






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 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= ["image_processor", "tokenizer"]
       lowerCamelCase							:				List[Any] 				= "BlipImageProcessor"
       lowerCamelCase							:				Any 				= "AutoTokenizer"



       def __init__(							self			,						a__			,						a__			,						a__    ) ->      Any:






            '''simple docstring'''





            super().__init__(a__			,						a__    )

            # add QFormer tokenizer
            __snake_case			:int          =						qformer_tokenizer



       def __call__(							self			,						a__ = None			,						a__ = None			,						a__ = True			,						a__ = False			,						a__ = None			,						a__ = None			,						a__ = 0			,						a__ = None			,						a__ = None			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = True			,						a__ = None			,						**a__			,						) ->      BatchFeature:






            '''simple docstring'''





            if images is None and text is None:
                 raise ValueError("""You have to specify at least images or text."""    )

            __snake_case			:str          =						BatchFeature()

            if text is not None:
                 __snake_case			:List[Any]          =						self.tokenizer(
                     text=a__			,						add_special_tokens=a__			,						padding=a__			,						truncation=a__			,						max_length=a__			,						stride=a__			,						pad_to_multiple_of=a__			,						return_attention_mask=a__			,						return_overflowing_tokens=a__			,						return_special_tokens_mask=a__			,						return_offsets_mapping=a__			,						return_token_type_ids=a__			,						return_length=a__			,						verbose=a__			,						return_tensors=a__			,						**a__			,						)
                 encoding.update(a__    )
                 __snake_case			:str          =						self.qformer_tokenizer(
                     text=a__			,						add_special_tokens=a__			,						padding=a__			,						truncation=a__			,						max_length=a__			,						stride=a__			,						pad_to_multiple_of=a__			,						return_attention_mask=a__			,						return_overflowing_tokens=a__			,						return_special_tokens_mask=a__			,						return_offsets_mapping=a__			,						return_token_type_ids=a__			,						return_length=a__			,						verbose=a__			,						return_tensors=a__			,						**a__			,						)
                 __snake_case			:Any          =						qformer_text_encoding.pop("""input_ids"""    )
                 __snake_case			:Union[str, Any]          =						qformer_text_encoding.pop("""attention_mask"""    )

            if images is not None:
                 __snake_case			:List[str]          =						self.image_processor(a__			,						return_tensors=a__    )
                 encoding.update(a__    )

            return encoding



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      Optional[Any]:






            '''simple docstring'''





            return self.tokenizer.batch_decode(*a__			,						**a__    )



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      Union[str, Any]:






            '''simple docstring'''





            return self.tokenizer.decode(*a__			,						**a__    )



       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.tokenizer.model_input_names
            __snake_case			:Any          =						self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )



       def 					__lowercase						(							self			,						a__			,						**a__    ) ->      int:






            '''simple docstring'''





            if os.path.isfile(a__    ):
                 raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''    )
            os.makedirs(a__			,						exist_ok=a__    )
            __snake_case			:List[Any]          =						os.path.join(a__			,						"""qformer_tokenizer"""    )
            self.qformer_tokenizer.save_pretrained(a__    )
            return super().save_pretrained(a__			,						**a__    )



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:str          =						AutoTokenizer.from_pretrained(a__			,						subfolder="""qformer_tokenizer"""    )
            __snake_case			:List[Any]          =						cls._get_arguments_from_pretrained(a__			,						**a__    )
            args.append(a__    )
            return cls(*a__    )










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

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






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case       , __snake_case			:List[Any]          =						get_dataset(snake_case__	,snake_case__  )
     print("""Processing..."""  )
     __snake_case       , __snake_case       , __snake_case			:Tuple          =						update_image_and_anno(snake_case__	,snake_case__	,snake_case__  )

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






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   str  ):




     '''simple docstring'''






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

          __snake_case			:List[str]          =						[]
          for obj_list in obj_lists:
               __snake_case			:int          =						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(snake_case__  )
          labels.append(snake_case__  )
     return img_paths, labels






def   UpperCamelCase  (       snake_case__     :   list	,snake_case__     :   list	,snake_case__     :   int = 1  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   int = 32  ):




     '''simple docstring'''






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


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


