
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						:      Dict									= logging.get_logger(__name__)

lowercase						:      int									= [
    ('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[str]									= [
    '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      (	_lowerCamelCase				:    Any)      ->					Tuple:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      torch.load(_lowerCamelCase		,						map_location="cpu")
							return sd




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Tuple=rename_keys_prefix)      ->					str:






							'''simple docstring'''





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




@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    str)      ->					Dict:






							'''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:
														__UpperCamelCase			:     List[Any]         =      "pretraining"
														if "vcr" in checkpoint_path:
																					__UpperCamelCase			:     Optional[Any]         =      {"visual_embedding_dim": 512}
														elif "vqa_advanced" in checkpoint_path:
																					__UpperCamelCase			:     Any         =      {"visual_embedding_dim": 2_048}
														elif "vqa" in checkpoint_path:
																					__UpperCamelCase			:     str         =      {"visual_embedding_dim": 2_048}
														elif "nlvr" in checkpoint_path:
																					__UpperCamelCase			:     Optional[Any]         =      {"visual_embedding_dim": 1_024}
														else:
																					raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.')
							else:
														if "vcr" in checkpoint_path:
																					__UpperCamelCase			:     Union[str, Any]         =      {"visual_embedding_dim": 512}
																					__UpperCamelCase			:     str         =      "multichoice"
														elif "vqa_advanced" in checkpoint_path:
																					__UpperCamelCase			:     Optional[int]         =      {"visual_embedding_dim": 2_048}
																					__UpperCamelCase			:     Optional[int]         =      "vqa_advanced"
														elif "vqa" in checkpoint_path:
																					__UpperCamelCase			:     int         =      {"visual_embedding_dim": 2_048, "num_labels": 3_129}
																					__UpperCamelCase			:     List[Any]         =      "vqa"
														elif "nlvr" in checkpoint_path:
																					__UpperCamelCase			:     Any         =      {
																					    "visual_embedding_dim": 1_024,
																					    "num_labels": 2,
																					}
																					__UpperCamelCase			:     Tuple         =      "nlvr"

							__UpperCamelCase			:     Union[str, Any]         =      VisualBertConfig(**_lowerCamelCase)

							# Load State Dict
							__UpperCamelCase			:     List[str]         =      load_state_dict(_lowerCamelCase)

							__UpperCamelCase			:     str         =      get_new_dict(_lowerCamelCase		,						_lowerCamelCase)

							if model_type == "pretraining":
														__UpperCamelCase			:     List[str]         =      VisualBertForPreTraining(_lowerCamelCase)
							elif model_type == "vqa":
														__UpperCamelCase			:     Any         =      VisualBertForQuestionAnswering(_lowerCamelCase)
							elif model_type == "nlvr":
														__UpperCamelCase			:     Dict         =      VisualBertForVisualReasoning(_lowerCamelCase)
							elif model_type == "multichoice":
														__UpperCamelCase			:     List[Any]         =      VisualBertForMultipleChoice(_lowerCamelCase)

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


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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)