



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()
_lowercase     							=		logging.get_logger(__name__)

_lowercase     							=		[
    ("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"),
]

_lowercase     							=		[
    "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				(UpperCamelCase		:      List[str] )  ->Union[str, Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		torch.load(UpperCAmelCase__      ,			map_location="""cpu""" )
			return sd






def      _A				(UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      List[str]=rename_keys_prefix )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		OrderedDict()
			lowerCamelCase__ :      List[str]								=		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
						lowerCamelCase__ :      Dict								=		key
						for name_pair in rename_keys_prefix:
									lowerCamelCase__ :      Any								=		new_key.replace(name_pair[0]      ,			name_pair[1] )
						lowerCamelCase__ :      Dict								=		d[key]
						if key == "bert.cls.predictions.decoder.weight":
									# Old bert code didn't have `decoder.bias`, but was added separately
									lowerCamelCase__ :      str								=		new_d["""cls.predictions.bias"""]
			return new_d






@torch.no_grad()
def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Optional[int] )  ->Optional[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:
						lowerCamelCase__ :      List[str]								=		"""pretraining"""
						if "vcr" in checkpoint_path:
									lowerCamelCase__ :      List[Any]								=		{"""visual_embedding_dim""": 512}
						elif "vqa_advanced" in checkpoint_path:
									lowerCamelCase__ :      Any								=		{"""visual_embedding_dim""": 2048}
						elif "vqa" in checkpoint_path:
									lowerCamelCase__ :      str								=		{"""visual_embedding_dim""": 2048}
						elif "nlvr" in checkpoint_path:
									lowerCamelCase__ :      List[Any]								=		{"""visual_embedding_dim""": 1024}
						else:
									raise NotImplementedError(f"No implementation found for `{checkpoint_path}`." )
			else:
						if "vcr" in checkpoint_path:
									lowerCamelCase__ :      Any								=		{"""visual_embedding_dim""": 512}
									lowerCamelCase__ :      Dict								=		"""multichoice"""
						elif "vqa_advanced" in checkpoint_path:
									lowerCamelCase__ :      Optional[int]								=		{"""visual_embedding_dim""": 2048}
									lowerCamelCase__ :      List[Any]								=		"""vqa_advanced"""
						elif "vqa" in checkpoint_path:
									lowerCamelCase__ :      int								=		{"""visual_embedding_dim""": 2048, """num_labels""": 3129}
									lowerCamelCase__ :      Tuple								=		"""vqa"""
						elif "nlvr" in checkpoint_path:
									lowerCamelCase__ :      Any								=		{
									    """visual_embedding_dim""": 1024,
									    """num_labels""": 2,
									}
									lowerCamelCase__ :      int								=		"""nlvr"""

			lowerCamelCase__ :      List[Any]								=		VisualBertConfig(**UpperCAmelCase__ )

			# Load State Dict
			lowerCamelCase__ :      str								=		load_state_dict(UpperCAmelCase__ )

			lowerCamelCase__ :      str								=		get_new_dict(UpperCAmelCase__      ,			UpperCAmelCase__ )

			if model_type == "pretraining":
						lowerCamelCase__ :      Optional[Any]								=		VisualBertForPreTraining(UpperCAmelCase__ )
			elif model_type == "vqa":
						lowerCamelCase__ :      Optional[int]								=		VisualBertForQuestionAnswering(UpperCAmelCase__ )
			elif model_type == "nlvr":
						lowerCamelCase__ :      Optional[Any]								=		VisualBertForVisualReasoning(UpperCAmelCase__ )
			elif model_type == "multichoice":
						lowerCamelCase__ :      Tuple								=		VisualBertForMultipleChoice(UpperCAmelCase__ )

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


if __name__ == "__main__":
				_lowercase     							=		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.''')
				_lowercase     							=		parser.parse_args()
				convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowercase     							=						TypeVar('''T''')
class 				__A					( Generic[T]      ):





			def __init__(self							,    __magic_name__ = True				):
						lowerCamelCase__ :      dict[T, list[T]]								=		{}  # dictionary of lists
						lowerCamelCase__ :      List[str]								=		directed





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):

						if not self.directed:  # For undirected graphs
									# if both source vertex and destination vertex are both present in the
									# adjacency list, add destination vertex to source vertex list of adjacent
									# vertices and add source vertex to destination vertex list of adjacent
									# vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												self.adj_list[destination_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination vertex
									# to source vertex list of adjacent vertices, then create a new vertex with
									# destination vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      int								=		[source_vertex]
									# if only destination vertex is present in adjacency list, add source vertex
									# to destination vertex list of adjacent vertices, then create a new vertex
									# with source vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif destination_vertex in self.adj_list:
												self.adj_list[destination_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Dict								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and assign a list
									# containing the destination vertex as it's first adjacent vertex also
									# create a new vertex with destination vertex as key and assign a list
									# containing the source vertex as it's first adjacent vertex.
									else:
												lowerCamelCase__ :      Any								=		[destination_vertex]
												lowerCamelCase__ :      Optional[Any]								=		[source_vertex]
						else:  # For directed graphs
									# if both source vertex and destination vertex are present in adjacency
									# list, add destination vertex to source vertex list of adjacent vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination
									# vertex to source vertex list of adjacent vertices and create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Optional[Any]								=		[]
									# if only destination vertex is present in adjacency list, create a new
									# vertex with source vertex as key and assign a list containing destination
									# vertex as first adjacent vertex
									elif destination_vertex in self.adj_list:
												lowerCamelCase__ :      Tuple								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and a list containing
									# destination vertex as it's first adjacent vertex. Then create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									else:
												lowerCamelCase__ :      Optional[int]								=		[destination_vertex]
												lowerCamelCase__ :      List[Any]								=		[]

						return self





			def __repr__(self				):
						return pformat(self.adj_list				)



