


"""simple docstring"""






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

import sentencepiece as spm

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


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

UpperCamelCase__							:Union[str, Any]       =  logging.get_logger(__name__)

UpperCamelCase__							:Union[str, Any]       =  {"""vocab_file""": """tokenizer.model"""}

UpperCamelCase__							:List[str]       =  {
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
UpperCamelCase__							:int       =  {
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCamelCase__							:Dict       =  """▁"""

UpperCamelCase__							:List[Any]       =  """[INST]""", """[/INST]"""
UpperCamelCase__							:Optional[Any]       =  """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCamelCase__							:Any       =  """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""






class     A(     lowerCamelCase__		):

  """simple docstring"""





  A         =       VOCAB_FILES_NAMES
  A         =       PRETRAINED_VOCAB_FILES_MAP
  A         =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A         =       ["input_ids", "attention_mask"]
  def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__="<unk>"				,							SCREAMING_SNAKE_CASE__="<s>"				,							SCREAMING_SNAKE_CASE__="</s>"				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=True				,							**SCREAMING_SNAKE_CASE__				,							)			->  str:

     """simple docstring"""



     _UpperCamelCase				:Any				    =  {} if sp_model_kwargs is None else sp_model_kwargs
     _UpperCamelCase				:Dict				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else bos_token
     _UpperCamelCase				:Tuple				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else eos_token
     _UpperCamelCase				:Optional[int]				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else unk_token
     _UpperCamelCase				:Optional[Any]				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else pad_token
     super().__init__(
         bos_token=SCREAMING_SNAKE_CASE__				,							eos_token=SCREAMING_SNAKE_CASE__				,							unk_token=SCREAMING_SNAKE_CASE__				,							pad_token=SCREAMING_SNAKE_CASE__				,							add_bos_token=SCREAMING_SNAKE_CASE__				,							add_eos_token=SCREAMING_SNAKE_CASE__				,							sp_model_kwargs=self.sp_model_kwargs				,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__				,							legacy=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__				,							)
     if legacy:
        logger.warning_once(
            f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
            ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''      )
     _UpperCamelCase				:List[Any]				    =  legacy
     _UpperCamelCase				:Optional[Any]				    =  vocab_file
     _UpperCamelCase				:Optional[int]				    =  add_bos_token
     _UpperCamelCase				:List[str]				    =  add_eos_token
     _UpperCamelCase				:List[str]				    =  spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(SCREAMING_SNAKE_CASE__      )
  def __getstate__(   self      )			->  Tuple:

     """simple docstring"""



     _UpperCamelCase				:Any				    =  self.__dict__.copy()
     _UpperCamelCase				:Union[str, Any]				    =  None
     _UpperCamelCase				:List[str]				    =  self.sp_model.serialized_model_proto()
     return state
  def __setstate__(   self				,							SCREAMING_SNAKE_CASE__      )			->  Optional[Any]:

     """simple docstring"""



     _UpperCamelCase				:Tuple				    =  d
     _UpperCamelCase				:Any				    =  spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )
  @property
  def      _UpperCamelCase(   self      )			->  Tuple:

     """simple docstring"""



     return self.sp_model.get_piece_size()
  def      _UpperCamelCase(   self      )			->  Union[str, Any]:

     """simple docstring"""



     _UpperCamelCase				:List[Any]				    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )			->  List[str]:

     """simple docstring"""



     if not self.legacy:
        _UpperCamelCase				:Any				    =  SPIECE_UNDERLINE + text.replace(SCREAMING_SNAKE_CASE__				,							''' '''      )
     return super().tokenize(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

     """simple docstring"""



     if not self.legacy:
        _UpperCamelCase				:Any				    =  text.startswith(SCREAMING_SNAKE_CASE__      )
        if is_first:
           _UpperCamelCase				:List[str]				    =  text[1:]

     _UpperCamelCase				:Tuple				    =  self.sp_model.encode(SCREAMING_SNAKE_CASE__				,							out_type=SCREAMING_SNAKE_CASE__      )

     if not self.legacy and not is_first and not text.startswith(''' '''      ) and tokens[0].startswith(SCREAMING_SNAKE_CASE__      ):
        _UpperCamelCase				:Tuple				    =  ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
     return tokens
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  str:

     """simple docstring"""



     return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__      )
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Dict:

     """simple docstring"""



     _UpperCamelCase				:Dict				    =  self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__      )
     return token
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Union[str, Any]:

     """simple docstring"""



     _UpperCamelCase				:Union[str, Any]				    =  []
     _UpperCamelCase				:List[str]				    =  ''''''
     _UpperCamelCase				:str				    =  False
     for i, token in enumerate(SCREAMING_SNAKE_CASE__      ):
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
           if not prev_is_special and i != 0:
              out_string += " "
           out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__      ) + token
           _UpperCamelCase				:Union[str, Any]				    =  True
           _UpperCamelCase				:Any				    =  []
        else:
           current_sub_tokens.append(SCREAMING_SNAKE_CASE__      )
           _UpperCamelCase				:List[Any]				    =  False
     out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__      )
     return out_string
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None      )			->  Tuple[str]:

     """simple docstring"""



     if not os.path.isdir(SCREAMING_SNAKE_CASE__      ):
        logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
        return
     _UpperCamelCase				:Tuple				    =  os.path.join(
         SCREAMING_SNAKE_CASE__				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

     if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE__      ) and os.path.isfile(self.vocab_file      ):
        copyfile(self.vocab_file				,							SCREAMING_SNAKE_CASE__      )
     elif not os.path.isfile(self.vocab_file      ):
        with open(SCREAMING_SNAKE_CASE__				,							'''wb'''      ) as fi:
           _UpperCamelCase				:List[Any]				    =  self.sp_model.serialized_model_proto()
           fi.write(SCREAMING_SNAKE_CASE__      )

     return (out_vocab_file,)
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=None      )			->  Union[str, Any]:

     """simple docstring"""



     _UpperCamelCase				:int				    =  [self.bos_token_id] if self.add_bos_token else []
     _UpperCamelCase				:str				    =  [self.eos_token_id] if self.add_eos_token else []

     _UpperCamelCase				:Optional[int]				    =  bos_token_id + token_ids_a + eos_token_id

     if token_ids_a is not None:
        _UpperCamelCase				:List[str]				    =  output + bos_token_id + token_ids_a + eos_token_id

     return output
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = False      )			->  List[int]:

     """simple docstring"""



     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=SCREAMING_SNAKE_CASE__				,							token_ids_a=SCREAMING_SNAKE_CASE__				,							already_has_special_tokens=SCREAMING_SNAKE_CASE__      )

     _UpperCamelCase				:Tuple				    =  [1] if self.add_bos_token else []
     _UpperCamelCase				:List[Any]				    =  [1] if self.add_eos_token else []

     if token_ids_a is None:
        return bos_token_id + ([0] * len(SCREAMING_SNAKE_CASE__      )) + eos_token_id
     return (
         bos_token_id
         + ([0] * len(SCREAMING_SNAKE_CASE__      ))
         + eos_token_id
         + bos_token_id
         + ([0] * len(SCREAMING_SNAKE_CASE__      ))
         + eos_token_id
     )
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None      )			->  List[int]:

     """simple docstring"""



     _UpperCamelCase				:str				    =  [self.bos_token_id] if self.add_bos_token else []
     _UpperCamelCase				:Optional[int]				    =  [self.eos_token_id] if self.add_eos_token else []

     _UpperCamelCase				:Tuple				    =  [0] * len(bos_token_id + token_ids_a + eos_token_id      )

     if token_ids_a is not None:
        output += [1] * len(bos_token_id + token_ids_a + eos_token_id      )

     return output





  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[int]:

     """simple docstring"""



     _UpperCamelCase				:str				    =  list(conversation.iter_texts()      )
     if not all([is_user for is_user, msg in dialogue[::2]]      ) or not all(
         [not is_user for is_user, msg in dialogue[1::2]]      ):
        raise ValueError(
            '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''      )

     _UpperCamelCase				:List[int]				    =  []
     if len(conversation.past_user_inputs      ) > 0:
        if not conversation.past_user_inputs[0].startswith(SCREAMING_SNAKE_CASE__      ) or E_SYS not in conversation.past_user_inputs[0]:
           _UpperCamelCase				:List[Any]				    =  (
               B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
           )
     elif not dialogue[0][1].startswith(SCREAMING_SNAKE_CASE__      ) or E_SYS not in dialogue[0][1]:
        _UpperCamelCase				:List[Any]				    =  (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

     dialog_tokens += sum(
         [
             [self.bos_token_id]
             + self.encode(
                 f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
             + [self.eos_token_id]
             for prompt, answer in zip(dialogue[::2]				,							dialogue[1::2]      )
         ]				,							[]				,							)
     if not (dialogue[-1][0]):
        raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"      )
     dialog_tokens += [self.bos_token_id] + self.encode(
         f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
     return dialog_tokens





"""simple docstring"""






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


if __name__ == "__main__":
					# read original image
					UpperCamelCase__							:List[str]       =  imread("""../image_data/lena.jpg""")
					# turn image in gray scale value
					UpperCamelCase__							:Union[str, Any]       =  cvtColor(img, COLOR_BGR2GRAY)

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

					# show result images
					imshow("""sobel filter""", sobel_grad)
					imshow("""sobel theta""", sobel_theta)
					waitKey(0)

