE_CASE__       :      Any     =						{v: k for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViltForQuestionAnswering(SCREAMING_SNAKE_CASE__       )
							elif "nlvr" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						True
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						2
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{0: "False", 1: "True"}
														SCREAMING_SNAKE_CASE__       :      Dict     =						{v: k for k, v in config.idalabel.items()}
														SCREAMING_SNAKE_CASE__       :      Any     =						3
														SCREAMING_SNAKE_CASE__       :      Dict     =						ViltForImagesAndTextClassification(SCREAMING_SNAKE_CASE__       )
							elif "irtr" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						True
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViltForImageAndTextRetrieval(SCREAMING_SNAKE_CASE__       )
							elif "mlm_itm" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Any     =						True
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViltForMaskedLM(SCREAMING_SNAKE_CASE__       )
							else:
														raise ValueError("Unknown model type"       )

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__			,				map_location="cpu"       )["state_dict"]
							SCREAMING_SNAKE_CASE__       :      int     =						create_rename_keys(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				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__       )
							if mlm_model or irtr_model:
														SCREAMING_SNAKE_CASE__       :      Dict     =						["itm_score.fc.weight", "itm_score.fc.bias"]
														for k in ignore_keys:
																					state_dict.pop(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

    # load state dict into HuggingFace model
							model.eval()
							if mlm_model:
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.load_state_dict(SCREAMING_SNAKE_CASE__			,				strict=SCREAMING_SNAKE_CASE__       )
														assert missing_keys == ["mlm_score.decoder.bias"]
							else:
														model.load_state_dict(SCREAMING_SNAKE_CASE__       )

							# Define processor
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						ViltImageProcessor(size=3_84       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						BertTokenizer.from_pretrained("bert-base-uncased"       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViltProcessor(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							# Forward pass on example inputs (image + text)
							if nlvr_model:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"			,				stream=SCREAMING_SNAKE_CASE__       ).raw       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"			,				stream=SCREAMING_SNAKE_CASE__       ).raw       )
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(
														    "The left image contains twice the number of dogs as the right image, and at least two dogs in total are"
														    " standing."
														)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						processor(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				return_tensors="pt"       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						processor(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				return_tensors="pt"       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(
														    input_ids=encoding_a.input_ids			,				pixel_values=encoding_a.pixel_values			,				pixel_values_a=encoding_a.pixel_values			,				)
							else:
														SCREAMING_SNAKE_CASE__       :      Any     =						Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg"			,				stream=SCREAMING_SNAKE_CASE__       ).raw       )
														if mlm_model:
																					SCREAMING_SNAKE_CASE__       :      int     =						"a bunch of [MASK] laying on a [MASK]."
														else:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"How many cats are there?"
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						processor(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				return_tensors="pt"       )
														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(**SCREAMING_SNAKE_CASE__       )

							# Verify outputs
							if mlm_model:
														SCREAMING_SNAKE_CASE__       :      Any     =						torch.Size([1, 11, 3_05_22]       )
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]       )
														assert outputs.logits.shape == expected_shape
														assert torch.allclose(outputs.logits[0, 0, :3]			,				SCREAMING_SNAKE_CASE__			,				atol=1E-4       )

														# verify masked token prediction equals "cats"
														SCREAMING_SNAKE_CASE__       :      Any     =						outputs.logits[0, 4, :].argmax(-1       ).item()
														assert tokenizer.decode([predicted_id]       ) == "cats"
							elif vqa_model:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.Size([1, 31_29]       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]       )
														assert torch.allclose(outputs.logits[0, :3]			,				SCREAMING_SNAKE_CASE__			,				atol=1E-4       )
														assert outputs.logits.shape == expected_shape
														assert torch.allclose(outputs.logits[0, 0, :3]			,				SCREAMING_SNAKE_CASE__			,				atol=1E-4       )

														# verify vqa prediction equals "2"
														SCREAMING_SNAKE_CASE__       :      str     =						outputs.logits.argmax(-1       ).item()
														assert model.config.idalabel[predicted_idx] == "2"
							elif nlvr_model:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.Size([1, 2]       )
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor([-2.8_7_2_1, 2.1_2_9_1]       )
														assert torch.allclose(outputs.logits[0, :3]			,				SCREAMING_SNAKE_CASE__			,				atol=1E-4       )
														assert outputs.logits.shape == expected_shape

							Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
							print(f'''Saving model and processor to {pytorch_dump_folder_path}'''       )
							model.save_pretrained(SCREAMING_SNAKE_CASE__       )
							processor.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		_lowerCamelCase :    Optional[int]  							= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt''',
		    type=str,
		    help='''URL of the checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		_lowerCamelCase :    Union[str, Any]  							= parser.parse_args()
		convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













import requests
from bsa import BeautifulSoup




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    str:

							'''simple docstring'''






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


if __name__ == "__main__":
		_lowerCamelCase :    Any  							= {
		    '''title''': (
		        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
		        '''capacitance, volumetric capacitance, and energy density'''
		    ),
		    '''journal''': '''Chem. Mater.''',
		    '''volume''': 3_0,
		    '''pages''': '''3979-3990''',
		    '''year''': 2_0_1_8,
		    '''hl''': '''en''',
		}
		print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))






