




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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	List[Any]:
		if isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							np.full((len(lowerCAmelCase ), sequence_length, 2)    ,						lowerCAmelCase )
		else:
				SCREAMING_SNAKE_CASE_					      =							np.full((len(lowerCAmelCase ), sequence_length)    ,						lowerCAmelCase )

		for i, tensor in enumerate(lowerCAmelCase ):
				if padding_side == "right":
						if isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
								SCREAMING_SNAKE_CASE_					      =							tensor[:sequence_length]
						else:
								SCREAMING_SNAKE_CASE_					      =							tensor[:sequence_length]
				else:
						if isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
								SCREAMING_SNAKE_CASE_					      =							tensor[:sequence_length]
						else:
								SCREAMING_SNAKE_CASE_					      =							tensor[:sequence_length]

		return out_tensor.tolist()


def 					_a							(					lowerCAmelCase )->	List[Any]:
		SCREAMING_SNAKE_CASE_					      =							ord(lowerCAmelCase )
		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
		SCREAMING_SNAKE_CASE_					      =							unicodedata.category(lowerCAmelCase )
		if cat.startswith('P' ):
				return True
		return False




@dataclass
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =True
						lowerCAmelCase__			      =None
						lowerCAmelCase__			      =None
						lowerCAmelCase__			      =-1_00
						lowerCAmelCase__			      ="pt"



						def 	__a					(			self	:   Dict					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								import torch

								SCREAMING_SNAKE_CASE_					      =							'label' if 'label' in features[0].keys() else 'labels'
								SCREAMING_SNAKE_CASE_					      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer.pad(
								    snake_case__					,							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

								SCREAMING_SNAKE_CASE_					      =							torch.tensor(batch['entity_ids']  ).shape[1]
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer.padding_side
								if padding_side == "right":
										SCREAMING_SNAKE_CASE_					      =							[
										    list(snake_case__  ) + [self.label_pad_token_id] * (sequence_length - len(snake_case__  )) for label in labels
										]
								else:
										SCREAMING_SNAKE_CASE_					      =							[
										    [self.label_pad_token_id] * (sequence_length - len(snake_case__  )) + list(snake_case__  ) for label in labels
										]

								SCREAMING_SNAKE_CASE_					      =							[feature['ner_tags'] for feature in features]
								SCREAMING_SNAKE_CASE_					      =							padding_tensor(snake_case__					,							-1					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[feature['original_entity_spans'] for feature in features]
								SCREAMING_SNAKE_CASE_					      =							padding_tensor(snake_case__					,							(-1, -1)					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							{k: torch.tensor(snake_case__					,							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}""")