


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

import cva
A							:					Optional[int]     						=     ""
A							:					Dict     						=     ""
A							:					Optional[int]     						=     ""
A							:					Tuple     						=     1  # (0 is vertical, 1 is horizontal)





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						__snake_case				=		get_dataset(__lowerCAmelCase      ,	__lowerCAmelCase )
						print("Processing..." )
						__snake_case				=		update_image_and_anno(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase )

						for index, image in enumerate(__lowerCAmelCase ):
												# 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'''      ,	__lowerCAmelCase      ,	[cva.IMWRITE_JPEG_QUALITY, 85] )
												print(F'''Success {index+1}/{len(__lowerCAmelCase )} 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(__lowerCAmelCase )
												with open(F'''/{file_root}.txt'''      ,	"w" ) as outfile:
																		outfile.write("\n".join(line for line in annos_list ) )





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	tuple[list, list]:


						'''simple docstring'''






						__snake_case				=		[]
						__snake_case				=		[]
						for label_file in glob.glob(os.path.join(__lowerCAmelCase      ,	"*.txt" ) ):
												__snake_case				=		label_file.split(os.sep )[-1].rsplit("."      ,	1 )[0]
												with open(__lowerCAmelCase ) as in_file:
																		__snake_case				=		in_file.readlines()
												__snake_case				=		os.path.join(__lowerCAmelCase      ,	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(__lowerCAmelCase )
												labels.append(__lowerCAmelCase )
						return img_paths, labels





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 1 )						->	tuple[list, list, list]:


						'''simple docstring'''






						__snake_case				=		[]
						__snake_case				=		[]
						__snake_case				=		[]
						for idx in range(len(__lowerCAmelCase ) ):
												__snake_case				=		[]
												__snake_case				=		img_list[idx]
												path_list.append(__lowerCAmelCase )
												__snake_case				=		anno_list[idx]
												__snake_case				=		cva.imread(__lowerCAmelCase )
												if flip_type == 1:
																		__snake_case				=		cva.flip(__lowerCAmelCase      ,	__lowerCAmelCase )
																		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(__lowerCAmelCase      ,	__lowerCAmelCase )
																		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(__lowerCAmelCase )
												new_imgs_list.append(__lowerCAmelCase )
						return new_imgs_list, new_annos_lists, path_list





def 	_lowerCAmelCase       (    _lowerCAmelCase = 32 )						->	str:


						'''simple docstring'''






						assert number_char > 1, "The number of character should greater than 1"
						__snake_case				=		ascii_lowercase + digits
						return "".join(random.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) )


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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps
											__snake_case				=		use_tpu_fourier_optimizations
											__snake_case				=		tpu_short_seq_length





