






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					(     _lowercase ):

					'''simple docstring'''







					_A								= torch.load(_lowercase					,			map_location='''cpu''' )
					return sd




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=rename_keys_prefix ):

					'''simple docstring'''







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




@torch.no_grad()
def      __A					(     _lowercase					,			_lowercase ):

					'''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:
										_A								= '''pretraining'''
										if "vcr" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 5_12}
										elif "vqa_advanced" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48}
										elif "vqa" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48}
										elif "nlvr" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 10_24}
										else:
															raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
					else:
										if "vcr" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 5_12}
															_A								= '''multichoice'''
										elif "vqa_advanced" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48}
															_A								= '''vqa_advanced'''
										elif "vqa" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48, '''num_labels''': 31_29}
															_A								= '''vqa'''
										elif "nlvr" in checkpoint_path:
															_A								= {
															    '''visual_embedding_dim''': 10_24,
															    '''num_labels''': 2,
															}
															_A								= '''nlvr'''

					_A								= VisualBertConfig(**_lowercase )

					# Load State Dict
					_A								= load_state_dict(_lowercase )

					_A								= get_new_dict(_lowercase					,			_lowercase )

					if model_type == "pretraining":
										_A								= VisualBertForPreTraining(_lowercase )
					elif model_type == "vqa":
										_A								= VisualBertForQuestionAnswering(_lowercase )
					elif model_type == "nlvr":
										_A								= VisualBertForVisualReasoning(_lowercase )
					elif model_type == "multichoice":
										_A								= VisualBertForMultipleChoice(_lowercase )

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


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)











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A      =				logging.get_logger(__name__)

__A      =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"roberta"

							def __init__(  self:   Dict		,     __A:   int=5_02_65		,     __A:   Union[str, Any]=7_68		,     __A:   Union[str, Any]=12		,     __A:   str=12		,     __A:   int=30_72		,     __A:   str="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   int=0.1		,     __A:   Optional[int]=5_12		,     __A:   Union[str, Any]=2		,     __A:   str=0.02		,     __A:   str=1e-12		,     __A:   Any=1		,     __A:   str=0		,     __A:   Any=2		,     __A:   Optional[int]="absolute"		,     __A:   Optional[Any]=True		,     __A:   Union[str, Any]=None		,     **__A:   List[str]		,     )    ->				Dict:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= use_cache
												_A								= classifier_dropout





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Dict   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )



