






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  (     ) ->   None:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__       =						get_dataset(UpperCamelCase_    ,   UpperCamelCase_   )
						print("Processing..."   )
						lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						update_image_and_anno(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )

						for index, image in enumerate(UpperCamelCase_   ):
												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__       =						random_chars(32   )
												lowerCAmelCase__       =						paths[index].split(os.sep   )[-1].rsplit("."    ,   1   )[0]
												lowerCAmelCase__       =						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}"   )
												lowerCAmelCase__       =						[]
												for anno in new_annos[index]:
																		lowerCAmelCase__       =						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   ) ->   tuple[list, list]:
						"""simple docstring"""







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

												lowerCAmelCase__       =						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__       =						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   ) ->   tuple[list, list, list]:
						"""simple docstring"""







						lowerCAmelCase__       =						[]
						lowerCAmelCase__       =						[]
						lowerCAmelCase__       =						[]
						for idx in range(len(UpperCamelCase_   )   ):
												lowerCAmelCase__       =						[]
												lowerCAmelCase__       =						img_list[idx]
												path_list.append(UpperCamelCase_   )
												lowerCAmelCase__       =						anno_list[idx]
												lowerCAmelCase__       =						cva.imread(UpperCamelCase_   )
												if flip_type == 1:
																		lowerCAmelCase__       =						cva.flip(UpperCamelCase_    ,   UpperCamelCase_   )
																		for bbox in img_annos:
																								lowerCAmelCase__       =						1 - bbox[1]
																								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]   )
												elif flip_type == 0:
																		lowerCAmelCase__       =						cva.flip(UpperCamelCase_    ,   UpperCamelCase_   )
																		for bbox in img_annos:
																								lowerCAmelCase__       =						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   ) ->   str:
						"""simple docstring"""







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


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














import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

a_							=    logging.getLogger()


def        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						os.path.join(UpperCamelCase_    ,   "all_results.json"   )
						if os.path.exists(UpperCamelCase_   ):
												with open(UpperCamelCase_    ,   "r"   ) as f:
																		lowerCAmelCase__       =						json.load(UpperCamelCase_   )
						else:
												raise ValueError(F"can't find {path}"   )
						return results


a_							=    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							import xla_spawn

							lowerCAmelCase__       =						self.get_auto_remove_tmp_dir()
							lowerCAmelCase__       =						F"\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

							with patch.object(__UpperCAmelCase		,    "argv"		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						time()
													xla_spawn.main()
													lowerCAmelCase__       =						time()

													lowerCAmelCase__       =						get_results(__UpperCAmelCase       )
													self.assertGreaterEqual(result["eval_accuracy"]		,    0.75       )

													# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
													self.assertLess(end - start		,    500       )


	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							import xla_spawn

							lowerCAmelCase__       =						"\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
							with patch.object(__UpperCAmelCase		,    "argv"		,    __UpperCAmelCase       ):
													xla_spawn.main()






