


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

import sentencepiece as spm

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


UpperCAmelCase_				:			Any   =	logging.get_logger(__name__)

UpperCAmelCase_				:			int   =	'▁'

UpperCAmelCase_				:			str   =	{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

UpperCAmelCase_				:			List[Any]   =	{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

UpperCAmelCase_				:			Optional[int]   =	{'vinai/bartpho-syllable': 1024}
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Any					       =						VOCAB_FILES_NAMES
      snake_case__    :					Tuple					       =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :					Dict					       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :					Dict					       =						['''input_ids''', '''attention_mask''']
      def __init__(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	List[str]="<s>"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="</s>"					,							SCREAMING_SNAKE_CASE__    :	Optional[int]="</s>"					,							SCREAMING_SNAKE_CASE__    :	Tuple="<s>"					,							SCREAMING_SNAKE_CASE__    :	Any="<unk>"					,							SCREAMING_SNAKE_CASE__    :	List[Any]="<pad>"					,							SCREAMING_SNAKE_CASE__    :	int="<mask>"					,							SCREAMING_SNAKE_CASE__    :	Optional[Dict[str, Any]] = None					,							**SCREAMING_SNAKE_CASE__    :	Tuple					,							)		->      None:
             # Mask token behave like a normal word, i.e. include the space before it
             a_ :		str							=  AddedToken(SCREAMING_SNAKE_CASE__					,							lstrip=SCREAMING_SNAKE_CASE__					,							rstrip=SCREAMING_SNAKE_CASE__					) if isinstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					) else mask_token

             a_ :		List[Any]							=  {} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=SCREAMING_SNAKE_CASE__					,							eos_token=SCREAMING_SNAKE_CASE__					,							unk_token=SCREAMING_SNAKE_CASE__					,							sep_token=SCREAMING_SNAKE_CASE__					,							cls_token=SCREAMING_SNAKE_CASE__					,							pad_token=SCREAMING_SNAKE_CASE__					,							mask_token=SCREAMING_SNAKE_CASE__					,							sp_model_kwargs=self.sp_model_kwargs					,							**SCREAMING_SNAKE_CASE__					,							)

             a_ :		Optional[int]							=  vocab_file
             a_ :		str							=  monolingual_vocab_file
             a_ :		Union[str, Any]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.Load(str(SCREAMING_SNAKE_CASE__					)					)

             # Load the reduced vocab

             # Keep order of special tokens for backward compatibility
             a_ :		int							=  {}
             a_ :		str							=  0
             for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                    if str(SCREAMING_SNAKE_CASE__					) not in self.fairseq_tokens_to_ids:
                           a_ :		Dict							=  cnt
                           cnt += 1
             with open(SCREAMING_SNAKE_CASE__					,							'r'					,							encoding='utf-8'					) as f:
                    for line in f.readlines():
                           a_ :		List[str]							=  line.strip().split()[0]
                           a_ :		Dict							=  len(self.fairseq_tokens_to_ids					)
             if str(SCREAMING_SNAKE_CASE__					) not in self.fairseq_tokens_to_ids:
                    a_ :		Dict							=  len(self.fairseq_tokens_to_ids					)

             a_ :		Any							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def __getstate__(	self    :	Optional[Any]					)		->      Union[str, Any]:
             a_ :		Optional[Any]							=  self.__dict__.copy()
             a_ :		Dict							=  None
             a_ :		List[Any]							=  self.sp_model.serialized_model_proto()
             return state
      def __setstate__(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	int					)		->      List[str]:
             a_ :		str							=  d

             # for backward compatibility
             if not hasattr(self					,							'sp_model_kwargs'					):
                    a_ :		int							=  {}

             a_ :		Any							=  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	List[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[List[int]] = None					)		->      List[int]:

             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             a_ :		Any							=  [self.cls_token_id]
             a_ :		Union[str, Any]							=  [self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	List[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[List[int]] = None					,							SCREAMING_SNAKE_CASE__    :	bool = False					)		->      List[int]:

             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__					)

             if token_ids_a is None:
                    return [1] + ([0] * len(SCREAMING_SNAKE_CASE__					)) + [1]
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE__					)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__					)) + [1]
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[List[int]] = None					)		->      List[int]:
             a_ :		int							=  [self.sep_token_id]
             a_ :		str							=  [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]
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[int]:
             return len(self.fairseq_ids_to_tokens					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Tuple:
             a_ :		Optional[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 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	str					)		->      List[str]:
             return self.sp_model.encode(SCREAMING_SNAKE_CASE__					,							out_type=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      List[Any]:
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             else:
                    return self.unk_token_id
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      List[Any]:
             return self.fairseq_ids_to_tokens[index]
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      int:
             a_ :		int							=  ''.join(SCREAMING_SNAKE_CASE__					).replace(SCREAMING_SNAKE_CASE__					,							' '					).strip()
             return out_string

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[str] = None					)		->      Tuple[str]:
             if not os.path.isdir(SCREAMING_SNAKE_CASE__					):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
                    return
             a_ :		Dict							=  os.path.join(
                 SCREAMING_SNAKE_CASE__					,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
             a_ :		Union[str, Any]							=  os.path.join(
                 SCREAMING_SNAKE_CASE__					,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
                           a_ :		Any							=  self.sp_model.serialized_model_proto()
                           fi.write(SCREAMING_SNAKE_CASE__					)

             if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
                 SCREAMING_SNAKE_CASE__					) and os.path.isfile(self.monolingual_vocab_file					):
                    copyfile(self.monolingual_vocab_file					,							SCREAMING_SNAKE_CASE__					)
             elif not os.path.isfile(self.monolingual_vocab_file					):
                    with open(SCREAMING_SNAKE_CASE__					,							'w'					,							encoding='utf-8'					) as fp:
                           for token in self.fairseq_tokens_to_ids:
                                  if token not in self.all_special_tokens:
                                         fp.write(F"""{str(SCREAMING_SNAKE_CASE__					)} \n"""					)

             return out_vocab_file, out_monolingual_vocab_file






from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase_				:			str   =	1.054571817e-34  # unit of ℏ : J * s

UpperCAmelCase_				:			Dict   =	3e8  # unit of c : m * s^-1







def 	SCREAMING_SNAKE_CASE_						(      __A			:      float  ,   __A			:      float  ,   __A			:      float    )     ->		dict[str, float]:

       """simple docstring"""

       if (force, area, distance).count(0    ) != 1:
              raise ValueError('One and only one argument must be 0'    )
       if force < 0:
              raise ValueError('Magnitude of force can not be negative'    )
       if distance < 0:
              raise ValueError('Distance can not be negative'    )
       if area < 0:
              raise ValueError('Area can not be negative'    )
       if force == 0:
              a_ :		Optional[Any]							=  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
                  2_40 * (distance) ** 4
              )
              return {"force": force}
       elif area == 0:
              a_ :		List[str]							=  (2_40 * force * (distance) ** 4) / (
                  REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
              )
              return {"area": area}
       elif distance == 0:
              a_ :		Tuple							=  (
                  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_40 * force)
              ) ** (1 / 4)
              return {"distance": distance}
       raise ValueError('One and only one argument must be 0'    )


# Run doctest
if __name__ == "__main__":
   import doctest

   doctest.testmod()


