

"""simple docstring"""




import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 if isinstance(__UpperCamelCase				,     __UpperCamelCase   ):
  UpperCAmelCase__       :	List[str]        =       np.full((len(__UpperCamelCase   ), sequence_length, 2)				,     __UpperCamelCase   )
 else:
  UpperCAmelCase__       :	Any        =       np.full((len(__UpperCamelCase   ), sequence_length)				,     __UpperCamelCase   )

 for i, tensor in enumerate(__UpperCamelCase   ):
  if padding_side == "right":
   if isinstance(__UpperCamelCase				,     __UpperCamelCase   ):
    UpperCAmelCase__       :	str        =       tensor[:sequence_length]
   else:
    UpperCAmelCase__       :	str        =       tensor[:sequence_length]
  else:
   if isinstance(__UpperCamelCase				,     __UpperCamelCase   ):
    UpperCAmelCase__       :	Optional[Any]        =       tensor[:sequence_length]
   else:
    UpperCAmelCase__       :	Union[str, Any]        =       tensor[:sequence_length]

 return out_tensor.tolist()
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	str        =       ord(__UpperCamelCase   )
 if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
  return True
 UpperCAmelCase__       :	Optional[Any]        =       unicodedata.category(__UpperCamelCase   )
 if cat.startswith("""P"""   ):
  return True
 return False







@dataclass
class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							42
  snake_case_												=							True
  snake_case_												=							None
  snake_case_												=							None
  snake_case_												=							-1_0_0
  snake_case_												=							"pt"

  def  __lowercase							(	self		:      Dict       ,A		:      List[str]     ):






   '''simple docstring'''
   import torch

   UpperCAmelCase__       :	Dict        =       """label""" if """label""" in features[0].keys() else """labels"""
   UpperCAmelCase__       :	Dict        =       [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   UpperCAmelCase__       :	Any        =       self.tokenizer.pad(
       A       ,padding=self.padding       ,max_length=self.max_length       ,pad_to_multiple_of=self.pad_to_multiple_of       ,return_tensors="""pt""" if labels is None else None       ,)

   if labels is None:
    return batch

   UpperCAmelCase__       :	Union[str, Any]        =       torch.tensor(batch["""entity_ids"""]     ).shape[1]
   UpperCAmelCase__       :	Union[str, Any]        =       self.tokenizer.padding_side
   if padding_side == "right":
    UpperCAmelCase__       :	Optional[Any]        =       [
        list(A     ) + [self.label_pad_token_id] * (sequence_length - len(A     )) for label in labels
    ]
   else:
    UpperCAmelCase__       :	Tuple        =       [
        [self.label_pad_token_id] * (sequence_length - len(A     )) + list(A     ) for label in labels
    ]

   UpperCAmelCase__       :	str        =       [feature["""ner_tags"""] for feature in features]
   UpperCAmelCase__       :	Any        =       padding_tensor(A       ,-1       ,A       ,A     )
   UpperCAmelCase__       :	List[str]        =       [feature["""original_entity_spans"""] for feature in features]
   UpperCAmelCase__       :	Optional[int]        =       padding_tensor(A       ,(-1, -1)       ,A       ,A     )
   UpperCAmelCase__       :	Optional[int]        =       {k: torch.tensor(A       ,dtype=torch.intaa     ) for k, v in batch.items()}

   return batch











from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

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


def 					_a							(					lowerCAmelCase )->	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 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_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:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						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:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

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