
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 							a		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False		,  snake_case__:   Union[str, Any]=False		,  snake_case__:   str=False   ):


				'''simple docstring'''
				lowercase_	    =   []
				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		(				snake_case__:   Any		,  snake_case__:   Union[str, Any]   ):


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




def 							a		(				snake_case__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   ['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(snake_case__		,  snake_case__   )




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   List[Any]		,  snake_case__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   dct.pop(snake_case__   )
				lowercase_	    =   val




@torch.no_grad()
def 							a		(				snake_case__:   Any		,  snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				lowercase_	    =   ViltConfig(image_size=384		,  patch_size=32		,  tie_word_embeddings=snake_case__   )
				lowercase_	    =   False
				lowercase_	    =   False
				lowercase_	    =   False
				lowercase_	    =   False
				if "vqa" in checkpoint_url:
								lowercase_	    =   True
								lowercase_	    =   3_129
								lowercase_	    =   '''huggingface/label-files'''
								lowercase_	    =   '''vqa2-id2label.json'''
								lowercase_	    =   json.load(open(hf_hub_download(snake_case__		,  snake_case__		,  repo_type='''dataset'''   )		,  '''r'''   )   )
								lowercase_	    =   {int(snake_case__   ): v for k, v in idalabel.items()}
								lowercase_	    =   idalabel
								lowercase_	    =   {v: k for k, v in idalabel.items()}
								lowercase_	    =   ViltForQuestionAnswering(snake_case__   )
				elif "nlvr" in checkpoint_url:
								lowercase_	    =   True
								lowercase_	    =   2
								lowercase_	    =   {0: '''False''', 1: '''True'''}
								lowercase_	    =   {v: k for k, v in config.idalabel.items()}
								lowercase_	    =   3
								lowercase_	    =   ViltForImagesAndTextClassification(snake_case__   )
				elif "irtr" in checkpoint_url:
								lowercase_	    =   True
								lowercase_	    =   ViltForImageAndTextRetrieval(snake_case__   )
				elif "mlm_itm" in checkpoint_url:
								lowercase_	    =   True
								lowercase_	    =   ViltForMaskedLM(snake_case__   )
				else:
								raise ValueError('''Unknown model type'''   )

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

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

				# Define processor
				lowercase_	    =   ViltImageProcessor(size=384   )
				lowercase_	    =   BertTokenizer.from_pretrained('''bert-base-uncased'''   )
				lowercase_	    =   ViltProcessor(snake_case__		,  snake_case__   )

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

				# Verify outputs
				if mlm_model:
								lowercase_	    =   torch.Size([1, 11, 30_522]   )
								lowercase_	    =   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]		,  snake_case__		,  atol=1e-4   )

								# verify masked token prediction equals "cats"
								lowercase_	    =   outputs.logits[0, 4, :].argmax(-1   ).item()
								assert tokenizer.decode([predicted_id]   ) == "cats"
				elif vqa_model:
								lowercase_	    =   torch.Size([1, 3_129]   )
								lowercase_	    =   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]		,  snake_case__		,  atol=1e-4   )
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]		,  snake_case__		,  atol=1e-4   )

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

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


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)






def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				if not isinstance(snake_case__		,  snake_case__   ):
								raise ValueError('''Input must be an integer'''   )
				if input_num <= 0:
								raise ValueError('''Input must be positive'''   )
				return sum(
				    divisor for divisor in range(1		,  input_num // 2 + 1   ) if input_num % divisor == 0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




