
'''simple docstring'''







from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor


class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		):
							__lowercase   	= """pixel_values"""
							__lowercase   	= False
							__lowercase   	= TimmBackboneConfig


							def __init__( self					,     lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(self					,     'timm'						)
								super().__init__(lowerCAmelCase_						)
								_snake_case     		=	config

								if config.backbone is None:
									raise ValueError('backbone is not set in the config. Please set it to a timm model name.'						)

								if config.backbone not in timm.list_models():
									raise ValueError(F'backbone {config.backbone} is not supported by timm.'						)

								if hasattr(lowerCAmelCase_					,     'out_features'						) and config.out_features is not None:
									raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'						)

								_snake_case     		=	getattr(lowerCAmelCase_					,     'use_pretrained_backbone'					,     lowerCAmelCase_						)
								if pretrained is None:
									raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'						)

								# We just take the final layer by default. This matches the default for the transformers models.
								_snake_case     		=	config.out_indices if getattr(lowerCAmelCase_					,     'out_indices'					,     lowerCAmelCase_						) is not None else (-1,)

								_snake_case     		=	timm.create_model(
								    config.backbone					,     pretrained=lowerCAmelCase_					,     features_only=config.features_only					,     in_chans=config.num_channels					,     out_indices=lowerCAmelCase_					,     **lowerCAmelCase_					,     )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								_snake_case     		=	self._backbone.return_layers
								_snake_case     		=	{layer['module']: str(lowerCAmelCase_						) for i, layer in enumerate(self._backbone.feature_info.info						)}
								super()._init_backbone(lowerCAmelCase_						)


							@classmethod
							def 				lowerCamelCase       ( cls					,     lowerCAmelCase_					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(cls					,     ['vision', 'timm']						)
								from ...models.timm_backbone import TimmBackboneConfig

								_snake_case     		=	kwargs.pop('config'					,     TimmBackboneConfig()						)

								_snake_case     		=	kwargs.pop('use_timm_backbone'					,     lowerCAmelCase_						)
								if not use_timm:
									raise ValueError('use_timm_backbone must be True for timm backbones'						)

								_snake_case     		=	kwargs.pop('num_channels'					,     config.num_channels						)
								_snake_case     		=	kwargs.pop('features_only'					,     config.features_only						)
								_snake_case     		=	kwargs.pop('use_pretrained_backbone'					,     config.use_pretrained_backbone						)
								_snake_case     		=	kwargs.pop('out_indices'					,     config.out_indices						)
								_snake_case     		=	TimmBackboneConfig(
								    backbone=lowerCAmelCase_					,     num_channels=lowerCAmelCase_					,     features_only=lowerCAmelCase_					,     use_pretrained_backbone=lowerCAmelCase_					,     out_indices=lowerCAmelCase_					,     )
								return super()._from_config(lowerCAmelCase_					,     **lowerCAmelCase_						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								pass




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     **lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	return_dict if return_dict is not None else self.config.use_return_dict
								_snake_case     		=	(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								_snake_case     		=	output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
									raise ValueError('Cannot output attentions for timm backbones at the moment'						)

								if output_hidden_states:
									# We modify the return layers to include all the stages of the backbone
									_snake_case     		=	self._all_layers
									_snake_case     		=	self._backbone(lowerCAmelCase_					,     **lowerCAmelCase_						)
									_snake_case     		=	self._return_layers
									_snake_case     		=	tuple(hidden_states[i] for i in self.out_indices						)
								else:
									_snake_case     		=	self._backbone(lowerCAmelCase_					,     **lowerCAmelCase_						)
									_snake_case     		=	None

								_snake_case     		=	tuple(lowerCAmelCase_						)
								_snake_case     		=	tuple(lowerCAmelCase_						) if hidden_states is not None else None

								if not return_dict:
									_snake_case     		=	(feature_maps,)
									if output_hidden_states:
										_snake_case     		=	output + (hidden_states,)
									return output

								return BackboneOutput(feature_maps=lowerCAmelCase_					,     hidden_states=lowerCAmelCase_					,     attentions=lowerCAmelCase_						)



'''simple docstring'''







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowercase					:							int   =      ""
lowercase					:							Union[str, Any]   =      ""
lowercase					:							Tuple   =      ""
lowercase					:							Optional[int]   =      1  # (0 is vertical, 1 is horizontal)







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case		,				_snake_case     		=	get_dataset(__A    ,       __A			)
	print('Processing...'			)
	_snake_case		,				_snake_case		,				_snake_case     		=	update_image_and_anno(__A    ,       __A    ,       __A			)

	for index, image in enumerate(__A			):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		_snake_case     		=	random_chars(32			)
		_snake_case     		=	paths[index].split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		_snake_case     		=	F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
		cva.imwrite(F'/{file_root}.jpg'    ,       __A    ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
		print(F'Success {index+1}/{len(__A			)} with {file_name}'			)
		_snake_case     		=	[]
		for anno in new_annos[index]:
			_snake_case     		=	F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
			annos_list.append(__A			)
		with open(F'/{file_root}.txt'    ,       'w'			) as outfile:
			outfile.write('\n'.join(line for line in annos_list			)			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			tuple[list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	for label_file in glob.glob(os.path.join(__A    ,       '*.txt'			)			):
		_snake_case     		=	label_file.split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		with open(__A			) as in_file:
			_snake_case     		=	in_file.readlines()
		_snake_case     		=	os.path.join(__A    ,       F'{label_name}.jpg'			)

		_snake_case     		=	[]
		for obj_list in obj_lists:
			_snake_case     		=	obj_list.rstrip('\n'			).split(' '			)
			boxes.append(
			    [
			        int(obj[0]			),
			        float(obj[1]			),
			        float(obj[2]			),
			        float(obj[3]			),
			        float(obj[4]			),
			    ]			)
		if not boxes:
			continue
		img_paths.append(__A			)
		labels.append(__A			)
	return img_paths, labels







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = 1			) ->			tuple[list, list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	_snake_case     		=	[]
	for idx in range(len(__A			)			):
		_snake_case     		=	[]
		_snake_case     		=	img_list[idx]
		path_list.append(__A			)
		_snake_case     		=	anno_list[idx]
		_snake_case     		=	cva.imread(__A			)
		if flip_type == 1:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
		elif flip_type == 0:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
		new_annos_lists.append(__A			)
		new_imgs_list.append(__A			)
	return new_imgs_list, new_annos_lists, path_list







def        SCREAMING_SNAKE_CASE__			(     __A = 32			) ->			str:
	assert number_char > 1, "The number of character should greater than 1"
	_snake_case     		=	ascii_lowercase + digits
	return "".join(random.choice(__A			) for _ in range(__A			)			)


if __name__ == "__main__":
					main()
					print("DONE ✅")

