

'''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()
UpperCamelCase_							=					logging.get_logger(__name__)

UpperCamelCase_							=					[
    ("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"),
]

UpperCamelCase_							=					[
    "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 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Optional[int]:
						lowercase   :       int								=torch.load(__magic_name__      ,   map_location='''cpu'''	)
						return sd






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






@torch.no_grad()
def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Any	)							->     Any:
						assert (
						    checkpoint_path.split('''/'''	)[-1] in ACCEPTABLE_CHECKPOINTS
						), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

						# Get Config
						if "pre" in checkpoint_path:
												lowercase   :       Any								='pretraining'
												if "vcr" in checkpoint_path:
																		lowercase   :       Dict								={'visual_embedding_dim': 512}
												elif "vqa_advanced" in checkpoint_path:
																		lowercase   :       Tuple								={'visual_embedding_dim': 2048}
												elif "vqa" in checkpoint_path:
																		lowercase   :       List[Any]								={'visual_embedding_dim': 2048}
												elif "nlvr" in checkpoint_path:
																		lowercase   :       Optional[int]								={'visual_embedding_dim': 1024}
												else:
																		raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''	)
						else:
												if "vcr" in checkpoint_path:
																		lowercase   :       Tuple								={'visual_embedding_dim': 512}
																		lowercase   :       List[Any]								='multichoice'
												elif "vqa_advanced" in checkpoint_path:
																		lowercase   :       Tuple								={'visual_embedding_dim': 2048}
																		lowercase   :       str								='vqa_advanced'
												elif "vqa" in checkpoint_path:
																		lowercase   :       List[str]								={'visual_embedding_dim': 2048, 'num_labels': 3129}
																		lowercase   :       Optional[Any]								='vqa'
												elif "nlvr" in checkpoint_path:
																		lowercase   :       int								={
																		    'visual_embedding_dim': 1024,
																		    'num_labels': 2,
																		}
																		lowercase   :       List[Any]								='nlvr'

						lowercase   :       Any								=VisualBertConfig(**__magic_name__	)

						# Load State Dict
						lowercase   :       Any								=load_state_dict(__magic_name__	)

						lowercase   :       Optional[Any]								=get_new_dict(__magic_name__      ,   __magic_name__	)

						if model_type == "pretraining":
												lowercase   :       Union[str, Any]								=VisualBertForPreTraining(__magic_name__	)
						elif model_type == "vqa":
												lowercase   :       int								=VisualBertForQuestionAnswering(__magic_name__	)
						elif model_type == "nlvr":
												lowercase   :       Union[str, Any]								=VisualBertForVisualReasoning(__magic_name__	)
						elif model_type == "multichoice":
												lowercase   :       List[Any]								=VisualBertForMultipleChoice(__magic_name__	)

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


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



'''simple docstring'''





from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Dict:
						for param in module.parameters():
												lowercase   :       List[str]								=False






def 							_lowerCAmelCase (      )							->     List[str]:
						lowercase   :       Dict								='''cuda''' if torch.cuda.is_available() else '''cpu'''
						if torch.backends.mps.is_available() and torch.backends.mps.is_built():
												lowercase   :       Optional[int]								='''mps'''
						if device == "mps":
												print(
												    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
												    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
												    ''' with generations.'''	)
						return device






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]	)							->     str:
						lowercase   :       Optional[int]								=plt.imshow(__magic_name__	)
						fig.axes.get_xaxis().set_visible(__magic_name__	)
						fig.axes.get_yaxis().set_visible(__magic_name__	)
						plt.show()







def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Any								=datetime.now()
						lowercase   :       Dict								=current_time.strftime('''%H:%M:%S'''	)
						return timestamp





