

"""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()
snake_case_   :   Optional[Any]        =       logging.get_logger(__name__)

snake_case_   :   Tuple        =       [
    ("""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"""),
]

snake_case_   :   Union[str, 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   lowercase_	(    _lowercase       :		Union[str, Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				torch.load(snake_case__							,      map_location="cpu"	)
					return sd



def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Optional[Any]							,      _lowercase       :		int=rename_keys_prefix	):




					'''simple docstring'''






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



@torch.no_grad()
def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Tuple	):




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

					UpperCAmelCase			:   Optional[int]      =				VisualBertConfig(**snake_case__	)

					# Load State Dict
					UpperCAmelCase			:   int      =				load_state_dict(snake_case__	)

					UpperCAmelCase			:   Optional[Any]      =				get_new_dict(snake_case__							,      snake_case__	)

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

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


if __name__ == "__main__":
	snake_case_   :   Dict        =       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.""")
	snake_case_   :   Union[str, Any]        =       parser.parse_args()
	convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""






def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Tuple							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				[False] * len(_lowercase	)
					UpperCAmelCase			:   List[str]      =				[]
					queue.append(_lowercase	)
					UpperCAmelCase			:   List[Any]      =				True

					while queue:
										UpperCAmelCase			:   Any      =				queue.pop(0	)
										for ind in range(len(graph[u]	)	):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(_lowercase	)
																				UpperCAmelCase			:   Optional[int]      =				True
																				UpperCAmelCase			:   int      =				u

					return visited[t]



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Dict							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				[-1] * (len(_lowercase	))
					UpperCAmelCase			:   List[Any]      =				0
					while bfs(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				float("Inf"	)
										UpperCAmelCase			:   str      =				sink

										while s != source:
															# Find the minimum value in select path
															UpperCAmelCase			:   Tuple      =				min(_lowercase							,      graph[parent[s]][s]	)
															UpperCAmelCase			:   Optional[Any]      =				parent[s]

										max_flow += path_flow
										UpperCAmelCase			:   List[str]      =				sink

										while v != source:
															UpperCAmelCase			:   Dict      =				parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															UpperCAmelCase			:   Dict      =				parent[v]
					return max_flow


snake_case_   :   Optional[Any]        =       [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

snake_case_     ,      snake_case_   :   str        =       0, 5




print(ford_fulkerson(graph, source, sink))


