



"""simple docstring"""


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

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

def  _lowerCAmelCase      (   ):
						UpperCAmelCase      ,    UpperCAmelCase             = get_dataset(lowercase_		,	lowercase_						)
						print('Processing...'						)
						UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = update_image_and_anno(lowercase_		,	lowercase_		,	lowercase_						)

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

def  _lowerCAmelCase      (   lowercase_		,	lowercase_						):
						UpperCAmelCase             = []
						UpperCAmelCase             = []
						for label_file in glob.glob(os.path.join(lowercase_		,	'*.txt'						)						):
												UpperCAmelCase             = label_file.split(os.sep						)[-1].rsplit('.'		,	1						)[0]
												with open(lowercase_						) as in_file:
																		UpperCAmelCase             = in_file.readlines()
												UpperCAmelCase             = os.path.join(lowercase_		,	F"""{label_name}.jpg"""						)

												UpperCAmelCase             = []
												for obj_list in obj_lists:
																		UpperCAmelCase             = 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(lowercase_						)
												labels.append(lowercase_						)
						return img_paths, labels

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

def  _lowerCAmelCase      (   lowercase_ = 32						):
						assert number_char > 1, "The number of character should greater than 1"
						UpperCAmelCase             = ascii_lowercase + digits
						return "".join(random.choice(lowercase_						) for _ in range(lowercase_						)						)


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











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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "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 						lowercase							(   UpperCamelCase__		):
     _a							 =						"fnet"

     def __init__(  self		,     _a=3_2000		,     _a=768		,     _a=12		,     _a=3072		,     _a="gelu_new"		,     _a=0.1		,     _a=512		,     _a=4		,     _a=0.02		,     _a=1e-12		,     _a=False		,     _a=512		,     _a=3		,     _a=1		,     _a=2		,     **_a		,     )						->							int:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Any			=						vocab_size
           _A							:     str			=						max_position_embeddings
           _A							:     Optional[Any]			=						hidden_size
           _A							:     List[str]			=						num_hidden_layers
           _A							:     List[str]			=						intermediate_size
           _A							:     List[Any]			=						hidden_act
           _A							:     List[str]			=						hidden_dropout_prob
           _A							:     List[str]			=						initializer_range
           _A							:     List[Any]			=						type_vocab_size
           _A							:     List[Any]			=						layer_norm_eps
           _A							:     List[str]			=						use_tpu_fourier_optimizations
           _A							:     str			=						tpu_short_seq_length

