


'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :		Dict		     =	logging.get_logger(__name__)

def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= SwinConfig(
	    embed_dim=1_92					,   depths=(2, 2, 18, 2)					,   num_heads=(6, 12, 24, 48)					,   window_size=12					,   out_features=['stage2', 'stage3', 'stage4']					,   )

	lowerCAmelCase					:    Optional[Any]      							= DetaConfig(
	    backbone_config=UpperCamelCase__					,   num_queries=9_00					,   encoder_ffn_dim=20_48					,   decoder_ffn_dim=20_48					,   num_feature_levels=5					,   assign_first_stage=UpperCamelCase__					,   with_box_refine=UpperCamelCase__					,   two_stage=UpperCamelCase__					,   )

	# set labels
	lowerCAmelCase					:    List[Any]      							= 'huggingface/label-files'
	if "o365" in model_name:
		lowerCAmelCase					:    Dict      							= 3_66
		lowerCAmelCase					:    Optional[Any]      							= 'object365-id2label.json'
	else:
		lowerCAmelCase					:    str      							= 91
		lowerCAmelCase					:    int      							= 'coco-detection-id2label.json'

	lowerCAmelCase					:    List[str]      							= num_labels
	lowerCAmelCase					:    Tuple      							= json.load(open(cached_download(hf_hub_url(UpperCamelCase__					,   UpperCamelCase__					,   repo_type='dataset'				)				)					,   'r'				)				)
	lowerCAmelCase					:    Optional[Any]      							= {int(UpperCamelCase__				): v for k, v in idalabel.items()}
	lowerCAmelCase					:    Optional[int]      							= idalabel
	lowerCAmelCase					:    Dict      							= {v: k for k, v in idalabel.items()}

	return config

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= []

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')				)
	rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')				)
	rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')				)
	rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')				)
	# stages
	for i in range(len(config.backbone_config.depths				)				):
		for j in range(config.backbone_config.depths[i]				):
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")				)

		if i < 3:
			rename_keys.append((F"backbone.0.body.layers.{i}.downsample.reduction.weight", F"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.weight", F"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")				)
			rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.bias", F"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")				)

	rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')				)
	rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')				)
	rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')				)
	rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')				)
	rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')				)
	rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')				)

	# transformer encoder
	for i in range(config.encoder_layers				):
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight")				)
		rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias")				)

	# transformer decoder
	for i in range(config.decoder_layers				):
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight")				)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias")				)

	# fmt: on

	return rename_keys

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				Dict					,   _A    :				List[Any]				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= dct.pop(UpperCamelCase__				)
	lowerCAmelCase					:    Tuple      							= val

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Tuple				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
	for i in range(len(backbone_config.depths				)				):
		lowerCAmelCase					:    Optional[int]      							= num_features[i]
		for j in range(backbone_config.depths[i]				):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			lowerCAmelCase					:    Optional[int]      							= state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"				)
			lowerCAmelCase					:    List[str]      							= state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"				)
			# next, add query, keys and values (in that order) to the state dict
			lowerCAmelCase					:    str      							= in_proj_weight[:dim, :]
			lowerCAmelCase					:    Any      							= in_proj_bias[: dim]
			lowerCAmelCase					:    str      							= in_proj_weight[
			    dim : dim * 2, :
			]
			lowerCAmelCase					:    Dict      							= in_proj_bias[
			    dim : dim * 2
			]
			lowerCAmelCase					:    Any      							= in_proj_weight[
			    -dim :, :
			]
			lowerCAmelCase					:    int      							= in_proj_bias[-dim :]
			# fmt: on

def 			__UpperCamelCase    (    _A    :				List[str]					,   _A    :				Any				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= config.d_model
	for i in range(config.decoder_layers				):
		# read in weights + bias of input projection layer of self-attention
		lowerCAmelCase					:    Optional[Any]      							= state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"				)
		lowerCAmelCase					:    List[str]      							= state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias"				)
		# next, add query, keys and values (in that order) to the state dict
		lowerCAmelCase					:    int      							= in_proj_weight[:hidden_size, :]
		lowerCAmelCase					:    Union[str, Any]      							= in_proj_bias[:hidden_size]
		lowerCAmelCase					:    str      							= in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		lowerCAmelCase					:    Union[str, Any]      							= in_proj_bias[hidden_size : hidden_size * 2]
		lowerCAmelCase					:    Tuple      							= in_proj_weight[-hidden_size:, :]
		lowerCAmelCase					:    Union[str, Any]      							= in_proj_bias[-hidden_size:]

def 			__UpperCamelCase    (    )    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCAmelCase					:    int      							= Image.open(requests.get(UpperCamelCase__					,   stream=UpperCamelCase__				).raw				)

	return im

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				str					,   _A    :				List[Any]					,   _A    :				Union[str, Any]				)    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    Any      							= get_deta_config(UpperCamelCase__				)

	# load original state dict
	if model_name == "deta-swin-large":
		lowerCAmelCase					:    Tuple      							= hf_hub_download(repo_id='nielsr/deta-checkpoints'					,   filename='adet_swin_ft.pth'				)
	elif model_name == "deta-swin-large-o365":
		lowerCAmelCase					:    str      							= hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'					,   filename='deta_swin_pt_o365.pth'				)
	else:
		raise ValueError(F"Model name {model_name} not supported"				)

	lowerCAmelCase					:    List[str]      							= torch.load(UpperCamelCase__					,   map_location='cpu'				)['model']

	# original state dict
	for name, param in state_dict.items():
		print(UpperCamelCase__					,   param.shape				)

	# rename keys
	lowerCAmelCase					:    int      							= create_rename_keys(UpperCamelCase__				)
	for src, dest in rename_keys:
		rename_key(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__				)
	read_in_swin_q_k_v(UpperCamelCase__					,   config.backbone_config				)
	read_in_decoder_q_k_v(UpperCamelCase__					,   UpperCamelCase__				)

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			lowerCAmelCase					:    str      							= state_dict.pop(UpperCamelCase__				)
			lowerCAmelCase					:    List[Any]      							= val
		if "input_proj" in key:
			lowerCAmelCase					:    List[str]      							= state_dict.pop(UpperCamelCase__				)
			lowerCAmelCase					:    Tuple      							= val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			lowerCAmelCase					:    List[str]      							= state_dict.pop(UpperCamelCase__				)
			lowerCAmelCase					:    List[str]      							= val

    # finally, create HuggingFace model and load state dict
	lowerCAmelCase					:    Dict      							= DetaForObjectDetection(UpperCamelCase__				)
	model.load_state_dict(UpperCamelCase__				)
	model.eval()

	lowerCAmelCase					:    Optional[Any]      							= 'cuda' if torch.cuda.is_available() else 'cpu'
	model.to(UpperCamelCase__				)

	# load image processor
	lowerCAmelCase					:    Union[str, Any]      							= DetaImageProcessor(format='coco_detection'				)

	# verify our conversion on image
	lowerCAmelCase					:    Union[str, Any]      							= prepare_img()
	lowerCAmelCase					:    Optional[int]      							= processor(images=UpperCamelCase__					,   return_tensors='pt'				)
	lowerCAmelCase					:    Tuple      							= encoding['pixel_values']
	lowerCAmelCase					:    Any      							= model(pixel_values.to(UpperCamelCase__				)				)

	# verify logits
	print('Logits:'					,   outputs.logits[0, :3, :3]				)
	print('Boxes:'					,   outputs.pred_boxes[0, :3, :3]				)
	if model_name == "deta-swin-large":
		lowerCAmelCase					:    Any      							= torch.tensor(
		    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]				)
		lowerCAmelCase					:    List[str]      							= torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]				)
	elif model_name == "deta-swin-large-o365":
		lowerCAmelCase					:    Dict      							= torch.tensor(
		    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]				)
		lowerCAmelCase					:    int      							= torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]				)
	assert torch.allclose(outputs.logits[0, :3, :3]					,   expected_logits.to(UpperCamelCase__				)					,   atol=1e-4				)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,   expected_boxes.to(UpperCamelCase__				)					,   atol=1e-4				)
	print('Everything ok!'				)

	if pytorch_dump_folder_path:
		# Save model and processor
		logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."				)
		Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
		model.save_pretrained(UpperCamelCase__				)
		processor.save_pretrained(UpperCamelCase__				)

	# Push to hub
	if push_to_hub:
		print('Pushing model and processor to hub...'				)
		model.push_to_hub(F"jozhang97/{model_name}"				)
		processor.push_to_hub(F"jozhang97/{model_name}"				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[str]		     =	argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    type=str,
				    default='deta-swin-large',
				    choices=['deta-swin-large', 'deta-swin-large-o365'],
				    help='Name of the model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=str,
				    help='Path to the folder to output PyTorch model.',
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)
				_lowerCAmelCase  :		Tuple		     =	parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




import math
import sys

import cva
import numpy as np

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= math.sqrt(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= 1 / (sigma * math.sqrt(2 * math.pi				))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				int					,   _A    :				int					,   _A    :				int				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= kernel_size // 2
	return img[x - half : x + half + 1, y - half : y + half + 1]

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= np.zeros((kernel_size, kernel_size)				)
	for i in range(0					,   _A				):
		for j in range(0					,   _A				):
			lowerCAmelCase					:    Optional[int]      							= math.sqrt(
			    abs(i - kernel_size // 2				) ** 2 + abs(j - kernel_size // 2				) ** 2				)
	return vec_gaussian(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float					,   _A    :				float					,   _A    :				int					,   )    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= np.zeros(img.shape				)
	lowerCAmelCase					:    int      							= get_gauss_kernel(_A					,   _A				)
	lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= 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				):
			lowerCAmelCase					:    int      							= get_slice(_A					,   _A					,   _A					,   _A				)
			lowerCAmelCase					:    Any      							= img_s - img_s[kernel_size // 2, kernel_size // 2]
			lowerCAmelCase					:    str      							= vec_gaussian(_A					,   _A				)
			lowerCAmelCase					:    Optional[int]      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    str      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    Union[str, Any]      							= np.sum(_A				) / np.sum(_A				)
			lowerCAmelCase					:    Tuple      							= val
	return imga

def 			__UpperCamelCase    (    _A    :				list				)    ->      tuple:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= args[1] if args[1:] else '../image_data/lena.jpg'
	lowerCAmelCase					:    Any      							= float(args[2]				) if args[2:] else 1.0
	lowerCAmelCase					:    Union[str, Any]      							= float(args[3]				) if args[3:] else 1.0
	if args[4:]:
		lowerCAmelCase					:    int      							= int(args[4]				)
		lowerCAmelCase					:    Optional[Any]      							= kernel_size + abs(kernel_size % 2 - 1				)
	else:
		lowerCAmelCase					:    Optional[int]      							= 5
	return filename, spatial_variance, intensity_variance, kernel_size


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

				_lowerCAmelCase  :		Union[str, Any]		     =	img / 255
				_lowerCAmelCase  :		List[str]		     =	out.astype('float32')
				_lowerCAmelCase  :		Optional[int]		     =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
				_lowerCAmelCase  :		Union[str, Any]		     =	out * 255
				_lowerCAmelCase  :		Optional[Any]		     =	np.uinta(out)
				cva.imshow('output image', out)
				cva.waitKey(0)
				cva.destroyAllWindows()
