
import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							[
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

lowerCamelCase__	=							[
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]


def 							__A(lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		return sd


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=rename_keys_prefix )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	OrderedDict()
		_UpperCamelCase						    =	torch.arange(config.max_position_embeddings ).expand((1, -1) )
		# detector_d = OrderedDict()
		for key in d:
				if "detector" in key:
						# detector_d[key.replace('detector.','')] = d[key]
						continue
				_UpperCamelCase						    =	key
				for name_pair in rename_keys_prefix:
						_UpperCamelCase						    =	new_key.replace(name_pair[0]						,  name_pair[1] )
				_UpperCamelCase						    =	d[key]
				if key == "bert.cls.predictions.decoder.weight":
						# Old bert code didn't have `decoder.bias`, but was added separately
						_UpperCamelCase						    =	new_d["""cls.predictions.bias"""]
		return new_d


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Any:

		"""simple docstring"""






		assert (
		    checkpoint_path.split("""/""" )[-1] in ACCEPTABLE_CHECKPOINTS
		), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'

		# Get Config
		if "pre" in checkpoint_path:
				_UpperCamelCase						    =	"""pretraining"""
				if "vcr" in checkpoint_path:
						_UpperCamelCase						    =	{"""visual_embedding_dim""": 5_1_2}
				elif "vqa_advanced" in checkpoint_path:
						_UpperCamelCase						    =	{"""visual_embedding_dim""": 2_0_4_8}
				elif "vqa" in checkpoint_path:
						_UpperCamelCase						    =	{"""visual_embedding_dim""": 2_0_4_8}
				elif "nlvr" in checkpoint_path:
						_UpperCamelCase						    =	{"""visual_embedding_dim""": 1_0_2_4}
				else:
						raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
		else:
				if "vcr" in checkpoint_path:
						_UpperCamelCase						    =	{"""visual_embedding_dim""": 5_1_2}
						_UpperCamelCase						    =	"""multichoice"""
				elif "vqa_advanced" in checkpoint_path:
						_UpperCamelCase						    =	{"""visual_embedding_dim""": 2_0_4_8}
						_UpperCamelCase						    =	"""vqa_advanced"""
				elif "vqa" in checkpoint_path:
						_UpperCamelCase						    =	{"""visual_embedding_dim""": 2_0_4_8, """num_labels""": 3_1_2_9}
						_UpperCamelCase						    =	"""vqa"""
				elif "nlvr" in checkpoint_path:
						_UpperCamelCase						    =	{
						    """visual_embedding_dim""": 1_0_2_4,
						    """num_labels""": 2,
						}
						_UpperCamelCase						    =	"""nlvr"""

		_UpperCamelCase						    =	VisualBertConfig(**lowerCAmelCase )

		# Load State Dict
		_UpperCamelCase						    =	load_state_dict(lowerCAmelCase )

		_UpperCamelCase						    =	get_new_dict(lowerCAmelCase						,  lowerCAmelCase )

		if model_type == "pretraining":
				_UpperCamelCase						    =	VisualBertForPreTraining(lowerCAmelCase )
		elif model_type == "vqa":
				_UpperCamelCase						    =	VisualBertForQuestionAnswering(lowerCAmelCase )
		elif model_type == "nlvr":
				_UpperCamelCase						    =	VisualBertForVisualReasoning(lowerCAmelCase )
		elif model_type == "multichoice":
				_UpperCamelCase						    =	VisualBertForMultipleChoice(lowerCAmelCase )

		model.load_state_dict(lowerCAmelCase )
		# Save Checkpoints
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
		lowerCamelCase__	=							parser.parse_args()
		convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		["image_processor", "tokenizer"]
						UpperCamelCase_							: Union[str, Any]	=		"ViltImageProcessor"
						UpperCamelCase_							: List[str]	=		("BertTokenizer", "BertTokenizerFast")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""       ,							a       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""				)
								if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""				)

								super().__init__(a       ,							a				)
								_UpperCamelCase						    =	self.image_processor

						def __call__(			self       ,							a       ,							a = None       ,							a = True       ,							a = False       ,							a = None       ,							a = None       ,							a = 0       ,							a = None       ,							a = None       ,							a = None       ,							a = False       ,							a = False       ,							a = False       ,							a = False       ,							a = True       ,							a = None       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer(
								    text=a       ,							add_special_tokens=a       ,							padding=a       ,							truncation=a       ,							max_length=a       ,							stride=a       ,							pad_to_multiple_of=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							return_overflowing_tokens=a       ,							return_special_tokens_mask=a       ,							return_offsets_mapping=a       ,							return_length=a       ,							verbose=a       ,							return_tensors=a       ,							**a       ,							)
								# add pixel_values + pixel_mask
								_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a				)
								encoding.update(a				)

								return encoding

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								return self.tokenizer.batch_decode(*a       ,							**a				)

						def  A_				(			self       ,							*a       ,							**a				)					->      int:


								'''simple docstring'''

								return self.tokenizer.decode(*a       ,							**a				)

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer.model_input_names
								_UpperCamelCase						    =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							a       ,							)
								return self.image_processor_class







						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,							a       ,							)
								return self.image_processor



