


"""simple docstring"""







import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  UpperCamelCase_	( UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			FunnelTokenizer
	snake_case__				:  List[str]    					=			FunnelTokenizerFast
	snake_case__				:  str    					=			True
	snake_case__				:  Optional[Any]    					=			True




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	str:
						super().setUp()

						__SCREAMING_SNAKE_CASE						   =				[
						    "<unk>",
						    "<cls>",
						    "<sep>",
						    "want",
						    "##want",
						    "##ed",
						    "wa",
						    "un",
						    "runn",
						    "##ing",
						    ",",
						    "low",
						    "lowest",
						]
						__SCREAMING_SNAKE_CASE						   =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["vocab_file"]   )
						with open(self.vocab_file			,			"w"			,			encoding="utf-8"   ) as vocab_writer:
											vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )




	def   UpperCAmelCase_      ( self			:				List[str]			,			**UpperCAmelCase__			:				str   )       ->	Any:
						return FunnelTokenizer.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Dict			,			**UpperCAmelCase__			:				int   )       ->	List[str]:
						return FunnelTokenizerFast.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				"UNwant\u00E9d,running"
						__SCREAMING_SNAKE_CASE						   =				"unwanted, running"
						return input_text, output_text




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				self.tokenizer_class(self.vocab_file   )

						__SCREAMING_SNAKE_CASE						   =				tokenizer.tokenize("UNwant\u00E9d,running"   )
						self.assertListEqual(UpperCAmelCase__			,			["un", "##want", "##ed", ",", "runn", "##ing"]   )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )			,			[7, 4, 5, 1_0, 8, 9]   )




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				self.get_tokenizers(do_lower_case=UpperCAmelCase__   )
						for tokenizer in tokenizers:
											__SCREAMING_SNAKE_CASE						   =				tokenizer("UNwant\u00E9d,running"   )
											__SCREAMING_SNAKE_CASE						   =				len(inputs["input_ids"]   ) - 1
											self.assertListEqual(inputs["token_type_ids"]			,			[2] + [0] * sentence_len   )

											__SCREAMING_SNAKE_CASE						   =				tokenizer("UNwant\u00E9d,running"			,			"UNwant\u00E9d,running"   )
											self.assertListEqual(inputs["token_type_ids"]			,			[2] + [0] * sentence_len + [1] * sentence_len   )




"""simple docstring"""







import math
import random




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value   ))


# Initial Value
a__				:							Tuple						=     0.02




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(2 * (random.randint(1  ,						100   )) - 1   )

					for _ in range(lowerCAmelCase_   ):
										# Forward propagation
										__SCREAMING_SNAKE_CASE						   =				sigmoid_function(INITIAL_VALUE * weight   )
										# How much did we miss?
										__SCREAMING_SNAKE_CASE						   =				(expected / 100) - layer_a
										# Error delta
										__SCREAMING_SNAKE_CASE						   =				layer_1_error * sigmoid_function(lowerCAmelCase_  ,						lowerCAmelCase_   )
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 100


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__				:							List[str]						=     int(input('''Expected value: '''))
		a__				:							str						=     int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
