'''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_bart import BartTokenizer


_lowerCAmelCase	:int						=     logging.get_logger(__name__)


_lowerCAmelCase	:List[Any]						=     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase	:Optional[int]						=     {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

_lowerCAmelCase	:Optional[int]						=     {
    """facebook/bart-base""": 1_024,
    """facebook/bart-large""": 1_024,
    """facebook/bart-large-mnli""": 1_024,
    """facebook/bart-large-cnn""": 1_024,
    """facebook/bart-large-xsum""": 1_024,
    """yjernite/bart_eli5""": 1_024,
}






class 					UpperCAmelCase			(							__UpperCamelCase			):




    '''simple docstring'''







    snake_case__				: str						    =    VOCAB_FILES_NAMES
    snake_case__				: Tuple						    =    PRETRAINED_VOCAB_FILES_MAP
    snake_case__				: str						    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__				: Any						    =    ['input_ids', 'attention_mask']
    snake_case__				: Union[str, Any]						    =    BartTokenizer






    def __init__(							self     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__="replace"     ,				lowercase__="<s>"     ,				lowercase__="</s>"     ,				lowercase__="</s>"     ,				lowercase__="<s>"     ,				lowercase__="<unk>"     ,				lowercase__="<pad>"     ,				lowercase__="<mask>"     ,				lowercase__=False     ,				lowercase__=True     ,				**lowercase__     ,				)				->			Any:
         super().__init__(
             lowercase__     ,				lowercase__     ,				tokenizer_file=lowercase__     ,				errors=lowercase__     ,				bos_token=lowercase__     ,				eos_token=lowercase__     ,				sep_token=lowercase__     ,				cls_token=lowercase__     ,				unk_token=lowercase__     ,				pad_token=lowercase__     ,				mask_token=lowercase__     ,				add_prefix_space=lowercase__     ,				trim_offsets=lowercase__     ,				**lowercase__     ,				)

         SCREAMING_SNAKE_CASE       :      Any     						=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
         if pre_tok_state.get('add_prefix_space'     ,				lowercase__			) != add_prefix_space:
              SCREAMING_SNAKE_CASE       :      int     						=							getattr(lowercase__     ,				pre_tok_state.pop('type'			)			)
              SCREAMING_SNAKE_CASE       :      Optional[int]     						=							add_prefix_space
              SCREAMING_SNAKE_CASE       :      int     						=							pre_tok_class(**lowercase__			)

         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         SCREAMING_SNAKE_CASE       :      Dict     						=							'post_processor'
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							getattr(self.backend_tokenizer     ,				lowercase__     ,				lowercase__			)
         if tokenizer_component_instance:
              SCREAMING_SNAKE_CASE       :      List[Any]     						=							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:
                   SCREAMING_SNAKE_CASE       :      Dict     						=							tuple(state['sep']			)
              if "cls" in state:
                   SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							tuple(state['cls']			)

              SCREAMING_SNAKE_CASE       :      List[Any]     						=							False

              if state.get('add_prefix_space'     ,				lowercase__			) != add_prefix_space:
                   SCREAMING_SNAKE_CASE       :      Tuple     						=							add_prefix_space
                   SCREAMING_SNAKE_CASE       :      Optional[int]     						=							True

              if state.get('trim_offsets'     ,				lowercase__			) != trim_offsets:
                   SCREAMING_SNAKE_CASE       :      Any     						=							trim_offsets
                   SCREAMING_SNAKE_CASE       :      List[str]     						=							True

              if changes_to_apply:
                   SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							getattr(lowercase__     ,				state.pop('type'			)			)
                   SCREAMING_SNAKE_CASE       :      Any     						=							component_class(**lowercase__			)
                   setattr(self.backend_tokenizer     ,				lowercase__     ,				lowercase__			)






    @property
    def    _UpperCamelCase     (							self			)				->			int:
         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    _UpperCamelCase     (							self     ,				lowercase__			)				->			str:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							AddedToken(lowercase__     ,				lstrip=lowercase__     ,				rstrip=lowercase__			) if isinstance(lowercase__     ,				lowercase__			) else value
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							value






    def    _UpperCamelCase     (							self     ,				*lowercase__     ,				**lowercase__			)				->			Union[str, Any]:
         SCREAMING_SNAKE_CASE       :      Tuple     						=							kwargs.get('is_split_into_words'     ,				lowercase__			)

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  'to use it with pretokenized inputs.'			)

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






    def    _UpperCamelCase     (							self     ,				*lowercase__     ,				**lowercase__			)				->			Optional[Any]:
         SCREAMING_SNAKE_CASE       :      Any     						=							kwargs.get('is_split_into_words'     ,				lowercase__			)

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  'to use it with pretokenized inputs.'			)

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






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__ = None			)				->			Dict:
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							self._tokenizer.model.save(lowercase__     ,				name=lowercase__			)
         return tuple(lowercase__			)






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__=None			)				->			Tuple:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							[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    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__ = None			)				->			Optional[Any]:
         SCREAMING_SNAKE_CASE       :      str     						=							[self.sep_token_id]
         SCREAMING_SNAKE_CASE       :      List[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]





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__magic_name__  							=	299_792_458

# Symbols
__magic_name__							,   __magic_name__							,   __magic_name__							,   __magic_name__  							=	symbols('''ct x y z''')





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return 1 / sqrt(1 - beta(__lowerCAmelCase				) ** 2				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return np.array(
        [
            [gamma(__lowerCAmelCase				), -gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), 0, 0],
            [-gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), gamma(__lowerCAmelCase				), 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase = None				):
    # Ensure event is not empty
    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(__lowerCAmelCase				) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __magic_name__  							=	transform(29_979_245)
     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
     __magic_name__  							=	{ct: c, x: 1, y: 1, z: 1}
     __magic_name__  							=	[four_vector[i].subs(sub_dict) for i in range(4)]

     print(F'''\n{numerical_vector}''')



