




'''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()
_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						[
    ("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"),
]

_SCREAMING_SNAKE_CASE			=						[
    "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_     )		->		int:
			A_									= torch.load(SCREAMING_SNAKE_CASE_       ,map_location='cpu'     )
			return sd





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=rename_keys_prefix     )		->		Any:
			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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Any:
			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': 2048}
						elif "vqa" in checkpoint_path:
									A_									= {'visual_embedding_dim': 2048}
						elif "nlvr" in checkpoint_path:
									A_									= {'visual_embedding_dim': 1024}
						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': 2048}
									A_									= 'vqa_advanced'
						elif "vqa" in checkpoint_path:
									A_									= {'visual_embedding_dim': 2048, 'num_labels': 3129}
									A_									= 'vqa'
						elif "nlvr" in checkpoint_path:
									A_									= {
									    'visual_embedding_dim': 1024,
									    'num_labels': 2,
									}
									A_									= 'nlvr'

			A_									= VisualBertConfig(**SCREAMING_SNAKE_CASE_     )

			# Load State Dict
			A_									= load_state_dict(SCREAMING_SNAKE_CASE_     )

			A_									= get_new_dict(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )

			if model_type == "pretraining":
						A_									= VisualBertForPreTraining(SCREAMING_SNAKE_CASE_     )
			elif model_type == "vqa":
						A_									= VisualBertForQuestionAnswering(SCREAMING_SNAKE_CASE_     )
			elif model_type == "nlvr":
						A_									= VisualBertForVisualReasoning(SCREAMING_SNAKE_CASE_     )
			elif model_type == "multichoice":
						A_									= 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__":
							_SCREAMING_SNAKE_CASE			=						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.")
							_SCREAMING_SNAKE_CASE			=						parser.parse_args()
							convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)











import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=sorted_examples[0]

   def is_too_big(A     ):
      return tok(A				,							return_tensors="pt"     ).input_ids.shape[1] > max_tokens

   for src, tgt in tqdm(sorted_examples[1:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=new_tgt + " " + tgt
      if is_too_big(A     ) or is_too_big(A     ):  # cant fit, finalize example
         finished_src.append(A     )
         finished_tgt.append(A     )
         UpperCAmelCase__			,							UpperCAmelCase__						=src, tgt
      else:  # can fit, keep adding
         UpperCAmelCase__			,							UpperCAmelCase__						=cand_src, cand_tgt

    # cleanup
   if new_src:
      assert new_tgt
      finished_src.append(A     )
      finished_tgt.append(A     )
   return finished_src, finished_tgt






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=pack_examples(A				,							A				,							A				,							A     )
      print(F"""packed {split} split from {len(A     )} examples -> {len(A     )}."""     )
      Path(save_path / F"""{split}.source"""     ).open("w"     ).write("\n".join(A     )     )
      Path(save_path / F"""{split}.target"""     ).open("w"     ).write("\n".join(A     )     )
   for split in ["val", "test"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      shutil.copyfile(A				,							save_path / F"""{split}.source"""     )
      shutil.copyfile(A				,							save_path / F"""{split}.target"""     )






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser()
   parser.add_argument("--tok_name"				,							type=A				,							help="like facebook/bart-large-cnn,t5-base, etc."     )
   parser.add_argument("--max_seq_len"				,							type=A				,							default=128     )
   parser.add_argument("--data_dir"				,							type=A     )
   parser.add_argument("--save_path"				,							type=A     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=AutoTokenizer.from_pretrained(args.tok_name     )
   return pack_data_dir(A				,							Path(args.data_dir     )				,							args.max_seq_len				,							args.save_path     )


if __name__ == "__main__":
    packer_cli()



