'''simple docstring'''


import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      OpenAIGPTTokenizer
							a_        =      OpenAIGPTTokenizerFast
							a_        =      True
							a_        =      False





							def       snake_case__						(      self				:							Dict	)							->	Any:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								__UpperCAmelCase							=			[
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''w</w>''',
								    '''r</w>''',
								    '''t</w>''',
								    '''lo''',
								    '''low''',
								    '''er</w>''',
								    '''low</w>''',
								    '''lowest</w>''',
								    '''newer</w>''',
								    '''wider</w>''',
								    '''<unk>''',
								]
								__UpperCAmelCase							=			dict(zip(__a     ,		range(len(__a	)	)	)	)
								__UpperCAmelCase							=			['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

								__UpperCAmelCase							=			os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES['''vocab_file''']	)
								__UpperCAmelCase							=			os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES['''merges_file''']	)
								with open(self.vocab_file     ,		'''w'''	) as fp:
									fp.write(json.dumps(__a	)	)
								with open(self.merges_file     ,		'''w'''	) as fp:
									fp.write('''\n'''.join(__a	)	)





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Optional[Any]	)							->	List[Any]:
								return "lower newer", "lower newer"





							def       snake_case__						(      self				:							Any	)							->	Optional[int]:
								__UpperCAmelCase							=			OpenAIGPTTokenizer(self.vocab_file     ,		self.merges_file	)

								__UpperCAmelCase							=			'''lower'''
								__UpperCAmelCase							=			['''low''', '''er</w>''']
								__UpperCAmelCase							=			tokenizer.tokenize(__a	)
								self.assertListEqual(__a     ,		__a	)

								__UpperCAmelCase							=			tokens + ['''<unk>''']
								__UpperCAmelCase							=			[1_4, 1_5, 2_0]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__a	)     ,		__a	)





							def       snake_case__						(      self				:							Tuple     ,		__a				:							int=1_5	)							->	str:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
										__UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(__a     ,		**__a	)

										# Simple input
										__UpperCAmelCase							=			'''This is a simple input'''
										__UpperCAmelCase							=			['''This is a simple input 1''', '''This is a simple input 2''']
										__UpperCAmelCase							=			('''This is a simple input''', '''This is a pair''')
										__UpperCAmelCase							=			[
										    ('''This is a simple input 1''', '''This is a simple input 2'''),
										    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
										]

										# Simple input tests
										self.assertRaises(__a     ,		tokenizer_r.encode     ,		__a     ,		max_length=__a     ,		padding='''max_length'''	)

										# Simple input
										self.assertRaises(__a     ,		tokenizer_r.encode_plus     ,		__a     ,		max_length=__a     ,		padding='''max_length'''	)

										# Simple input
										self.assertRaises(
										    __a     ,		tokenizer_r.batch_encode_plus     ,		__a     ,		max_length=__a     ,		padding='''max_length'''     ,		)

										# Pair input
										self.assertRaises(__a     ,		tokenizer_r.encode     ,		__a     ,		max_length=__a     ,		padding='''max_length'''	)

										# Pair input
										self.assertRaises(__a     ,		tokenizer_r.encode_plus     ,		__a     ,		max_length=__a     ,		padding='''max_length'''	)

										# Pair input
										self.assertRaises(
										    __a     ,		tokenizer_r.batch_encode_plus     ,		__a     ,		max_length=__a     ,		padding='''max_length'''     ,		)






							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[int]:
								pass






@require_ftfy
@require_spacy
@require_tokenizers
class 						A   (   UpperCAmelCase   ):
							pass


'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__lowerCAmelCase	:       str						       =	299_792_458

# Symbols
__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase	:       Any						       =	symbols("ct x y z")





def       lowerCAmelCase					(		UpperCamelCase__				:   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       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return 1 / sqrt(1 - beta(UpperCamelCase__       ) ** 2       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return np.array(
	    [
	        [gamma(UpperCamelCase__       ), -gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), 0, 0],
	        [-gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), gamma(UpperCamelCase__       ), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   np.ndarray | None = None       ):


	"""simple docstring"""
	# Ensure event is not empty
	if event is None:
		__UpperCAmelCase							=			np.array([ct, x, y, z]       )  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(UpperCamelCase__       ) @ event


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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

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