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





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			dct.pop(lowerCamelCase__    )
				lowerCamelCase_						=			val





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

				# load state_dict of original model, remove and rename some keys
				lowerCamelCase_						=			torch.hub.load_state_dict_from_url(lowerCamelCase__	, map_location="cpu"    )["state_dict"]
				lowerCamelCase_						=			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:
								lowerCamelCase_						=			["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:
								lowerCamelCase_	,    lowerCamelCase_						=			model.load_state_dict(lowerCamelCase__	, strict=lowerCamelCase__    )
								assert missing_keys == ["mlm_score.decoder.bias"]
				else:
								model.load_state_dict(lowerCamelCase__    )

				# Define processor
				lowerCamelCase_						=			ViltImageProcessor(size=3_8_4    )
				lowerCamelCase_						=			BertTokenizer.from_pretrained("bert-base-uncased"    )
				lowerCamelCase_						=			ViltProcessor(lowerCamelCase__	, lowerCamelCase__    )

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

				# Verify outputs
				if mlm_model:
								lowerCamelCase_						=			torch.Size([1, 1_1, 3_0_5_2_2]    )
								lowerCamelCase_						=			torch.tensor([-12.50_61, -12.51_23, -12.51_74]    )
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]	, lowerCamelCase__	, atol=1e-4    )

								# verify masked token prediction equals "cats"
								lowerCamelCase_						=			outputs.logits[0, 4, :].argmax(-1    ).item()
								assert tokenizer.decode([predicted_id]    ) == "cats"
				elif vqa_model:
								lowerCamelCase_						=			torch.Size([1, 3_1_2_9]    )
								lowerCamelCase_						=			torch.tensor([-15.94_95, -18.14_72, -10.30_41]    )
								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"
								lowerCamelCase_						=			outputs.logits.argmax(-1    ).item()
								assert model.config.idalabel[predicted_idx] == "2"
				elif nlvr_model:
								lowerCamelCase_						=			torch.Size([1, 2]    )
								lowerCamelCase_						=			torch.tensor([-2.87_21, 2.12_91]    )
								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 os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__A				=logging.get_logger(__name__)

__A				=list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__A				=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class     _SCREAMING_SNAKE_CASE     :
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'Model type selected in the list: ' + ', '.join(snake_case_						)}						)
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}						)
	lowerCAmelCase__         = field(
	    default=1_28			,       metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }			,       )
	lowerCAmelCase__         = field(
	    default=1_28			,       metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}			,       )
	lowerCAmelCase__         = field(
	    default=64			,       metadata={
	        'help': (
	            'The maximum number of tokens for the question. Questions longer than this will '
	            'be truncated to this length.'
	        )
	    }			,       )
	lowerCAmelCase__         = field(
	    default=30			,       metadata={
	        'help': (
	            'The maximum length of an answer that can be generated. This is needed because the start '
	            'and end predictions are not conditioned on one another.'
	        )
	    }			,       )
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'Overwrite the cached training and evaluation sets'}						)
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}						)
	lowerCAmelCase__         = field(
	    default=0.0			,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}						)
	lowerCAmelCase__         = field(
	    default=20			,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}						)
	lowerCAmelCase__         = field(
	    default=0			,       metadata={
	        'help': (
	            'language id of input for language-specific xlm models (see'
	            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
	        )
	    }			,       )
	lowerCAmelCase__         = field(default=1			,       metadata={'help': 'multiple threads for converting example to features'}						)




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'train'
	lowerCAmelCase__         = 'dev'




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 42
	lowerCAmelCase__         = 42
	lowerCAmelCase__         = 42
	lowerCAmelCase__         = 42



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase = None						,  lowercase = Split.train						,  lowercase = False						,  lowercase = None						,  lowercase = "pt"						,  )				->       List[str]:
					lowerCamelCase_						=			args
					lowerCamelCase_						=			is_language_sensitive
					lowerCamelCase_						=			SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
					if isinstance(lowercase						,  lowercase ):
									try:
													lowerCamelCase_						=			Split[mode]
									except KeyError:
													raise KeyError("mode is not a valid split name" )
					lowerCamelCase_						=			mode
					# Load data features from cache or dataset file
					lowerCamelCase_						=			"v2" if args.version_2_with_negative else "v1"
					lowerCamelCase_						=			os.path.join(
					    cache_dir if cache_dir is not None else args.data_dir						,  f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'						,  )

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					lowerCamelCase_						=			cached_features_file + ".lock"
					with FileLock(lowercase ):
									if os.path.exists(lowercase ) and not args.overwrite_cache:
													lowerCamelCase_						=			time.time()
													lowerCamelCase_						=			torch.load(lowercase )

													# Legacy cache files have only features, while new cache files
													# will have dataset and examples also.
													lowerCamelCase_						=			self.old_features["features"]
													lowerCamelCase_						=			self.old_features.get("dataset"						,  lowercase )
													lowerCamelCase_						=			self.old_features.get("examples"						,  lowercase )
													logger.info(
													    f'Loading features from cached file {cached_features_file} [took %.3f s]'						,  time.time() - start )

													if self.dataset is None or self.examples is None:
																	logger.warning(
																	    f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
																	    " future run" )
									else:
													if mode == Split.dev:
																	lowerCamelCase_						=			self.processor.get_dev_examples(args.data_dir )
													else:
																	lowerCamelCase_						=			self.processor.get_train_examples(args.data_dir )

													lowerCamelCase_	,    lowerCamelCase_						=			squad_convert_examples_to_features(
													    examples=self.examples						,  tokenizer=lowercase						,  max_seq_length=args.max_seq_length						,  doc_stride=args.doc_stride						,  max_query_length=args.max_query_length						,  is_training=mode == Split.train						,  threads=args.threads						,  return_dataset=lowercase						,  )

													lowerCamelCase_						=			time.time()
													torch.save(
													    {"features": self.features, "dataset": self.dataset, "examples": self.examples}						,  lowercase						,  )
													# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
													logger.info(
													    f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )



	def __len__(    self )				->       Tuple:
					return len(self.features )



	def __getitem__(    self						,  lowercase )				->       Dict[str, torch.Tensor]:
					# Convert to Tensors and build dataset
					lowerCamelCase_						=			self.features[i]

					lowerCamelCase_						=			torch.tensor(feature.input_ids						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.attention_mask						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.token_type_ids						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.cls_index						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.p_mask						,  dtype=torch.float )
					lowerCamelCase_						=			torch.tensor(feature.is_impossible						,  dtype=torch.float )

					lowerCamelCase_						=			{
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					    "token_type_ids": token_type_ids,
					}

					if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
									del inputs["token_type_ids"]

					if self.args.model_type in ["xlnet", "xlm"]:
									inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
									if self.args.version_2_with_negative:
													inputs.update({"is_impossible": is_impossible} )
									if self.is_language_sensitive:
													inputs.update({"langs": (torch.ones(input_ids.shape						,  dtype=torch.intaa ) * self.args.lang_id)} )

					if self.mode == Split.train:
									lowerCamelCase_						=			torch.tensor(feature.start_position						,  dtype=torch.long )
									lowerCamelCase_						=			torch.tensor(feature.end_position						,  dtype=torch.long )
									inputs.update({"start_positions": start_positions, "end_positions": end_positions} )

					return inputs


