'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


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

_A			:      Union[str, Any]					      =				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:      List[str]					      =				{
    '''vocab_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
        ),
        '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-reader''': (
            '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-reader''': (
            '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
        ),
    },
}

_A			:      int					      =				{
    '''google/realm-cc-news-pretrained-embedder''': 512,
    '''google/realm-cc-news-pretrained-encoder''': 512,
    '''google/realm-cc-news-pretrained-scorer''': 512,
    '''google/realm-cc-news-pretrained-openqa''': 512,
    '''google/realm-orqa-nq-openqa''': 512,
    '''google/realm-orqa-nq-reader''': 512,
    '''google/realm-orqa-wq-openqa''': 512,
    '''google/realm-orqa-wq-reader''': 512,
}

_A			:      Union[str, Any]					      =				{
    '''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       str      	=					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE     :       int      	=					PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					PRETRAINED_INIT_CONFIGURATION
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE     :       str      	=					RealmTokenizer
 def __init__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	int=None      ,       SCREAMING_SNAKE_CASE__   :	int=None      ,       SCREAMING_SNAKE_CASE__   :	Any=True      ,       SCREAMING_SNAKE_CASE__   :	List[str]="[UNK]"      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]="[SEP]"      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]="[PAD]"      ,       SCREAMING_SNAKE_CASE__   :	Tuple="[CLS]"      ,       SCREAMING_SNAKE_CASE__   :	List[Any]="[MASK]"      ,       SCREAMING_SNAKE_CASE__   :	List[str]=True      ,       SCREAMING_SNAKE_CASE__   :	List[str]=None      ,       **SCREAMING_SNAKE_CASE__   :	List[str]      ,       )  ->    Optional[int]:
  super().__init__(
      SCREAMING_SNAKE_CASE__      ,       tokenizer_file=SCREAMING_SNAKE_CASE__      ,       do_lower_case=SCREAMING_SNAKE_CASE__      ,       unk_token=SCREAMING_SNAKE_CASE__      ,       sep_token=SCREAMING_SNAKE_CASE__      ,       pad_token=SCREAMING_SNAKE_CASE__      ,       cls_token=SCREAMING_SNAKE_CASE__      ,       mask_token=SCREAMING_SNAKE_CASE__      ,       tokenize_chinese_chars=SCREAMING_SNAKE_CASE__      ,       strip_accents=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__      ,       )

  __lowerCAmelCase											=						json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
  if (
      normalizer_state.get("""lowercase"""      ,       SCREAMING_SNAKE_CASE__							) != do_lower_case
      or normalizer_state.get("""strip_accents"""      ,       SCREAMING_SNAKE_CASE__							) != strip_accents
      or normalizer_state.get("""handle_chinese_chars"""      ,       SCREAMING_SNAKE_CASE__							) != tokenize_chinese_chars
  ):
   __lowerCAmelCase											=						getattr(SCREAMING_SNAKE_CASE__      ,       normalizer_state.pop("""type"""							)							)
   __lowerCAmelCase											=						do_lower_case
   __lowerCAmelCase											=						strip_accents
   __lowerCAmelCase											=						tokenize_chinese_chars
   __lowerCAmelCase											=						normalizer_class(**SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						do_lower_case
 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    int:
  __lowerCAmelCase											=						PaddingStrategy.MAX_LENGTH

  __lowerCAmelCase											=						text
  __lowerCAmelCase											=						kwargs.pop("""text_pair"""      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						kwargs.pop("""return_tensors"""      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						{
      """input_ids""": [],
      """attention_mask""": [],
      """token_type_ids""": [],
  }

  for idx, candidate_text in enumerate(SCREAMING_SNAKE_CASE__							):
   if batch_text_pair is not None:
    __lowerCAmelCase											=						batch_text_pair[idx]
   else:
    __lowerCAmelCase											=						None

   __lowerCAmelCase											=						super().__call__(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       return_tensors=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						encoded_candidates.get("""input_ids"""							)
   __lowerCAmelCase											=						encoded_candidates.get("""attention_mask"""							)
   __lowerCAmelCase											=						encoded_candidates.get("""token_type_ids"""							)

   if encoded_input_ids is not None:
    output_data["input_ids"].append(SCREAMING_SNAKE_CASE__							)
   if encoded_attention_mask is not None:
    output_data["attention_mask"].append(SCREAMING_SNAKE_CASE__							)
   if encoded_token_type_ids is not None:
    output_data["token_type_ids"].append(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						{key: item for key, item in output_data.items() if len(SCREAMING_SNAKE_CASE__							) != 0}

  return BatchEncoding(SCREAMING_SNAKE_CASE__      ,       tensor_type=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=None							)  ->    List[str]:
  __lowerCAmelCase											=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

  if token_ids_a:
   output += token_ids_a + [self.sep_token_id]

  return output
 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	List[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[List[int]] = None							)  ->    List[int]:
  __lowerCAmelCase											=						[self.sep_token_id]
  __lowerCAmelCase											=						[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							) * [0] + len(token_ids_a + sep							) * [1]





 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[str] = None							)  ->    Tuple[str]:
  __lowerCAmelCase											=						self._tokenizer.model.save(SCREAMING_SNAKE_CASE__      ,       name=SCREAMING_SNAKE_CASE__							)
  return tuple(SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_A			:      List[Any]					      =				299792458

# Symbols
_A      ,     _A      ,     _A      ,     _A			:      Union[str, Any]					      =				symbols('''ct x y z''')







def      UpperCamelCase_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

 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_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

 return 1 / sqrt(1 - beta(snake_case_      ) ** 2      )







def      UpperCamelCase_						(					snake_case_       :       float      )					->     np.ndarray:






 '''simple docstring'''

 return np.array(
     [
         [gamma(snake_case_      ), -gamma(snake_case_      ) * beta(snake_case_      ), 0, 0],
         [-gamma(snake_case_      ) * beta(snake_case_      ), gamma(snake_case_      ), 0, 0],
         [0, 0, 1, 0],
         [0, 0, 0, 1],
     ]      )







def      UpperCamelCase_						(					snake_case_       :       float						,		snake_case_       :       np.ndarray | None = None      )					->     np.ndarray:






 '''simple docstring'''

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

 return transformation_matrix(snake_case_      ) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 _A			:      str					      =				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
 _A			:      int					      =				{ct: c, x: 1, y: 1, z: 1}
 _A			:      Any					      =				[four_vector[i].subs(sub_dict) for i in range(4)]

 print(f'\n{numerical_vector}')






