






"""simple docstring"""






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE						:						Any					   =       299_792_458

# Symbols
SCREAMING_SNAKE_CASE						:						Any					   =       symbols('''ct x y z''')



def  __lowerCamelCase					(					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  __lowerCamelCase					(					lowerCAmelCase__				):
    return 1 / sqrt(1 - beta(lowerCAmelCase__				) ** 2				)



def  __lowerCamelCase					(					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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = None				):
    # Ensure event is not empty
    if event is None:
        A__							 =       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:
     SCREAMING_SNAKE_CASE						:						Any					   =       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
     SCREAMING_SNAKE_CASE						:						str					   =       {ct: c, x: 1, y: 1, z: 1}
     SCREAMING_SNAKE_CASE						:						Optional[int]					   =       [four_vector[i].subs(sub_dict) for i in range(4)]

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










"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


	def __init__(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a=False  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(
					    __a  ,					__a  ,					tokenizer_file=__a  ,					unk_token=__a  ,					bos_token=__a  ,					eos_token=__a  ,					add_prefix_space=__a  ,					**__a  ,					)

					A__							 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'  ,					__a						) != add_prefix_space:
									A__							 =       getattr(__a  ,					pre_tok_state.pop('type'						)						)
									A__							 =       add_prefix_space
									A__							 =       pre_tok_class(**__a						)

					A__							 =       add_prefix_space


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__a  ,					add_special_tokens=__a						) + [self.eos_token_id]						)

					if len(__a						) > self.model_max_length:
									A__							 =       input_ids[-self.model_max_length :]
					return input_ids


