

import numpy

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





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







							'''simple docstring'''






							return calculate_hypothesis_value(A__       ,							A__				) - output(
							    A__       ,							A__				)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__=m				):







							'''simple docstring'''






							snake_case_          =   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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   summation_of_cost_derivative(A__       ,							A__				) / m
							return cost_derivative_value





def  __lowerCamelCase     (			):







							'''simple docstring'''






							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							snake_case_          =   0.00_00_02
							snake_case_          =   0
							snake_case_          =   0
							while True:
														j += 1
														snake_case_          =   [0, 0, 0, 0]
														for i in range(0       ,							len(A__				)				):
																					snake_case_          =   get_cost_derivative(i - 1				)
																					snake_case_          =   (
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    A__       ,							A__       ,							atol=A__       ,							rtol=A__       ,							):
																					break
														snake_case_          =   temp_parameter_vector
							print(('Number of iterations:', j)				)





def  __lowerCamelCase     (			):







							'''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()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase		:       str						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       int						    =	{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			int	      =      '''canine'''




				def __init__( self				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1_6384				,       snake_case=16				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=0xE000				,       snake_case=0xE001				,       snake_case=4				,       snake_case=4				,       snake_case=8				,       snake_case=1_6384				,       snake_case=128				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       bos_token_id=snake_case				,       eos_token_id=snake_case				,       **snake_case	)

											snake_case_          =   max_position_embeddings
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_act
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   initializer_range
											snake_case_          =   type_vocab_size
											snake_case_          =   layer_norm_eps

											# Character config:
											snake_case_          =   downsampling_rate
											snake_case_          =   upsampling_kernel_size
											snake_case_          =   num_hash_functions
											snake_case_          =   num_hash_buckets
											snake_case_          =   local_transformer_stride




