
'''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()
lowercase    :							Optional[int]      =					logging.get_logger(__name__)

lowercase    :							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"),
]

lowercase    :							Optional[int]      =					[
    "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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Union[str, Any]:
	_snake_case   						=		torch.load(__lowerCamelCase			,				map_location='cpu'	)
	return sd







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







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	List[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:
		_snake_case   						=		"""pretraining"""
		if "vcr" in checkpoint_path:
			_snake_case   						=		{"""visual_embedding_dim""": 512}
		elif "vqa_advanced" in checkpoint_path:
			_snake_case   						=		{"""visual_embedding_dim""": 2_048}
		elif "vqa" in checkpoint_path:
			_snake_case   						=		{"""visual_embedding_dim""": 2_048}
		elif "nlvr" in checkpoint_path:
			_snake_case   						=		{"""visual_embedding_dim""": 1_024}
		else:
			raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'	)
	else:
		if "vcr" in checkpoint_path:
			_snake_case   						=		{"""visual_embedding_dim""": 512}
			_snake_case   						=		"""multichoice"""
		elif "vqa_advanced" in checkpoint_path:
			_snake_case   						=		{"""visual_embedding_dim""": 2_048}
			_snake_case   						=		"""vqa_advanced"""
		elif "vqa" in checkpoint_path:
			_snake_case   						=		{"""visual_embedding_dim""": 2_048, """num_labels""": 3_129}
			_snake_case   						=		"""vqa"""
		elif "nlvr" in checkpoint_path:
			_snake_case   						=		{
			    """visual_embedding_dim""": 1_024,
			    """num_labels""": 2,
			}
			_snake_case   						=		"""nlvr"""

	_snake_case   						=		VisualBertConfig(**__lowerCamelCase	)

	# Load State Dict
	_snake_case   						=		load_state_dict(__lowerCamelCase	)

	_snake_case   						=		get_new_dict(__lowerCamelCase			,				__lowerCamelCase	)

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

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


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









import cva
import numpy as np
class     a    :



      """simple docstring"""


      def __init__(      self					:			Optional[Any]		,      __lowercase					:			float		,      __lowercase					:			int					)							->      List[Any]:

        if k in (0.04, 0.06):
          __UpperCAmelCase							:	int           =  k
          __UpperCAmelCase							:	List[str]           =  window_size
        else:
          raise ValueError("""invalid k value"""					)
      def __str__(      self					:			Union[str, Any]					)							->      str:
        return str(self.k					)




      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str					)							->      tuple[cva.Mat, list[list[int]]]:
        __UpperCAmelCase							:	Optional[Any]           =  cva.imread(__lowercase		,      0					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[Any]           =  img.shape
        __UpperCAmelCase							:	list[list[int]]           =  []
        __UpperCAmelCase							:	Any           =  img.copy()
        __UpperCAmelCase							:	Tuple           =  cva.cvtColor(__lowercase		,      cva.COLOR_GRAY2RGB					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[str]           =  np.gradient(__lowercase					)
        __UpperCAmelCase							:	int           =  dx**2
        __UpperCAmelCase							:	Any           =  dy**2
        __UpperCAmelCase							:	Any           =  dx * dy
        __UpperCAmelCase							:	Optional[int]           =  0.04
        __UpperCAmelCase							:	List[str]           =  self.window_size // 2
        for y in range(__lowercase		,      h - offset					):
          for x in range(__lowercase		,      w - offset					):
            __UpperCAmelCase							:	Tuple           =  ixx[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	List[Any]           =  iyy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	Optional[int]           =  ixy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()

            __UpperCAmelCase							:	int           =  (wxx * wyy) - (wxy**2)
            __UpperCAmelCase							:	Any           =  wxx + wyy
            __UpperCAmelCase							:	Optional[Any]           =  det - k * (trace**2)
            # Can change the value
            if r > 0.5:
              corner_list.append([x, y, r]					)
              color_img.itemset((y, x, 0)		,      0					)
              color_img.itemset((y, x, 1)		,      0					)
              color_img.itemset((y, x, 2)		,      255					)
        return color_img, corner_list


if __name__ == "__main__":
       a		:       int			     = HarrisCorner(0.04, 3)
       a     ,a		:       Any			     = edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)


