import numpy

# List of input, output pairs
lowercase_     		= (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase_     		= (((515, 22, 13), 555), ((61, 35, 49), 150))
lowercase_     		= [2, 4, 1, 5]
lowercase_     		= len(train_data)
lowercase_     		= 0.009



def 							UpperCamelCase__			(			a__			,      a__="train"      ):


						'''simple docstring'''






						return calculate_hypothesis_value(a__			,      a__      ) - output(
						    a__			,      a__      )



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=0
						for i in range(len(a__      ) - 1      ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]      )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]      )
						return None



def 							UpperCamelCase__			(			a__			,      a__=m      ):


						'''simple docstring'''






						_lowerCAmelCase										=0
						for i in range(a__      ):
												if index == -1:
																		summation_value += _error(a__      )
												else:
																		summation_value += _error(a__      ) * train_data[i][0][index]
						return summation_value



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=summation_of_cost_derivative(a__			,      a__      ) / m
						return cost_derivative_value



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_lowerCAmelCase										=0.000_002
						_lowerCAmelCase										=0
						_lowerCAmelCase										=0
						while True:
												j += 1
												_lowerCAmelCase										=[0, 0, 0, 0]
												for i in range(0			,      len(a__      )      ):
																		_lowerCAmelCase										=get_cost_derivative(i - 1      )
																		_lowerCAmelCase										=(
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    a__			,      a__			,      atol=a__			,      rtol=a__			,      ):
																		break
												_lowerCAmelCase										=temp_parameter_vector
						print(('Number of iterations:', j)      )



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						for i in range(len(a__      )      ):
												print(('Actual output value:', output(a__			,      'test'      ))      )
												print(('Hypothesis output:', calculate_hypothesis_value(a__			,      'test'      ))      )


if __name__ == "__main__":
							run_gradient_descent()
							print('''\nTesting gradient descent for a linear hypothesis function.\n''')
							test_gradient_descent()






'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {'''vocab_file''': '''vocab.txt'''}

lowercase_     		= {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

lowercase_     		= {
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


lowercase_     		= {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							VOCAB_FILES_NAMES
		lowercase   :      Tuple       =							PRETRAINED_VOCAB_FILES_MAP
		lowercase   :      Optional[int]       =							PRETRAINED_INIT_CONFIGURATION
		lowercase   :      int       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase   :      List[str]       =							ConvBertTokenizer






		def __init__(   self		,						__A=None		,						__A=None		,						__A=True		,						__A="[UNK]"		,						__A="[SEP]"		,						__A="[PAD]"		,						__A="[CLS]"		,						__A="[MASK]"		,						__A=True		,						__A=None		,						**__A		,						) ->     Union[str, Any]:
								super().__init__(
								    __A		,						tokenizer_file=__A		,						do_lower_case=__A		,						unk_token=__A		,						sep_token=__A		,						pad_token=__A		,						cls_token=__A		,						mask_token=__A		,						tokenize_chinese_chars=__A		,						strip_accents=__A		,						**__A		,						)

								_lowerCAmelCase										=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get('lowercase'		,						__A				) != do_lower_case
								    or normalizer_state.get('strip_accents'		,						__A				) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'		,						__A				) != tokenize_chinese_chars
								):
														_lowerCAmelCase										=getattr(__A		,						normalizer_state.pop('type'				)				)
														_lowerCAmelCase										=do_lower_case
														_lowerCAmelCase										=strip_accents
														_lowerCAmelCase										=tokenize_chinese_chars
														_lowerCAmelCase										=normalizer_class(**__A				)

								_lowerCAmelCase										=do_lower_case






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     int:
								_lowerCAmelCase										=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

								return output






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     List[int]:
								_lowerCAmelCase										=[self.sep_token_id]
								_lowerCAmelCase										=[self.cls_token_id]
								if token_ids_a is None:
														return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     Tuple[str]:
								_lowerCAmelCase										=self._tokenizer.model.save(__A		,						name=__A				)
								return tuple(__A				)





