





'''simple docstring'''







import math
import sys

import cva
import numpy as np



def    __UpperCamelCase	(	a    : List[Any]					,   a    : Any     ) ->np.ndarray:
 snake_case				=			math.sqrt(_A     )
 snake_case				=			1 / (sigma * math.sqrt(2 * math.pi     ))
 return cons * np.exp(-((img / sigma) ** 2) * 0.5     )



def    __UpperCamelCase	(	a    : Union[str, Any]					,   a    : Optional[Any]					,   a    : Optional[int]					,   a    : Optional[int]     ) ->np.ndarray:
 snake_case				=			kernel_size // 2
 return img[x - half : x + half + 1, y - half : y + half + 1]



def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]     ) ->np.ndarray:
 snake_case				=			np.zeros((kernel_size, kernel_size)     )
 for i in range(0					,   _A     ):
  for j in range(0					,   _A     ):
   snake_case				=			math.sqrt(
       abs(i - kernel_size // 2     ) ** 2 + abs(j - kernel_size // 2     ) ** 2     )
 return vec_gaussian(_A					,   _A     )



def    __UpperCamelCase	(	a    : int					,   a    : int					,   a    : Dict					,   a    : Optional[Any]					,   ) ->np.ndarray:
 snake_case				=			np.zeros(img.shape     )
 snake_case				=			get_gauss_kernel(_A					,   _A     )
 snake_case				,		snake_case				=			img.shape
 for i in range(kernel_size // 2					,   size_x - kernel_size // 2     ):
  for j in range(kernel_size // 2					,   size_y - kernel_size // 2     ):
   snake_case				=			get_slice(_A					,   _A					,   _A					,   _A     )
   snake_case				=			img_s - img_s[kernel_size // 2, kernel_size // 2]
   snake_case				=			vec_gaussian(_A					,   _A     )
   snake_case				=			np.multiply(_A					,   _A     )
   snake_case				=			np.multiply(_A					,   _A     )
   snake_case				=			np.sum(_A     ) / np.sum(_A     )
   snake_case				=			val
 return imga



def    __UpperCamelCase	(	a    : str     ) ->tuple:
 snake_case				=			args[1] if args[1:] else '''../image_data/lena.jpg'''
 snake_case				=			float(args[2]     ) if args[2:] else 1.0
 snake_case				=			float(args[3]     ) if args[3:] else 1.0
 if args[4:]:
  snake_case				=			int(args[4]     )
  snake_case				=			kernel_size + abs(kernel_size % 2 - 1     )
 else:
  snake_case				=			5
 return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
   _lowercase       						=			parse_args(sys.argv)
   _lowercase       						=			cva.imread(filename, 0)
   cva.imshow('input image', img)

   _lowercase       						=			img / 255
   _lowercase       						=			out.astype('float32')
   _lowercase       						=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
   _lowercase       						=			out * 255
   _lowercase       						=			np.uinta(out)
   cva.imshow('output image', out)
   cva.waitKey(0)
   cva.destroyAllWindows()








'''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       						=			logging.get_logger(__name__)

_lowercase       						=			[
    ('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       						=			[
    '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    __UpperCamelCase	(	a    : List[str]     ) ->Optional[int]:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 return sd

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : Union[str, Any]					,   a    : int=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    __UpperCamelCase	(	a    : Optional[int]					,   a    : int     ) ->Union[str, 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''': 2048}
  elif "vqa" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 2048}
  elif "nlvr" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 1024}
  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''': 2048}
   snake_case				=			'''vqa_advanced'''
  elif "vqa" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
   snake_case				=			'''vqa'''
  elif "nlvr" in checkpoint_path:
   snake_case				=			{
       '''visual_embedding_dim''': 1024,
       '''num_labels''': 2,
   }
   snake_case				=			'''nlvr'''

 snake_case				=			VisualBertConfig(**a     )

 # Load State Dict
 snake_case				=			load_state_dict(a     )

 snake_case				=			get_new_dict(a					,   a     )

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

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


if __name__ == "__main__":
   _lowercase       						=			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       						=			parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)

