





"""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()
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     __A      (			a_						:Tuple)		->		Tuple:
				__a :						Tuple			       =  torch.load(a_   ,       map_location='''cpu''')
				return sd

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

@torch.no_grad()
def     __A      (			a_						:Tuple   ,       a_						:Optional[int])		->		str:
				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 :						Tuple			       =  {'''visual_embedding_dim''': 5_12}
								elif "vqa_advanced" in checkpoint_path:
												__a :						Union[str, Any]			       =  {'''visual_embedding_dim''': 20_48}
								elif "vqa" in checkpoint_path:
												__a :						Tuple			       =  {'''visual_embedding_dim''': 20_48}
								elif "nlvr" in checkpoint_path:
												__a :						str			       =  {'''visual_embedding_dim''': 10_24}
								else:
												raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`.""")
				else:
								if "vcr" in checkpoint_path:
												__a :						Dict			       =  {'''visual_embedding_dim''': 5_12}
												__a :						List[str]			       =  '''multichoice'''
								elif "vqa_advanced" in checkpoint_path:
												__a :						Any			       =  {'''visual_embedding_dim''': 20_48}
												__a :						Any			       =  '''vqa_advanced'''
								elif "vqa" in checkpoint_path:
												__a :						Tuple			       =  {'''visual_embedding_dim''': 20_48, '''num_labels''': 31_29}
												__a :						Tuple			       =  '''vqa'''
								elif "nlvr" in checkpoint_path:
												__a :						List[Any]			       =  {
												    '''visual_embedding_dim''': 10_24,
												    '''num_labels''': 2,
												}
												__a :						str			       =  '''nlvr'''

				__a :						Any			       =  VisualBertConfig(**a_)

				# Load State Dict
				__a :						Union[str, Any]			       =  load_state_dict(a_)

				__a :						List[str]			       =  get_new_dict(a_   ,       a_)

				if model_type == "pretraining":
								__a :						Optional[Any]			       =  VisualBertForPreTraining(a_)
				elif model_type == "vqa":
								__a :						Union[str, Any]			       =  VisualBertForQuestionAnswering(a_)
				elif model_type == "nlvr":
								__a :						Any			       =  VisualBertForVisualReasoning(a_)
				elif model_type == "multichoice":
								__a :						str			       =  VisualBertForMultipleChoice(a_)

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


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)










"""simple docstring"""






import copy
import re




class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  '''hp'''
					__lowerCAmelCase  =  {}
					__lowerCAmelCase  =  None







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  prefix
									__a :						List[str]			       =  defaults
									cls.build_naming_info()







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									if len(_UpperCAmelCase     ) == 0:
													return ""
									__a :						Optional[int]			       =  None
									if any(char.isdigit() for char in word     ):
													raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number"""     )
									if word in info["short_word"]:
													return info["short_word"][word]
									for prefix_len in range(1	,			len(_UpperCAmelCase     ) + 1     ):
													__a :						str			       =  word[:prefix_len]
													if prefix in info["reverse_short_word"]:
																	continue
													else:
																	__a :						Tuple			       =  prefix
																	break

									if short_word is None:
													# Paranoid fallback
													def int_to_alphabetic(_UpperCAmelCase     ):
																	__a :						List[str]			       =  ''''''
																	while integer != 0:
																					__a :						Union[str, Any]			       =  chr(ord('''A'''     ) + integer % 10     ) + s
																					integer //= 10
																	return s

													__a :						Optional[int]			       =  0
													while True:
																	__a :						List[str]			       =  word + '''#''' + int_to_alphabetic(_UpperCAmelCase     )
																	if sword in info["reverse_short_word"]:
																					continue
																	else:
																					__a :						Optional[Any]			       =  sword
																					break

									__a :						List[str]			       =  short_word
									__a :						Dict			       =  word
									return short_word







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  param_name.split('''_'''     )

									__a :						str			       =  [TrialShortNamer.shortname_for_word(_UpperCAmelCase	,			_UpperCAmelCase     ) for word in words]

									# We try to create a separatorless short name, but if there is a collision we have to fallback
									# to a separated short name
									__a :						Dict			       =  ['''''', '''_''']

									for separator in separators:
													__a :						Union[str, Any]			       =  separator.join(_UpperCAmelCase     )
													if shortname not in info["reverse_short_param"]:
																	__a :						List[str]			       =  shortname
																	__a :						Union[str, Any]			       =  param_name
																	return shortname

									return param_name







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						int			       =  TrialShortNamer.shortname_for_key(_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  short_name
									__a :						Optional[Any]			       =  param_name







					@classmethod
					def  _lowerCamelCase		(						cls     ):
									if cls.NAMING_INFO is not None:
													return

									__a :						Optional[Any]			       =  {
									    '''short_word''': {},
									    '''reverse_short_word''': {},
									    '''short_param''': {},
									    '''reverse_short_param''': {},
									}

									__a :						List[str]			       =  list(cls.DEFAULTS.keys()     )

									for k in field_keys:
													cls.add_new_param_name(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						Optional[Any]			       =  info







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									cls.build_naming_info()
									assert cls.PREFIX is not None
									__a :						str			       =  [copy.copy(cls.PREFIX     )]

									for k, v in params.items():
													if k not in cls.DEFAULTS:
																	raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""     )
													if v == cls.DEFAULTS[k]:
																	# The default value is not added to the name
																	continue

													__a :						Union[str, Any]			       =  cls.NAMING_INFO['''short_param'''][k]

													if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						str			       =  1 if v else 0

													__a :						Tuple			       =  '''''' if isinstance(_UpperCAmelCase	,			(int, float)     ) else '''-'''
													__a :						Dict			       =  f"""{key}{sep}{v}"""
													name.append(_UpperCAmelCase     )

									return "_".join(_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  repr[len(cls.PREFIX     ) + 1 :]
									if repr == "":
													__a :						Tuple			       =  []
									else:
													__a :						str			       =  repr.split('''_'''     )

									__a :						Optional[Any]			       =  {}

									for value in values:
													if "-" in value:
																	__a       ,						__a :						List[Any]			       =  value.split('''-'''     )
													else:
																	__a :						int			       =  re.sub('''[0-9.]'''	,			''''''	,			_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  float(re.sub('''[^0-9.]'''	,			''''''	,			_UpperCAmelCase     )     )

													__a :						Dict			       =  cls.NAMING_INFO['''reverse_short_param'''][p_k]

													__a :						Union[str, Any]			       =  p_v

									for k in cls.DEFAULTS:
													if k not in parameters:
																	__a :						Optional[int]			       =  cls.DEFAULTS[k]

									return parameters