






"""simple docstring"""
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 			__UpperCamelCase	(		snake_case__					):
	A_		:						Union[str, Any]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)
	return sd






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






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	assert (
	    checkpoint_path.split("""/"""					)[-1] in ACCEPTABLE_CHECKPOINTS
	), F"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

	# Get Config
	if "pre" in checkpoint_path:
		A_		:						Any	    =   """pretraining"""
		if "vcr" in checkpoint_path:
			A_		:						Optional[Any]	    =   {"""visual_embedding_dim""": 512}
		elif "vqa_advanced" in checkpoint_path:
			A_		:						List[Any]	    =   {"""visual_embedding_dim""": 2_048}
		elif "vqa" in checkpoint_path:
			A_		:						List[Any]	    =   {"""visual_embedding_dim""": 2_048}
		elif "nlvr" in checkpoint_path:
			A_		:						Any	    =   {"""visual_embedding_dim""": 1_024}
		else:
			raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""					)
	else:
		if "vcr" in checkpoint_path:
			A_		:						str	    =   {"""visual_embedding_dim""": 512}
			A_		:						List[Any]	    =   """multichoice"""
		elif "vqa_advanced" in checkpoint_path:
			A_		:						Optional[Any]	    =   {"""visual_embedding_dim""": 2_048}
			A_		:						Optional[Any]	    =   """vqa_advanced"""
		elif "vqa" in checkpoint_path:
			A_		:						Any	    =   {"""visual_embedding_dim""": 2_048, """num_labels""": 3_129}
			A_		:						List[str]	    =   """vqa"""
		elif "nlvr" in checkpoint_path:
			A_		:						Tuple	    =   {
			    """visual_embedding_dim""": 1_024,
			    """num_labels""": 2,
			}
			A_		:						Tuple	    =   """nlvr"""

	A_		:						Tuple	    =   VisualBertConfig(**snake_case__					)

	# Load State Dict
	A_		:						int	    =   load_state_dict(snake_case__					)

	A_		:						Union[str, Any]	    =   get_new_dict(snake_case__   ,   snake_case__					)

	if model_type == "pretraining":
		A_		:						Optional[int]	    =   VisualBertForPreTraining(snake_case__					)
	elif model_type == "vqa":
		A_		:						List[str]	    =   VisualBertForQuestionAnswering(snake_case__					)
	elif model_type == "nlvr":
		A_		:						str	    =   VisualBertForVisualReasoning(snake_case__					)
	elif model_type == "multichoice":
		A_		:						str	    =   VisualBertForMultipleChoice(snake_case__					)

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


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)









"""simple docstring"""
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    "The `inpainting.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionInpaintPipeline` instead."
)

