

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							:  List[str]   	=						logging.get_logger(__name__)

lowerCAmelCase							:  List[Any]   	=						[
    ("""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							:  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 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: str  		=       torch.load(_UpperCAmelCase				,	map_location="cpu"    )
						return sd







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







@torch.no_grad()
def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						assert (
						    checkpoint_path.split("/"    )[-1] in ACCEPTABLE_CHECKPOINTS
						), f"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."

						# Get Config
						if "pre" in checkpoint_path:
												SCREAMING_SNAKE_CASE_: Any  		=       "pretraining"
												if "vcr" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {"visual_embedding_dim": 5_12}
												elif "vqa_advanced" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: int  		=       {"visual_embedding_dim": 20_48}
												elif "vqa" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"visual_embedding_dim": 20_48}
												elif "nlvr" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       {"visual_embedding_dim": 10_24}
												else:
																		raise NotImplementedError(f"No implementation found for `{checkpoint_path}`."    )
						else:
												if "vcr" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       {"visual_embedding_dim": 5_12}
																		SCREAMING_SNAKE_CASE_: str  		=       "multichoice"
												elif "vqa_advanced" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {"visual_embedding_dim": 20_48}
																		SCREAMING_SNAKE_CASE_: List[str]  		=       "vqa_advanced"
												elif "vqa" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {"visual_embedding_dim": 20_48, "num_labels": 31_29}
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       "vqa"
												elif "nlvr" in checkpoint_path:
																		SCREAMING_SNAKE_CASE_: str  		=       {
																		    "visual_embedding_dim": 10_24,
																		    "num_labels": 2,
																		}
																		SCREAMING_SNAKE_CASE_: Dict  		=       "nlvr"

						SCREAMING_SNAKE_CASE_: Dict  		=       VisualBertConfig(**_UpperCAmelCase    )

						# Load State Dict
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       load_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       get_new_dict(_UpperCAmelCase				,	_UpperCAmelCase    )

						if model_type == "pretraining":
												SCREAMING_SNAKE_CASE_: Dict  		=       VisualBertForPreTraining(_UpperCAmelCase    )
						elif model_type == "vqa":
												SCREAMING_SNAKE_CASE_: str  		=       VisualBertForQuestionAnswering(_UpperCAmelCase    )
						elif model_type == "nlvr":
												SCREAMING_SNAKE_CASE_: Tuple  		=       VisualBertForVisualReasoning(_UpperCAmelCase    )
						elif model_type == "multichoice":
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       VisualBertForMultipleChoice(_UpperCAmelCase    )

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


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









def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       [0] * len(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       []
						SCREAMING_SNAKE_CASE_: str  		=       []
						SCREAMING_SNAKE_CASE_: List[str]  		=       0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(_UpperCAmelCase    )    ):
												if indegree[i] == 0:
																		queue.append(_UpperCAmelCase    )

						while queue:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       queue.pop(0    )
												cnt += 1
												topo.append(_UpperCAmelCase    )
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(_UpperCAmelCase    )

						if cnt != len(_UpperCAmelCase    ):
												print("Cycle exists"    )
						else:
												print(_UpperCAmelCase    )


# Adjacency List of Graph
lowerCAmelCase							:  Any   	=						{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}



topological_sort(graph)






