



"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCamelCase       	=       logging.get_logger(__name__)

lowerCamelCase       	=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase       	=       {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase       	=       {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					VOCAB_FILES_NAMES
       UpperCamelCase						=					PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase						=					['''input_ids''', '''attention_mask''']
       UpperCamelCase						=					RobertaTokenizer



       def __init__(							self      :  List[str]		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  Optional[Any]=None		,    _UpperCAmelCase      :  List[str]="replace"		,    _UpperCAmelCase      :  Tuple="<s>"		,    _UpperCAmelCase      :  Union[str, Any]="</s>"		,    _UpperCAmelCase      :  List[str]="</s>"		,    _UpperCAmelCase      :  Union[str, Any]="<s>"		,    _UpperCAmelCase      :  Union[str, Any]="<unk>"		,    _UpperCAmelCase      :  Dict="<pad>"		,    _UpperCAmelCase      :  List[str]="<mask>"		,    _UpperCAmelCase      :  List[Any]=False		,    _UpperCAmelCase      :  Any=True		,    **_UpperCAmelCase      :  Dict		,    )			->						Optional[int]:


             '''simple docstring'''




             super().__init__(
                 _UpperCAmelCase		,    _UpperCAmelCase		,    tokenizer_file=_UpperCAmelCase		,    errors=_UpperCAmelCase		,    bos_token=_UpperCAmelCase		,    eos_token=_UpperCAmelCase		,    sep_token=_UpperCAmelCase		,    cls_token=_UpperCAmelCase		,    unk_token=_UpperCAmelCase		,    pad_token=_UpperCAmelCase		,    mask_token=_UpperCAmelCase		,    add_prefix_space=_UpperCAmelCase		,    trim_offsets=_UpperCAmelCase		,    **_UpperCAmelCase		,    )

             UpperCAmelCase_      			=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
             if pre_tok_state.get("add_prefix_space"		,    _UpperCAmelCase   ) != add_prefix_space:
                   UpperCAmelCase_      			=							getattr(_UpperCAmelCase		,    pre_tok_state.pop("type"   )   )
                   UpperCAmelCase_      			=							add_prefix_space
                   UpperCAmelCase_      			=							pre_tok_class(**_UpperCAmelCase   )

             UpperCAmelCase_      			=							add_prefix_space

             UpperCAmelCase_      			=							"post_processor"
             UpperCAmelCase_      			=							getattr(self.backend_tokenizer		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             if tokenizer_component_instance:
                   UpperCAmelCase_      			=							json.loads(tokenizer_component_instance.__getstate__()   )

                   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                   if "sep" in state:
                         UpperCAmelCase_      			=							tuple(state["sep"]   )
                   if "cls" in state:
                         UpperCAmelCase_      			=							tuple(state["cls"]   )

                   UpperCAmelCase_      			=							False

                   if state.get("add_prefix_space"		,    _UpperCAmelCase   ) != add_prefix_space:
                         UpperCAmelCase_      			=							add_prefix_space
                         UpperCAmelCase_      			=							True

                   if state.get("trim_offsets"		,    _UpperCAmelCase   ) != trim_offsets:
                         UpperCAmelCase_      			=							trim_offsets
                         UpperCAmelCase_      			=							True

                   if changes_to_apply:
                         UpperCAmelCase_      			=							getattr(_UpperCAmelCase		,    state.pop("type"   )   )
                         UpperCAmelCase_      			=							component_class(**_UpperCAmelCase   )
                         setattr(self.backend_tokenizer		,    _UpperCAmelCase		,    _UpperCAmelCase   )



       @property
       def 					lowercase__    (							self      :  int   )			->						str:


             '''simple docstring'''




             if self._mask_token is None:
                   if self.verbose:
                         logger.error("Using mask_token, but it is not set yet."   )
                   return None
             return str(self._mask_token   )



       @mask_token.setter
       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  int   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							AddedToken(_UpperCAmelCase		,    lstrip=_UpperCAmelCase		,    rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ) else value
             UpperCAmelCase_      			=							value



       def 					lowercase__    (							self      :  List[Any]		,    *_UpperCAmelCase      :  List[Any]		,    **_UpperCAmelCase      :  str   )			->						BatchEncoding:


             '''simple docstring'''




             UpperCAmelCase_      			=							kwargs.get("is_split_into_words"		,    _UpperCAmelCase   )
             assert self.add_prefix_space or not is_split_into_words, (
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                 "to use it with pretokenized inputs."
             )

             return super()._batch_encode_plus(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Optional[Any]		,    *_UpperCAmelCase      :  Any		,    **_UpperCAmelCase      :  Optional[Any]   )			->						BatchEncoding:


             '''simple docstring'''




             UpperCAmelCase_      			=							kwargs.get("is_split_into_words"		,    _UpperCAmelCase   )

             assert self.add_prefix_space or not is_split_into_words, (
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                 "to use it with pretokenized inputs."
             )

             return super()._encode_plus(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[str]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Optional[str] = None   )			->						Tuple[str]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._tokenizer.model.save(_UpperCAmelCase		,    name=_UpperCAmelCase   )
             return tuple(_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  List[Any]=None   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                   return output

             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  List[int]		,    _UpperCAmelCase      :  Optional[List[int]] = None   )			->						List[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							[self.sep_token_id]
             UpperCAmelCase_      			=							[self.cls_token_id]

             if token_ids_a is None:
                   return len(cls + token_ids_a + sep   ) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]









"""simple docstring"""





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        a__     (				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							get_dataset(lowerCAmelCase__     ,      lowerCAmelCase__				)
      print("Processing..."				)
      UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							update_image_and_anno(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)

      for index, image in enumerate(lowerCAmelCase__				):
            # 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"""     ,      lowerCAmelCase__     ,      [cva.IMWRITE_JPEG_QUALITY, 85]				)
            print(f"""Success {index+1}/{len(lowerCAmelCase__				)} 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(lowerCAmelCase__				)
            with open(f"""/{file_root}.txt"""     ,      "w"				) as outfile:
                  outfile.write("\n".join(line for line in annos_list				)				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for label_file in glob.glob(os.path.join(lowerCAmelCase__     ,      "*.txt"				)				):
            UpperCAmelCase_      			=							label_file.split(os.sep				)[-1].rsplit("."     ,      1				)[0]
            with open(lowerCAmelCase__				) as in_file:
                  UpperCAmelCase_      			=							in_file.readlines()
            UpperCAmelCase_      			=							os.path.join(lowerCAmelCase__     ,      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(lowerCAmelCase__				)
            labels.append(lowerCAmelCase__				)
      return img_paths, labels
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__ = 1				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for idx in range(len(lowerCAmelCase__				)				):
            UpperCAmelCase_      			=							[]
            UpperCAmelCase_      			=							img_list[idx]
            path_list.append(lowerCAmelCase__				)
            UpperCAmelCase_      			=							anno_list[idx]
            UpperCAmelCase_      			=							cva.imread(lowerCAmelCase__				)
            if flip_type == 1:
                  UpperCAmelCase_      			=							cva.flip(lowerCAmelCase__     ,      lowerCAmelCase__				)
                  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(lowerCAmelCase__     ,      lowerCAmelCase__				)
                  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(lowerCAmelCase__				)
            new_imgs_list.append(lowerCAmelCase__				)
      return new_imgs_list, new_annos_lists, path_list
def        a__     (				lowerCAmelCase__ = 32				):
      assert number_char > 1, "The number of character should greater than 1"
      UpperCAmelCase_      			=							ascii_lowercase + digits
      return "".join(random.choice(lowerCAmelCase__				) for _ in range(lowerCAmelCase__				)				)


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




