"""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()
__lowerCamelCase											=  logging.get_logger(__name__)

__lowerCamelCase											=  [
    ("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"),
]

__lowerCamelCase											=  [
    "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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							torch.load(UpperCamelCase__       ,    map_location='cpu'				)
							return sd






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__=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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""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': 512}
														elif "vqa_advanced" in checkpoint_path:
																					A__	     =							{'visual_embedding_dim': 2_048}
														elif "vqa" in checkpoint_path:
																					A__	     =							{'visual_embedding_dim': 2_048}
														elif "nlvr" in checkpoint_path:
																					A__	     =							{'visual_embedding_dim': 1_024}
														else:
																					raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''				)
							else:
														if "vcr" in checkpoint_path:
																					A__	     =							{'visual_embedding_dim': 512}
																					A__	     =							'multichoice'
														elif "vqa_advanced" in checkpoint_path:
																					A__	     =							{'visual_embedding_dim': 2_048}
																					A__	     =							'vqa_advanced'
														elif "vqa" in checkpoint_path:
																					A__	     =							{'visual_embedding_dim': 2_048, 'num_labels': 3_129}
																					A__	     =							'vqa'
														elif "nlvr" in checkpoint_path:
																					A__	     =							{
																					    'visual_embedding_dim': 1_024,
																					    'num_labels': 2,
																					}
																					A__	     =							'nlvr'

							A__	     =							VisualBertConfig(**UpperCamelCase__				)

							# Load State Dict
							A__	     =							load_state_dict(UpperCamelCase__				)

							A__	     =							get_new_dict(UpperCamelCase__       ,    UpperCamelCase__				)

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

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


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






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





