
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
     import torch


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


lowerCamelCase__							=     logging.get_logger(__name__)
lowerCamelCase__							=     {"""vocab_file""": """spiece.model"""}

lowerCamelCase__							=     {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowerCamelCase__							=     {
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				List[str] 				= VOCAB_FILES_NAMES
       lowerCamelCase							:				List[Any] 				= PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase							:				Union[str, Any] 				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase							:				Tuple 				= ["input_ids", "attention_mask"]



       def __init__(							self			,						a__			,						a__=False			,						a__=False			,						a__=False			,						a__=None			,						a__=None			,						a__=None			,						a__=None			,						a__ = None			,						**a__			,						) ->      None:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						{} if sp_model_kwargs is None else sp_model_kwargs

            __snake_case			:str          =						kwargs.get("""name_or_path"""    )
            if name_or_path is None:
                 logger.warning(
                     """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
                     """ you are testing the model, this can safely be ignored"""    )
                 __snake_case			:Optional[int]          =						"""None"""

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            __snake_case			:Dict          =						"""<|endoftext|>""" if eos_token is None else eos_token
            __snake_case			:str          =						"""<unk>""" if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                 __snake_case			:Union[str, Any]          =						unk_token if pad_token is None else pad_token
                 __snake_case			:Optional[int]          =						eos_token if bos_token is None else bos_token
            else:
                 __snake_case			:Any          =						"""<pad>""" if pad_token is None else pad_token
                 __snake_case			:Optional[int]          =						"""<s>""" if bos_token is None else bos_token

            super().__init__(
                do_lower_case=a__			,						remove_space=a__			,						keep_accents=a__			,						bos_token=a__			,						eos_token=a__			,						unk_token=a__			,						pad_token=a__			,						sp_model_kwargs=self.sp_model_kwargs			,						**a__			,						)

            __snake_case			:Union[str, Any]          =						do_lower_case
            __snake_case			:Optional[Any]          =						remove_space
            __snake_case			:Dict          =						keep_accents
            __snake_case			:Dict          =						vocab_file

            __snake_case			:Union[str, Any]          =						spm.SentencePieceProcessor(**self.sp_model_kwargs    )
            self.sp_model.Load(a__    )

            # Used for whitespace normalization in input texts
            # fmt : off
            __snake_case			:int          =						{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
            # fmt : on

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            __snake_case			:Optional[int]          =						re.compile(
                F'''[{''.join(map(a__			,						list(range(0			,						9    )    ) + list(range(11			,						32    )    ) + list(range(1_27			,						1_60    )    ) + [1_60, 1_73, 82_03]    )    )}]'''    )



       def __getstate__(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.__dict__.copy()
            __snake_case			:Optional[Any]          =						None
            return state



       def __setstate__(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						d

            # for backward compatibility
            if not hasattr(self			,						"""sp_model_kwargs"""    ):
                 __snake_case			:Tuple          =						{}

            __snake_case			:Optional[int]          =						spm.SentencePieceProcessor(**self.sp_model_kwargs    )
            self.sp_model.Load(self.vocab_file    )



       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return len(self.sp_model    )



       def 					__lowercase						(							self			,						a__    ) ->      str:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.non_printing_characters_re.sub(""""""			,						a__    )

            # Normalize whitespaces
            __snake_case			:str          =						"""""".join([char if char not in self.whitespaces else """ """ for char in text]    )

            # NFC Unicode normalization
            __snake_case			:Union[str, Any]          =						unicodedata.normalize("""NFC"""			,						a__    )
            return text



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






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.preprocess_text(a__    )
            return self.sp_model.encode(a__			,						out_type=a__    )



       def 					__lowercase						(							self			,						a__    ) ->      int:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__    )



       def 					__lowercase						(							self			,						a__    ) ->      str:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__    )



       @staticmethod
       def 					__lowercase						(							a__    ) ->      str:






            '''simple docstring'''





            return out_string



       def 					__lowercase						(							self			,						a__    ) ->      str:






            '''simple docstring'''





            __snake_case			:List[str]          =						[]
            __snake_case			:Dict          =						""""""
            __snake_case			:str          =						False
            for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                      # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                      if not prev_is_special:
                           out_string += " "

                      out_string += self.sp_model.decode(a__    ) + token
                      __snake_case			:Any          =						True
                      __snake_case			:Tuple          =						[]
                 else:
                      current_sub_tokens.append(a__    )
                      __snake_case			:List[Any]          =						False
            out_string += self.sp_model.decode(a__    )

            return out_string



       def 					__lowercase						(							self    ) ->      Dict[str, int]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						{self.convert_ids_to_tokens(a__    ): i for i in range(self.vocab_size    )}
            vocab.update(self.added_tokens_encoder    )
            return vocab



       def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__    ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
                 return
            __snake_case			:List[str]          =						os.path.join(
                a__			,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

            if os.path.abspath(self.vocab_file    ) != os.path.abspath(a__    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file			,						a__    )
            elif not os.path.isfile(self.vocab_file    ):
                 with open(a__			,						"""wb"""    ) as fi:
                      __snake_case			:str          =						self.sp_model.serialized_model_proto()
                      fi.write(a__    )

            return (out_vocab_file,)



       def 					__lowercase						(							self			,						a__			,						a__ = False    ) ->      Union[List[int], List[List[int]], "torch.Tensor"]:






            '''simple docstring'''





            if isinstance(a__			,						a__    ):
                 __snake_case			:str          =						self.preprocess_text(a__    )
                 __snake_case			:Dict          =						self.sp_model.encode(a__    )
            else:
                 __snake_case			:int          =						[self.preprocess_text(a__    ) for t in text]
                 __snake_case			:Any          =						self.sp_model.encode(a__    )

            if return_tensors is True or return_tensors == "pt":
                 __snake_case			:Dict          =						torch.tensor(a__    )

            return token_ids



       def 					__lowercase						(							self			,						a__    ) ->      str:






            '''simple docstring'''





            return self.sp_model.decode(a__    )



       def 					__lowercase						(							self			,						a__    ) ->      List[int]:






            '''simple docstring'''





            __snake_case			:Dict          =						[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
            __snake_case			:int          =						(
                F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(a__    ) + F'''{self.bos_token}Bot:'''
            )
            return self.encode(text=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 ✅""")


