

"""simple docstring"""






import math as m
def 		A_	(   __UpperCamelCase     :				int       ,	__UpperCamelCase     :				List[str]       ,	__UpperCamelCase     :				str=0.0_01				):
		return (f(a + h				) - f(a - h				)) / (2 * h)
def 		A_	(   __UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				int=0       ,	__UpperCamelCase     :				Optional[int]=1_00       ,	__UpperCamelCase     :				Any=0.00_01       ,	__UpperCamelCase     :				Optional[int]=1e-6       ,	__UpperCamelCase     :				int=False				):
		lowercase										=							xa  # set the initial guess
		lowercase										=							[a]
		lowercase										=							abs(f(UpperCamelCase_				)				)
		lowercase										=							lambda __UpperCamelCase				: calc_derivative(UpperCamelCase_       ,	UpperCamelCase_       ,	h=UpperCamelCase_				)  # noqa: E731  Derivative of f(x)
		for _ in range(UpperCamelCase_				):
				if fa(UpperCamelCase_				) == 0:
						raise ValueError('''No converging solution found'''				)
				lowercase										=							a - f(UpperCamelCase_				) / fa(UpperCamelCase_				)  # Calculate the next estimate
				if logsteps:
						steps.append(UpperCamelCase_				)
				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				      =   newton_raphson(
							    f, xa=10, maxiter=1_000, 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}}}''')







from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Tuple				=       DPRContextEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRContextEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							str				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       DPRQuestionEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       DPRQuestionEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRReaderTokenizer
						_SCREAMING_SNAKE_CASE						:							int				=       DPRReaderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							List[str]				=       True






						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence ' * 4  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
												lowerCAmelCase__         =    BatchEncoding({'input_ids': input_ids} )

												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [0]
												lowerCAmelCase__         =    DPRReaderOutput(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )

												lowerCAmelCase__    ,							lowerCAmelCase__         =    8, 9
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    tokenizer.decode_best_spans(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].start_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].end_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].doc_id  ,    0 )







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
												lowerCAmelCase__         =    tokenizer(questions=['question sequence']  ,    titles=['title sequence']  ,    texts=['text sequence'] )
												self.assertIn('input_ids'  ,    _UpperCamelCase )
												self.assertIn('attention_mask'  ,    _UpperCamelCase )
												self.assertListEqual(encoded_input['input_ids'][0]  ,    _UpperCamelCase )





