



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					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : int=False      ,lowerCAmelCase_       : List[Any]=False      ,lowerCAmelCase_       : Optional[int]=False    )				->			List[str]:






						'''simple docstring'''



						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_       : Tuple      ,lowerCAmelCase_       : Tuple    )				->			Optional[int]:






						'''simple docstring'''



						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_       : Tuple    )				->			Union[str, Any]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Union[str, Any]    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       dct.pop(lowerCAmelCase_    )
						UpperCAmelCase_=       val



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : List[str]    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       ViltConfig(image_size=3_84      ,patch_size=32      ,tie_word_embeddings=lowerCAmelCase_    )
						UpperCAmelCase_=       False
						UpperCAmelCase_=       False
						UpperCAmelCase_=       False
						UpperCAmelCase_=       False
						if "vqa" in checkpoint_url:
												UpperCAmelCase_=       True
												UpperCAmelCase_=       31_29
												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_84    )
						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, 11, 3_05_22]    )
												UpperCAmelCase_=       torch.tensor([-12.5_061, -12.5_123, -12.5_174]    )
												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, 31_29]    )
												UpperCAmelCase_=       torch.tensor([-15.9_495, -18.1_472, -10.3_041]    )
												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([-2.8_721, 2.1_291]    )
												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__":
	__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)









import warnings
from functools import wraps
from typing import Callable



def 	__a					(   lowerCAmelCase_       : Callable    )				->			Callable:






						'''simple docstring'''



						@wraps(lowerCAmelCase_    )
						def _inner_fn(*lowerCAmelCase_       : List[Any]      ,**lowerCAmelCase_       : Tuple    ):
												warnings.warn(
												    (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")      ,lowerCAmelCase_      ,)
												return fn(*lowerCAmelCase_      ,**lowerCAmelCase_    )

						return _inner_fn




