"""simple docstring"""






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__  :  Optional[int]						     =	logging.get_logger(__name__)



def   a_						(					lowerCamelCase					,			lowerCamelCase=False					,			lowerCamelCase=False					,			lowerCamelCase=False       ):
				UpperCAmelCase__             =							[]
				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_						(					lowerCamelCase					,			lowerCamelCase       ):
				for i in range(config.num_hidden_layers       ):
								UpperCAmelCase__             =							'vilt.'
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								UpperCAmelCase__             =							state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight'''       )
								UpperCAmelCase__             =							state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias'''       )
								# next, add query, keys and values (in that order) to the state dict
								UpperCAmelCase__             =							in_proj_weight[
								    : config.hidden_size, :
								]
								UpperCAmelCase__             =							in_proj_bias[: config.hidden_size]
								UpperCAmelCase__             =							in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								UpperCAmelCase__             =							in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								UpperCAmelCase__             =							in_proj_weight[
								    -config.hidden_size :, :
								]
								UpperCAmelCase__             =							in_proj_bias[-config.hidden_size :]



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							dct.pop(lowerCamelCase       )
				UpperCAmelCase__             =							val



@torch.no_grad()
def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							ViltConfig(image_size=3_8_4					,			patch_size=3_2					,			tie_word_embeddings=lowerCamelCase       )
				UpperCAmelCase__             =							False
				UpperCAmelCase__             =							False
				UpperCAmelCase__             =							False
				UpperCAmelCase__             =							False
				if "vqa" in checkpoint_url:
								UpperCAmelCase__             =							True
								UpperCAmelCase__             =							3_1_2_9
								UpperCAmelCase__             =							'huggingface/label-files'
								UpperCAmelCase__             =							'vqa2-id2label.json'
								UpperCAmelCase__             =							json.load(open(hf_hub_download(lowerCamelCase					,			lowerCamelCase					,			repo_type='dataset'       )					,			'r'       )       )
								UpperCAmelCase__             =							{int(lowerCamelCase       ): v for k, v in idalabel.items()}
								UpperCAmelCase__             =							idalabel
								UpperCAmelCase__             =							{v: k for k, v in idalabel.items()}
								UpperCAmelCase__             =							ViltForQuestionAnswering(lowerCamelCase       )
				elif "nlvr" in checkpoint_url:
								UpperCAmelCase__             =							True
								UpperCAmelCase__             =							2
								UpperCAmelCase__             =							{0: 'False', 1: 'True'}
								UpperCAmelCase__             =							{v: k for k, v in config.idalabel.items()}
								UpperCAmelCase__             =							3
								UpperCAmelCase__             =							ViltForImagesAndTextClassification(lowerCamelCase       )
				elif "irtr" in checkpoint_url:
								UpperCAmelCase__             =							True
								UpperCAmelCase__             =							ViltForImageAndTextRetrieval(lowerCamelCase       )
				elif "mlm_itm" in checkpoint_url:
								UpperCAmelCase__             =							True
								UpperCAmelCase__             =							ViltForMaskedLM(lowerCamelCase       )
				else:
								raise ValueError('Unknown model type'       )

				# load state_dict of original model, remove and rename some keys
				UpperCAmelCase__             =							torch.hub.load_state_dict_from_url(lowerCamelCase					,			map_location='cpu'       )['state_dict']
				UpperCAmelCase__             =							create_rename_keys(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )
				read_in_q_k_v(lowerCamelCase					,			lowerCamelCase       )
				if mlm_model or irtr_model:
								UpperCAmelCase__             =							['itm_score.fc.weight', 'itm_score.fc.bias']
								for k in ignore_keys:
												state_dict.pop(lowerCamelCase					,			lowerCamelCase       )

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

				# Define processor
				UpperCAmelCase__             =							ViltImageProcessor(size=3_8_4       )
				UpperCAmelCase__             =							BertTokenizer.from_pretrained('bert-base-uncased'       )
				UpperCAmelCase__             =							ViltProcessor(lowerCamelCase					,			lowerCamelCase       )

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

				# Verify outputs
				if mlm_model:
								UpperCAmelCase__             =							torch.Size([1, 1_1, 3_0_5_2_2]       )
								UpperCAmelCase__             =							torch.tensor([-12.5061, -12.5123, -12.5174]       )
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]					,			lowerCamelCase					,			atol=1e-4       )

								# verify masked token prediction equals "cats"
								UpperCAmelCase__             =							outputs.logits[0, 4, :].argmax(-1       ).item()
								assert tokenizer.decode([predicted_id]       ) == "cats"
				elif vqa_model:
								UpperCAmelCase__             =							torch.Size([1, 3_1_2_9]       )
								UpperCAmelCase__             =							torch.tensor([-15.9495, -18.1472, -10.3041]       )
								assert torch.allclose(outputs.logits[0, :3]					,			lowerCamelCase					,			atol=1e-4       )
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]					,			lowerCamelCase					,			atol=1e-4       )

								# verify vqa prediction equals "2"
								UpperCAmelCase__             =							outputs.logits.argmax(-1       ).item()
								assert model.config.idalabel[predicted_idx] == "2"
				elif nlvr_model:
								UpperCAmelCase__             =							torch.Size([1, 2]       )
								UpperCAmelCase__             =							torch.tensor([-2.8721, 2.1291]       )
								assert torch.allclose(outputs.logits[0, :3]					,			lowerCamelCase					,			atol=1e-4       )
								assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
						lowerCAmelCase__  :  List[Any]						     =	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__  :  str						     =	parser.parse_args()
						convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if a < 0 or b < 0:
								raise ValueError('the value of both inputs must be positive'       )

				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"
				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"

				UpperCAmelCase__             =							max(len(lowerCamelCase       )					,			len(lowerCamelCase       )       )

				return "0b" + "".join(
				    str(int(char_a == '1' and char_b == '1'       )       )
				    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase       )					,			b_binary.zfill(lowerCamelCase       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



