


'''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()
__lowercase	:						Optional[Any]       =	logging.get_logger(__name__)

__lowercase	:						str       =	[
    ('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'),
]

__lowercase	:						List[Any]       =	[
    '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							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Dict					    =  torch.load(_SCREAMING_SNAKE_CASE						,      map_location='cpu'   )
					return sd






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]=rename_keys_prefix   ):
					__a			:       Any					    =  OrderedDict()
					__a			:       str					    =  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			:       List[str]					    =  key
										for name_pair in rename_keys_prefix:
															__a			:       List[str]					    =  new_key.replace(name_pair[0]						,      name_pair[1]   )
										__a			:       Tuple					    =  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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   List[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			:       Dict					    =  'pretraining'
										if "vcr" in checkpoint_path:
															__a			:       List[str]					    =  {'visual_embedding_dim': 512}
										elif "vqa_advanced" in checkpoint_path:
															__a			:       Optional[Any]					    =  {'visual_embedding_dim': 2_048}
										elif "vqa" in checkpoint_path:
															__a			:       Optional[Any]					    =  {'visual_embedding_dim': 2_048}
										elif "nlvr" in checkpoint_path:
															__a			:       Optional[int]					    =  {'visual_embedding_dim': 1_024}
										else:
															raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""   )
					else:
										if "vcr" in checkpoint_path:
															__a			:       Optional[int]					    =  {'visual_embedding_dim': 512}
															__a			:       int					    =  'multichoice'
										elif "vqa_advanced" in checkpoint_path:
															__a			:       Optional[int]					    =  {'visual_embedding_dim': 2_048}
															__a			:       Union[str, Any]					    =  'vqa_advanced'
										elif "vqa" in checkpoint_path:
															__a			:       Union[str, Any]					    =  {'visual_embedding_dim': 2_048, 'num_labels': 3_129}
															__a			:       Optional[int]					    =  'vqa'
										elif "nlvr" in checkpoint_path:
															__a			:       Union[str, Any]					    =  {
															    'visual_embedding_dim': 1_024,
															    'num_labels': 2,
															}
															__a			:       Any					    =  'nlvr'

					__a			:       Optional[int]					    =  VisualBertConfig(**_SCREAMING_SNAKE_CASE   )

					# Load State Dict
					__a			:       List[Any]					    =  load_state_dict(_SCREAMING_SNAKE_CASE   )

					__a			:       Tuple					    =  get_new_dict(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

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

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


if __name__ == "__main__":
	__lowercase	:						Optional[int]       =	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.')
	__lowercase	:						Any       =	parser.parse_args()
	convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''



import os
import sys

__lowercase	:						List[Any]       =	os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__lowercase	:						int       =	[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[str]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoConfig.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoTokenizer.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   str						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoTokenizer.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModel.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					return AutoModel.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForCausalLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[Any]						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					return AutoModelForCausalLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForMaskedLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoModelForMaskedLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForSequenceClassification.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoModelForSequenceClassification.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Any						,      **_SCREAMING_SNAKE_CASE      :   List[str]   ):
					return AutoModelForQuestionAnswering.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )



