



'''simple docstring'''






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	_lowerCAmelCase     (    __A     ,		unittest.TestCase       ):






		'''simple docstring'''
		snake_case_							= GPTNeoXJapaneseTokenizer
		snake_case_							= False
		snake_case_							= {'do_clean_text': False, 'add_prefix_space': False}
		def 						__lowercase      (						self							:       Union[str, Any]		)      ->			Union[str, Any]:







									'''simple docstring'''





									super().setUp()

									_lowercase							:		Optional[Any]				=   [
									    '''こん''',
									    '''こんに''',
									    '''にちは''',
									    '''ばんは''',
									    '''世界,㔺界''',
									    '''、''',
									    '''。''',
									    '''<BR>''',
									    '''<SP>''',
									    '''<TAB>''',
									    '''<URL>''',
									    '''<EMAIL>''',
									    '''<TEL>''',
									    '''<DATE>''',
									    '''<PRICE>''',
									    '''<BLOCK>''',
									    '''<KIGOU>''',
									    '''<U2000U2BFF>''',
									    '''<|emoji1|>''',
									    '''<unk>''',
									    '''<|startoftext|>''',
									    '''<|endoftext|>''',
									]
									_lowercase							:		List[str]				=   {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
									_lowercase							:		Dict				=   {'''unk_token''': '''<unk>'''}

									_lowercase							:		Union[str, Any]				=   os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES['''vocab_file''']		)
									_lowercase							:		Dict				=   os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES['''emoji_file''']		)
									with open(self.vocab_file      ,		'''w'''      ,		encoding='''utf-8'''		) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)
									with open(self.emoji_file      ,		'''w'''		) as emoji_writer:
																emoji_writer.write(json.dumps(UpperCamelCase_		)		)
		def 						__lowercase      (						self							:       int      ,		**UpperCamelCase_							:       Optional[int]		)      ->			str:







									'''simple docstring'''





									kwargs.update(self.special_tokens_map		)
									return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,		**UpperCamelCase_		)
		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       str		)      ->			Optional[Any]:







									'''simple docstring'''





									_lowercase							:		Union[str, Any]				=   '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
									_lowercase							:		Optional[Any]				=   '''こんにちは、世界。 \nこんばんは、世界。😀'''
									return input_text, output_text
		def 						__lowercase      (						self							:       List[str]      ,		UpperCamelCase_							:       str		)      ->			Union[str, Any]:







									'''simple docstring'''





									_lowercase			, _lowercase							:		Tuple				=   self.get_input_output_texts(UpperCamelCase_		)
									_lowercase							:		Dict				=   tokenizer.encode(UpperCamelCase_      ,		add_special_tokens=UpperCamelCase_		)
									_lowercase							:		List[str]				=   tokenizer.decode(UpperCamelCase_      ,		clean_up_tokenization_spaces=UpperCamelCase_		)
									return text, ids
		def 						__lowercase      (						self							:       List[Any]		)      ->			List[Any]:







									'''simple docstring'''





									pass  # TODO add if relevant
		def 						__lowercase      (						self							:       Dict		)      ->			Tuple:







									'''simple docstring'''





									pass  # TODO add if relevant
		def 						__lowercase      (						self							:       Dict		)      ->			Optional[int]:







									'''simple docstring'''





									pass  # TODO add if relevant
		def 						__lowercase      (						self							:       int		)      ->			int:







									'''simple docstring'''





									_lowercase							:		Union[str, Any]				=   self.get_tokenizer()

									# Testing tokenization
									_lowercase							:		List[str]				=   '''こんにちは、世界。　こんばんは、㔺界。'''
									_lowercase							:		Union[str, Any]				=   ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
									_lowercase							:		Optional[Any]				=   tokenizer.tokenize(UpperCamelCase_		)
									self.assertListEqual(UpperCamelCase_      ,		UpperCamelCase_		)

									# Testing conversion to ids without special tokens
									_lowercase							:		Optional[Any]				=   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
									_lowercase							:		Optional[int]				=   tokenizer.convert_tokens_to_ids(UpperCamelCase_		)
									self.assertListEqual(UpperCamelCase_      ,		UpperCamelCase_		)

									# Testing conversion to ids with special tokens
									_lowercase							:		Optional[Any]				=   tokens + [tokenizer.unk_token]
									_lowercase							:		List[str]				=   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
									_lowercase							:		Tuple				=   tokenizer.convert_tokens_to_ids(UpperCamelCase_		)
									self.assertListEqual(UpperCamelCase_      ,		UpperCamelCase_		)
		@slow
		def 						__lowercase      (						self							:       List[str]		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b'''		)

									_lowercase							:		str				=   tokenizer.encode('''ありがとう。'''      ,		add_special_tokens=UpperCamelCase_		)
									_lowercase							:		Tuple				=   tokenizer.encode('''どういたしまして。'''      ,		add_special_tokens=UpperCamelCase_		)

									_lowercase							:		Optional[int]				=   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_		)
									_lowercase							:		int				=   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_      ,		UpperCamelCase_		)

									assert encoded_sentence == ids_a
									assert encoded_pair == ids_a + ids_a
		def 						__lowercase      (						self							:       Optional[int]		)      ->			Optional[int]:







									'''simple docstring'''





									pass





		def 						__lowercase      (						self							:       str		)      ->			Any:







									'''simple docstring'''





									pass








'''simple docstring'''






import math as m





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any] ,       snake_case_    :     int ,       snake_case_    :     Optional[Any]=0.0_0_1  )		->Tuple:






							'''simple docstring'''
							return (f(a + h  ) - f(a - h  )) / (2 * h)





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict ,       snake_case_    :     List[Any]=0 ,       snake_case_    :     Dict=1_00 ,       snake_case_    :     List[str]=0.0_0_0_1 ,       snake_case_    :     str=1E-6 ,       snake_case_    :     Tuple=False  )		->List[Any]:






							'''simple docstring'''
							_lowercase							:		Tuple				=   xa  # set the initial guess
							_lowercase							:		List[str]				=   [a]
							_lowercase							:		str				=   abs(f(snake_case_  )  )
							_lowercase							:		Dict				=   lambda snake_case_  : calc_derivative(snake_case_ ,       snake_case_ ,       h=snake_case_  )  # noqa: E731  Derivative of f(x)
							for _ in range(snake_case_  ):
														if fa(snake_case_  ) == 0:
																					raise ValueError('''No converging solution found'''  )
														_lowercase							:		Tuple				=   a - f(snake_case_  ) / fa(snake_case_  )  # Calculate the next estimate
														if logsteps:
																					steps.append(snake_case_  )
														if error < maxerror:
																					break
							else:
														raise ValueError('''Iteration limit reached, no converging solution found'''  )
							if logsteps:
														# If logstep is true, then log intermediate steps
														return a, error, steps
							return a, error


if __name__ == "__main__":
						from matplotlib import pyplot as plt

						lowerCamelCase__	  =		lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
						lowerCamelCase__  ,      lowerCamelCase__  ,      lowerCamelCase__	  =		newton_raphson(
						    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
						)
						plt.plot([abs(f(x)) for x in steps])
						plt.xlabel('step')
						plt.ylabel('error')
						plt.show()
						print(f'''solution = {{{solution:f}}}, error = {{{error:f}}}''')



