




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()
a													=			logging.get_logger(__name__)

a													=			[
    ("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"),
]

a													=			[
    "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        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Any:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )
						return sd




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=rename_keys_prefix       )						->				Any:
						_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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Dict:
						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(**snake_case       )

						# Load State Dict
						_UpperCAmelCase      =			load_state_dict(snake_case       )

						_UpperCAmelCase      =			get_new_dict(snake_case			,     snake_case       )

						if model_type == "pretraining":
												_UpperCAmelCase      =			VisualBertForPreTraining(snake_case       )
						elif model_type == "vqa":
												_UpperCAmelCase      =			VisualBertForQuestionAnswering(snake_case       )
						elif model_type == "nlvr":
												_UpperCAmelCase      =			VisualBertForVisualReasoning(snake_case       )
						elif model_type == "multichoice":
												_UpperCAmelCase      =			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__":
				a													=			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.")
				a													=			parser.parse_args()
				convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				bool:
						_UpperCAmelCase      =			len(snake_case       ) + 1
						_UpperCAmelCase      =			len(snake_case       ) + 1

						# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
						# length i of input_string matches with prefix string of length j of
						# given pattern.
						# "dp" stands for dynamic programming.
						_UpperCAmelCase      =			[[0 for i in range(snake_case       )] for j in range(snake_case       )]

						# since string of zero length match pattern of zero length
						_UpperCAmelCase      =			1

						# since pattern of zero length will never match with string of non-zero length
						for i in range(1			,     snake_case       ):
												_UpperCAmelCase      =			0

						# since string of zero length will match with pattern where there
						# is at least one * alternatively
						for j in range(1			,     snake_case       ):
												_UpperCAmelCase      =			dp[0][j - 2] if pattern[j - 1] == """*""" else 0

						# now using bottom-up approach to find for all remaining lengths
						for i in range(1			,     snake_case       ):
												for j in range(1			,     snake_case       ):
																		if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																								_UpperCAmelCase      =			dp[i - 1][j - 1]

																		elif pattern[j - 1] == "*":
																								if dp[i][j - 2] == 1:
																														_UpperCAmelCase      =			1
																								elif pattern[j - 2] in (input_string[i - 1], "."):
																														_UpperCAmelCase      =			dp[i - 1][j]
																								else:
																														_UpperCAmelCase      =			0
																		else:
																								_UpperCAmelCase      =			0

						return bool(dp[-1][-1]       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				a													=			"aab"
				a													=			"c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F'{input_string} matches the given pattern {pattern}')
				else:
								print(F'{input_string} does not match with the given pattern {pattern}')