




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    (			UpperCamelCase				,       UpperCamelCase=False				,       UpperCamelCase=False				,       UpperCamelCase=False							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					[]
			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    (			UpperCamelCase				,       UpperCamelCase							)			->			List[str]:


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

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase	: Any		=					["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase				,       UpperCamelCase							)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					dct.pop(UpperCamelCase							)
			__UpperCAmelCase	: Optional[int]		=					val

@torch.no_grad()
def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


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

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

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

			# Define processor
			__UpperCAmelCase	: Optional[Any]		=					ViltImageProcessor(size=384							)
			__UpperCAmelCase	: Optional[Any]		=					BertTokenizer.from_pretrained("bert-base-uncased"							)
			__UpperCAmelCase	: Any		=					ViltProcessor(UpperCamelCase				,       UpperCamelCase							)

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

			# Verify outputs
			if mlm_model:
						__UpperCAmelCase	: List[Any]		=					torch.Size([1, 11, 3_0522]							)
						__UpperCAmelCase	: List[str]		=					torch.tensor([-12.5061, -12.5123, -12.5174]							)
						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, 0, :3]				,       UpperCamelCase				,       atol=1e-4							)

						# verify masked token prediction equals "cats"
						__UpperCAmelCase	: Optional[int]		=					outputs.logits[0, 4, :].argmax(-1							).item()
						assert tokenizer.decode([predicted_id]							) == "cats"
			elif vqa_model:
						__UpperCAmelCase	: List[str]		=					torch.Size([1, 3129]							)
						__UpperCAmelCase	: Union[str, Any]		=					torch.tensor([-15.9495, -18.1472, -10.3041]							)
						assert torch.allclose(outputs.logits[0, :3]				,       UpperCamelCase				,       atol=1e-4							)
						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, 0, :3]				,       UpperCamelCase				,       atol=1e-4							)

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

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


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    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						__UpperCAmelCase	: Union[str, Any]		=					_modexpt(UpperCamelCase				,       exponent // 2				,       UpperCamelCase							) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase				,       exponent - 1				,       UpperCamelCase							)) % modulo_value

def 					_UpperCamelCase    (			UpperCamelCase = 1777				,       UpperCamelCase = 1855				,       UpperCamelCase = 8							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					base
			for _ in range(1				,       UpperCamelCase							):
						__UpperCAmelCase	: str		=					_modexpt(UpperCamelCase				,       UpperCamelCase				,       10**digits							)

			return result


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


