
"""simple docstring"""



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase__        =					299792458

# Symbols
UpperCAmelCase__        =					symbols('ct x y z')
def    _UpperCAmelCase (					__lowerCamelCase  :			List[str]						)	->   float:






  if velocity > c:
    raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''						)
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError('''Speed must be greater than or equal to 1!'''						)

  return velocity / c
def    _UpperCAmelCase (					__lowerCamelCase  :			Dict						)	->   float:
  return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_						) ** 2						)
def    _UpperCAmelCase (					__lowerCamelCase  :			List[Any]						)	->   np.ndarray:
  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_						), -gamma(SCREAMING_SNAKE_CASE_						) * beta(SCREAMING_SNAKE_CASE_						), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_						) * beta(SCREAMING_SNAKE_CASE_						), gamma(SCREAMING_SNAKE_CASE_						), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]						)
def    _UpperCAmelCase (					__lowerCamelCase  :			Optional[Any]					,   __lowerCamelCase  :			Optional[Any] = None						)	->   np.ndarray:






  if event is None:
    _snake_case   	=			np.array([ct, x, y, z]						)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_						) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 UpperCAmelCase__        =					transform(29979245)
 print('Example of four vector: ')
 print(F"ct\' = {four_vector[0]}")
 print(F"x\' = {four_vector[1]}")
 print(F"y\' = {four_vector[2]}")
 print(F"z\' = {four_vector[3]}")

 # Substitute symbols with numerical values
 UpperCAmelCase__        =					{ct: c, x: 1, y: 1, z: 1}
 UpperCAmelCase__        =					[four_vector[i].subs(sub_dict) for i in range(4)]

 print(F"\n{numerical_vector}")






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     None

SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE		:int						=     {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE		:List[str]						=     {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE		:Optional[Any]						=     """▁"""






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Any										=      ["""input_ids""", """attention_mask"""]
     UpperCamelCase_	:List[Any]										=      BarthezTokenizer






     def __init__(				self		,						_lowercase=None		,						_lowercase=None		,						_lowercase="<s>"		,						_lowercase="</s>"		,						_lowercase="</s>"		,						_lowercase="<s>"		,						_lowercase="<unk>"		,						_lowercase="<pad>"		,						_lowercase="<mask>"		,						**_lowercase		,						)->     Optional[int]:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase_			  =						AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase	) if isinstance(_lowercase		,						_lowercase	) else mask_token

         super().__init__(
             _lowercase		,						tokenizer_file=_lowercase		,						bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						**_lowercase		,						)

         UpperCamelCase_			  =						vocab_file
         UpperCamelCase_			  =						False if not self.vocab_file else True






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]
         UpperCamelCase_			  =						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:
         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]






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                 "tokenizer."	)

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

         if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowercase	):
             copyfile(self.vocab_file		,						_lowercase	)

         return (out_vocab_file,)
