
"""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()
_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_,						a_=False,						a_=False,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   []
		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 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


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





		'''simple docstring'''


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





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   dct.pop(a_      )
		lowerCamelCase					:      List[Any]          =   val
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   ViltConfig(image_size=384,						patch_size=32,						tie_word_embeddings=a_      )
		lowerCamelCase					:      Tuple          =   False
		lowerCamelCase					:      List[Any]          =   False
		lowerCamelCase					:      Optional[Any]          =   False
		lowerCamelCase					:      Optional[int]          =   False
		if "vqa" in checkpoint_url:
				lowerCamelCase					:      List[str]          =   True
				lowerCamelCase					:      Optional[Any]          =   3129
				lowerCamelCase					:      Dict          =   'huggingface/label-files'
				lowerCamelCase					:      Union[str, Any]          =   'vqa2-id2label.json'
				lowerCamelCase					:      List[str]          =   json.load(open(hf_hub_download(a_,						a_,						repo_type='dataset'      ),						'r'      )      )
				lowerCamelCase					:      Optional[int]          =   {int(a_      ): v for k, v in idalabel.items()}
				lowerCamelCase					:      List[str]          =   idalabel
				lowerCamelCase					:      int          =   {v: k for k, v in idalabel.items()}
				lowerCamelCase					:      Optional[int]          =   ViltForQuestionAnswering(a_      )
		elif "nlvr" in checkpoint_url:
				lowerCamelCase					:      List[Any]          =   True
				lowerCamelCase					:      Any          =   2
				lowerCamelCase					:      Optional[int]          =   {0: 'False', 1: 'True'}
				lowerCamelCase					:      Any          =   {v: k for k, v in config.idalabel.items()}
				lowerCamelCase					:      Tuple          =   3
				lowerCamelCase					:      Optional[int]          =   ViltForImagesAndTextClassification(a_      )
		elif "irtr" in checkpoint_url:
				lowerCamelCase					:      List[Any]          =   True
				lowerCamelCase					:      str          =   ViltForImageAndTextRetrieval(a_      )
		elif "mlm_itm" in checkpoint_url:
				lowerCamelCase					:      Tuple          =   True
				lowerCamelCase					:      Any          =   ViltForMaskedLM(a_      )
		else:
				raise ValueError('Unknown model type'      )

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

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

		# Define processor
		lowerCamelCase					:      Optional[int]          =   ViltImageProcessor(size=384      )
		lowerCamelCase					:      Any          =   BertTokenizer.from_pretrained('bert-base-uncased'      )
		lowerCamelCase					:      List[str]          =   ViltProcessor(a_,						a_      )

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

		# Verify outputs
		if mlm_model:
				lowerCamelCase					:      Dict          =   torch.Size([1, 11, 3_0522]      )
				lowerCamelCase					:      str          =   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],						a_,						atol=1E-4      )

				# verify masked token prediction equals "cats"
				lowerCamelCase					:      int          =   outputs.logits[0, 4, :].argmax(-1      ).item()
				assert tokenizer.decode([predicted_id]      ) == "cats"
		elif vqa_model:
				lowerCamelCase					:      int          =   torch.Size([1, 3129]      )
				lowerCamelCase					:      str          =   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],						a_,						atol=1E-4      )
				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, 0, :3],						a_,						atol=1E-4      )

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

		Path(a_      ).mkdir(exist_ok=a_      )
		print(F"""Saving model and processor to {pytorch_dump_folder_path}"""      )
		model.save_pretrained(a_      )
		processor.save_pretrained(a_      )


if __name__ == "__main__":
					_A						=			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.'
					)

					_A						=			parser.parse_args()
					convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








"""simple docstring"""

def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return str(a_      ) == str(a_      )[::-1]
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return int(a_      ) + int(str(a_      )[::-1]      )
def 		UpperCAmelCase (		a_ = 1_0000      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   []
		for num in range(1,						a_      ):
				lowerCamelCase					:      List[str]          =   0
				lowerCamelCase					:      Union[str, Any]          =   num
				while iterations < 50:
						lowerCamelCase					:      Optional[int]          =   sum_reverse(a_      )
						iterations += 1
						if is_palindrome(a_      ):
								break
				else:
						lychrel_nums.append(a_      )
		return len(a_      )


if __name__ == "__main__":
					print(F"""{solution() = }""")






