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		:       Any      =					logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE		:       List[str]      =					[
    ("""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		:       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    snake_case		(__lowercase		)			->			Union[str, Any]:

						'''simple docstring'''



						_snake_case		:	str							=       torch.load(UpperCamelCase__   ,     map_location="cpu"		)
						return sd
def    snake_case		(__lowercase   ,     __lowercase   ,     __lowercase=rename_keys_prefix		)			->			str:

						'''simple docstring'''



						_snake_case		:	int							=       OrderedDict()
						_snake_case		:	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
												_snake_case		:	Optional[Any]							=       key
												for name_pair in rename_keys_prefix:
																		_snake_case		:	Dict							=       new_key.replace(name_pair[0]   ,     name_pair[1]		)
												_snake_case		:	Tuple							=       d[key]
												if key == "bert.cls.predictions.decoder.weight":
																		# Old bert code didn't have `decoder.bias`, but was added separately
																		_snake_case		:	Tuple							=       new_d['''cls.predictions.bias''']
						return new_d
@torch.no_grad()
def    snake_case		(__lowercase   ,     __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:
												_snake_case		:	Optional[int]							=       '''pretraining'''
												if "vcr" in checkpoint_path:
																		_snake_case		:	str							=       {'''visual_embedding_dim''': 512}
												elif "vqa_advanced" in checkpoint_path:
																		_snake_case		:	Tuple							=       {'''visual_embedding_dim''': 2_048}
												elif "vqa" in checkpoint_path:
																		_snake_case		:	Any							=       {'''visual_embedding_dim''': 2_048}
												elif "nlvr" in checkpoint_path:
																		_snake_case		:	int							=       {'''visual_embedding_dim''': 1_024}
												else:
																		raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""		)
						else:
												if "vcr" in checkpoint_path:
																		_snake_case		:	Any							=       {'''visual_embedding_dim''': 512}
																		_snake_case		:	Union[str, Any]							=       '''multichoice'''
												elif "vqa_advanced" in checkpoint_path:
																		_snake_case		:	List[str]							=       {'''visual_embedding_dim''': 2_048}
																		_snake_case		:	Union[str, Any]							=       '''vqa_advanced'''
												elif "vqa" in checkpoint_path:
																		_snake_case		:	List[str]							=       {'''visual_embedding_dim''': 2_048, '''num_labels''': 3_129}
																		_snake_case		:	str							=       '''vqa'''
												elif "nlvr" in checkpoint_path:
																		_snake_case		:	str							=       {
																		    '''visual_embedding_dim''': 1_024,
																		    '''num_labels''': 2,
																		}
																		_snake_case		:	Any							=       '''nlvr'''

						_snake_case		:	str							=       VisualBertConfig(**UpperCamelCase__		)

						# Load State Dict
						_snake_case		:	Dict							=       load_state_dict(UpperCamelCase__		)

						_snake_case		:	List[Any]							=       get_new_dict(UpperCamelCase__   ,     UpperCamelCase__		)

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

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


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






def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   int					)      ->    list:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   len(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   [[0] * n for i in range(UpperCamelCase__					)]
						for i in range(UpperCamelCase__					):
												__SCREAMING_SNAKE_CASE:				str           =   y_points[i]

						for i in range(2       ,			UpperCamelCase__					):
												for j in range(UpperCamelCase__       ,			UpperCamelCase__					):
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
																		    (xa - x_points[j - i + 1]) * q[j][i - 1]
																		    - (xa - x_points[j]) * q[j - 1][i - 1]
																		) / (x_points[j] - x_points[j - i + 1])

						return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()



