
'''simple docstring'''







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

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



def 			_a		(	):
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= get_dataset(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						print("Processing..."  )
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= update_image_and_anno(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

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



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

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



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



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


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

'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor""", """tokenizer"""]
		a							:					Optional[int]       =					"""BlipImageProcessor"""
		a							:					Optional[int]       =					"""AutoTokenizer"""




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase      )

								# add QFormer tokenizer
								_SCREAMING_SNAKE_CASE				= qformer_tokenizer




		def __call__(				self					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = False					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = 0					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = True					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								if images is None and text is None:
														raise ValueError("You have to specify at least images or text."      )

								_SCREAMING_SNAKE_CASE				= BatchFeature()

								if text is not None:
														_SCREAMING_SNAKE_CASE				= self.tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														encoding.update(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.qformer_tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("input_ids"      )
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("attention_mask"      )

								if images is not None:
														_SCREAMING_SNAKE_CASE				= self.image_processor(UpperCamelCase					,						return_tensors=UpperCamelCase      )
														encoding.update(UpperCamelCase      )

								return encoding




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.batch_decode(*UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.decode(*UpperCamelCase					,						**UpperCamelCase      )




		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer.model_input_names
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




		def     lowercase       (				self					,						UpperCamelCase					,						**UpperCamelCase      ):
								if os.path.isfile(UpperCamelCase      ):
														raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'      )
								os.makedirs(UpperCamelCase					,						exist_ok=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= os.path.join(UpperCamelCase					,						"qformer_tokenizer"      )
								self.qformer_tokenizer.save_pretrained(UpperCamelCase      )
								return super().save_pretrained(UpperCamelCase					,						**UpperCamelCase      )




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCamelCase					,						subfolder="qformer_tokenizer"      )
								_SCREAMING_SNAKE_CASE				= cls._get_arguments_from_pretrained(UpperCamelCase					,						**UpperCamelCase      )
								args.append(UpperCamelCase      )
								return cls(*UpperCamelCase      )