



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   			=logging.get_logger(__name__)

__A   			={
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """beit"""




			def __init__(   self							: Optional[Any]       ,       a_							: Tuple=81_92       ,       a_							: Dict=7_68       ,       a_							: List[str]=12       ,       a_							: Tuple=12       ,       a_							: Union[str, Any]=30_72       ,       a_							: List[Any]="gelu"       ,       a_							: int=0.0       ,       a_							: List[Any]=0.0       ,       a_							: str=0.0_2       ,       a_							: Any=1e-12       ,       a_							: str=2_24       ,       a_							: int=16       ,       a_							: Optional[Any]=3       ,       a_							: Tuple=False       ,       a_							: Dict=False       ,       a_							: int=False       ,       a_							: int=False       ,       a_							: Dict=0.1       ,       a_							: str=0.1       ,       a_							: int=True       ,       a_							: int=[3, 5, 7, 11]       ,       a_							: Dict=[1, 2, 3, 6]       ,       a_							: Tuple=True       ,       a_							: Dict=0.4       ,       a_							: int=2_56       ,       a_							: Union[str, Any]=1       ,       a_							: Tuple=False       ,       a_							: Optional[Any]=2_55       ,       **a_							: int       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )

					__UpperCAmelCase    :     List[Any]													=	vocab_size
					__UpperCAmelCase    :     int													=	hidden_size
					__UpperCAmelCase    :     Union[str, Any]													=	num_hidden_layers
					__UpperCAmelCase    :     str													=	num_attention_heads
					__UpperCAmelCase    :     int													=	intermediate_size
					__UpperCAmelCase    :     Tuple													=	hidden_act
					__UpperCAmelCase    :     Any													=	hidden_dropout_prob
					__UpperCAmelCase    :     int													=	attention_probs_dropout_prob
					__UpperCAmelCase    :     Optional[int]													=	initializer_range
					__UpperCAmelCase    :     Any													=	layer_norm_eps

					__UpperCAmelCase    :     Optional[int]													=	image_size
					__UpperCAmelCase    :     int													=	patch_size
					__UpperCAmelCase    :     int													=	num_channels
					__UpperCAmelCase    :     int													=	use_mask_token
					__UpperCAmelCase    :     Dict													=	use_absolute_position_embeddings
					__UpperCAmelCase    :     Dict													=	use_relative_position_bias
					__UpperCAmelCase    :     Dict													=	use_shared_relative_position_bias
					__UpperCAmelCase    :     List[Any]													=	layer_scale_init_value
					__UpperCAmelCase    :     Any													=	drop_path_rate
					__UpperCAmelCase    :     Optional[Any]													=	use_mean_pooling
					# decode head attributes (semantic segmentation)
					__UpperCAmelCase    :     Dict													=	out_indices
					__UpperCAmelCase    :     Optional[Any]													=	pool_scales
					# auxiliary head attributes (semantic segmentation)
					__UpperCAmelCase    :     Any													=	use_auxiliary_head
					__UpperCAmelCase    :     str													=	auxiliary_loss_weight
					__UpperCAmelCase    :     List[str]													=	auxiliary_channels
					__UpperCAmelCase    :     str													=	auxiliary_num_convs
					__UpperCAmelCase    :     Any													=	auxiliary_concat_input
					__UpperCAmelCase    :     str													=	semantic_loss_ignore_index

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       version.parse("""1.11"""   )




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return OrderedDict(
					    [
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
					    ] )




			@property
			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					return 1e-4










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

import cva
__A   			=""
__A   			=""
__A   			=""
__A   			=1  # (0 is vertical, 1 is horizontal)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	get_dataset(_UpperCAmelCase ,   _UpperCAmelCase						)
		print('''Processing...'''						)
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	update_image_and_anno(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

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

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	[]
		__UpperCAmelCase    :     Any													=	[]
		for label_file in glob.glob(os.path.join(_UpperCAmelCase ,   '''*.txt'''						)						):
				__UpperCAmelCase    :     Optional[Any]													=	label_file.split(os.sep						)[-1].rsplit('''.''' ,   1						)[0]
				with open(_UpperCAmelCase						) as in_file:
						__UpperCAmelCase    :     List[str]													=	in_file.readlines()
				__UpperCAmelCase    :     Optional[Any]													=	os.path.join(_UpperCAmelCase ,   f'{label_name}.jpg'						)

				__UpperCAmelCase    :     str													=	[]
				for obj_list in obj_lists:
						__UpperCAmelCase    :     str													=	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(_UpperCAmelCase						)
				labels.append(_UpperCAmelCase						)
		return img_paths, labels

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int = 1						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		__UpperCAmelCase    :     Any													=	[]
		for idx in range(len(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     Tuple													=	[]
				__UpperCAmelCase    :     List[Any]													=	img_list[idx]
				path_list.append(_UpperCAmelCase						)
				__UpperCAmelCase    :     str													=	anno_list[idx]
				__UpperCAmelCase    :     str													=	cva.imread(_UpperCAmelCase						)
				if flip_type == 1:
						__UpperCAmelCase    :     Any													=	cva.flip(_UpperCAmelCase ,   _UpperCAmelCase						)
						for bbox in img_annos:
								__UpperCAmelCase    :     List[str]													=	1 - bbox[1]
								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]						)
				elif flip_type == 0:
						__UpperCAmelCase    :     Any													=	cva.flip(_UpperCAmelCase ,   _UpperCAmelCase						)
						for bbox in img_annos:
								__UpperCAmelCase    :     Union[str, Any]													=	1 - bbox[2]
								new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]						)
				new_annos_lists.append(_UpperCAmelCase						)
				new_imgs_list.append(_UpperCAmelCase						)
		return new_imgs_list, new_annos_lists, path_list

def        a						(      _UpperCAmelCase							:					int = 32						):







		'''simple docstring'''





		assert number_char > 1, "The number of character should greater than 1"
		__UpperCAmelCase    :     Union[str, Any]													=	ascii_lowercase + digits
		return "".join(random.choice(_UpperCAmelCase						) for _ in range(_UpperCAmelCase						)						)


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





